Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex structural designs, leading to problems such as high memory usage, long inference time, and extensive consumption of computing resources. These limitations pose certain challenges for the practical application of HGNNs, especially for resource-constrained devices. To mitigate this issue, we propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN), which incorporates the brain-inspired and energy-saving properties of Spiking Neural Networks (SNNs) into heterogeneous graph learning to reduce the computing cost without compromising the performance. Specifically, SpikingHAN aggregates metapath-based neighbor information using a single-layer graph convolution with shared parameters. It then employs a semantic-level attention mechanism to capture the importance of different meta-paths and performs semantic aggregation. Finally, it encodes the heterogeneous information into a spike sequence through SNNs, simulating bioinformatic processing to derive a binarized 1-bit representation of the heterogeneous graph. Comprehensive experimental results from three real-world heterogeneous graph datasets show that SpikingHAN delivers competitive node classification performance. It achieves this with fewer parameters, quicker inference, reduced memory usage, and lower energy consumption.

Abstract Although the code completion model based on the Transformer architecture has achieved remarkable results, the computational complexity of its multi-head self-attention grows quadratically with the increase of sequence length, resulting in low efficiency. To this end, we propose a code completion model based on grouped attention, referred to as GACC. This method groups the attention heads of a representation in the query, key, or value so that the attention heads in the same group share the single attention head of other representations, thereby reducing the number of parameters and computational complexity. We conducted experiments on public datasets of Python and JavaScript, and the experimental results show that compared to models based on multi-head self-attention, GACC effectively reduces the time taken for the model to suggest the next code token while achieving comparable performance on Top-k and MRR metrics.

With the development of service-oriented computing, software developers increasingly rely on diverse Web application programming interfaces (APIs, also known as Web services) from unmanned Web API markets. This trend aims to expedite the development of feature-rich Mashup applications while simultaneously reducing time and costs. However, the growing abundance of Web APIs presents a challenge in service discovery. Consequently, Web API recommendation is proposed as a vital strategy for facilitating service discovery. Nonetheless, existing approaches to Web API recommendation suffer from limitations in effectively extracting rich semantics from description documents and service networks, leading to suboptimal recommendation performance. To address this issue, this article proposes trustworthy contrastive learning (TCL) for Web API recommendation via exploring textual and structural semantics, named TCL. TCL takes the trustworthiness of both the textual and structural representations into account to differentiate the loss of contrastive learning so that both textual and structural representation learning can be mutually improved. Empirical evaluations conducted on a real-world dataset crawled from ProgrammableWeb.com demonstrate the effectiveness of the proposed approach, showcasing its superiority over baseline methods.

As edge devices grow smarter and application scenarios become more diverse, users' demands for lower latency and higher efficiency in data storage and processing have risen sharply. Individual edge devices and nodes are no longer sufficient to meet these expanding storage requirements. Consequently, developing efficient, low-latency, and cost-effective solutions for collaborative storage across edge devices and nodes has become a critical challenge. In this paper, we present a framework for the transaction and pricing of storage resources in an edge computing environment involving multiple edge service providers, to address trust and incentive issues in storage resource collaboration. Firstly, we propose a secure and decentralized storage resource trading mechanism by leveraging blockchain technology and smart contracts. We introduce Proof of Transaction Expectation (PoTE), an efficient, reliable, and lightweight consensus mechanism, to ensure transaction transparency, openness, and non-repudiation. Secondly, we introduce a game theory-based storage resource pricing model, where a leader interacts with multiple followers to optimize profits while maintaining service quality. To address dynamic pricing and storage resource allocation problems under incomplete information, we propose the Stackelberg Game Approach based on Multi-Agent Reinforcement Learning (SGA-MARL), which formulates the optimal pricing and trading share decisions in the two-stage Stackelberg game as a stochastic Markov Decision Process (MDP). Simulations and prototype testing validate the effectiveness of the proposed system, with results showing that the PoTE consensus achieves up to 40% higher throughput than Proof-of-Work while reducing latency by over 50% compared to PBFT, and the SGA-MARL algorithm improves leader profit by approximately 30% and resource satisfaction rates by over 80% compared to baseline methods like MA-PPO and DQN.

With the rapid advancement of service computing technologies, the proliferation of Web APIs on the Internet has increased exponentially. However, selecting the most suitable APIs for Mashup creation from this extensive pool presents a significant challenge for users. Numerous Web API recommendation methods have been developed to address this issue, aiming to simplify the complex selection process. Despite these advancements, there has been limited research on the recommendation of complementary functions. In response, we propose CoWAR, a comprehensive framework for recommending complementary Web APIs tailored to Mashup creation, based on the Web APIs previously selected by users. Specifically, we introduce a data labeling algorithm that generates a labeled dataset using Mashup-API interactions derived from historical Mashups and Web APIs. Furthermore, we utilize the Sentence BERT model to generate representation vectors of Web APIs from their functional descriptions. Subsequently, SANFM (Self Attentional Neural Factorization Machines) model is employed to train the complementary Web API recommendation model on the labeled dataset, utilizing the Web APIs' representation vectors. An attention mechanism is integrated into CoWAR to identify varying complementary weights between the selected Web APIs and candidate Web APIs, thereby enhancing recommendation performance. To the best of our knowledge, this is the first work to address the complementary function recommendation problem using a learning-based approach. Experimental validation on a real-world dataset demonstrates the effectiveness of the proposed framework, showing that the learning model outperforms both traditional machine learning-based models and several deep learning-based models.

In China, the number of riders in the on-demand delivery industry has surpassed ten million. Ensuring that these riders earn a decent income can enhance their financial security, reduce poverty, and promote social equity and stability. Due to ease of use, lower-cost maintenance and environmental friendliness, electric bicycles (e-bikes) are the primary mode of transportation for delivery riders. However, these riders frequently encounter depleted batteries due to limited capacity and prolonged charging times, necessitating inconvenient swaps or recharges during deliveries. To address this issue, we propose the e-bike Battery Swap-as-a-Service (eBaaS), an innovative battery-swapping system that leverages an intelligent AIoT network for seamless battery swapping at distributed locations across urban areas. eBaaS integrates edge-cloud collaboration, battery resource allocation, battery anomaly detection, and battery range prediction to minimize downtime and reduce unnecessary mileage. While eBaaS's potential benefits are evident, there has been a lack of robust methods to quantify its impact. Thus, we further developed the eBaaS Impact Evaluation Method (EIEM), the first comprehensive model to address this gap. EIEM analyzes data from approximately 260,000 delivery riders and 5 million riding trajectories. Findings indicate that eBaaS reduces average invalid mileage by 6 km and increases the order volume by an average of over 20% daily per e-bike rider. Meanwhile, the annual electricity savings result in a reduction of 2.74 million kilograms of carbon emissions for 260,000 riders. The eBaaS system is therefore significantly beneficial for environmental conservation and sustainable urban development.

ABSTRACT Edge‐cloud computing offers an efficient method to flexibly allocate various computing resources for periodic workflow applications commonly employed in industrial production, commercial operations, and scientific research. Rationalized allocation of computational resources for scheduling in the edge‐cloud environment is the key to reducing energy consumption of the periodic workflows scheduling process. To this end, this paper proposes an optimization method for dynamic energy‐efficient scheduling of periodic workflows based on the collaboration of edge‐cloud computational resources while satisfying the constraints of workflow deadlines. In our method, periodic workflow scheduling is defined to be performed on a three‐tier integrated scheduling architecture of user terminals, edge computing platform, and cloud computing platform. Task groups are generated based on workflow critical paths, and appropriate edge‐cloud computing resources are selected for workflow tasks using corresponding scheduling policies at each scheduling stage. It reduces energy consumption during task scheduling while satisfying workflow deadline constraints. Comparative experiments in a simulated edge‐cloud environment show that our method reduces energy consumption of the scheduling process by 19.38%, 22.7%, and 37.34% compared to GA, PSO, and cloud computing, respectively. That is, the method effectively reduces the scheduling energy consumption during periodic workflow processing and significantly improves computational resource utilization.

ABSTRACT To address the issues of low sampling quality and efficiency in processing large‐scale event logs in existing business process event log sampling methods, a new method, named VariantRank, is proposed, which is based on the importance of trace variants. First, the importance of each trace variant is calculated based on the activity importance and the importance of directly‐follow relationships within the trace variants. Then, the trace variants are ranked according to their importance. Finally, based on the given sampling rate and the ranking of trace variants, the final sampling is performed to obtain the sample event logs. The effectiveness of the proposed sampling method is evaluated in terms of both sampling quality and sampling efficiency across 8 public event log datasets. The experimental analysis shows that, compared with the state‐of‐the‐art sampling methods, VariantRank improves the sampling efficiency while ensuring the sampling quality.

ABSTRACT Software development plays a crucial role in the modern mobile application domain, reflecting its significance through widespread application. With the continuous evolution and vast number of Android APIs, developers need to invest considerable effort in learning how to use various suitable APIs for their projects. Unfortunately, most current recommendation methods, when representing programs as source code sequences, abstract syntax trees, or API call paths, often focus only on contextual relationships while ignoring valuable information in API dependency relationships. Moreover, existing sequence models (such as RNN, LSTM) often fail to make correct predictions for low‐frequency API methods with high‐frequency suffixes, as these models tend to capture the most common API sequence patterns, causing these relatively low‐frequency but potentially more applicable APIs to be overlooked. To address this issue, we propose an API dependency path‐based Android API recommendation method, DPAPIRec. This approach combines program analysis with deep learning, which first extracts API methods and their data flow and control flow dependency relationships from a large number of Android APPs through program analysis techniques and then obtains a comprehensive API dependency paths repository. Finally, a deep learning method is applied to learn and represent these dependency relationships to improve API recommendation accuracy. Furthermore, to better extract dependency relationships, we employ an improved attention‐based LSTM model with a novel loss architecture, enhancing the global dependency relationships between APIs through a weighted mixed loss, thereby strengthening the weight of initial nodes and alleviating the problem of low‐frequency APIs with high‐frequency suffixes. Our experiments on the AndroZoo dataset demonstrate that DPAPIRec significantly outperforms baseline methods in Android API recommendation tasks, showing substantial improvements in both Accuracy and Mean Reciprocal Rank (MRR).

ABSTRACT With the constant dynamics of temporal dependence and spatial correlation, the interaction between them has become intricate. Existing work attempts to model precise temporal dependency and spatial correlation to make their interactions more accurate but ignores the importance of understanding how the two interact with each other. Thus, this article mines deeper into their interaction mechanism and proposes a new traffic prediction model called traffic flow prediction model based on spatial–temporal identity (TPST). It provides a new way named the spatial–temporal identity mechanism to model spatial–temporal interactions, which convert complex temporal dependence and spatial correlation into their identity information. Meanwhile, in order to improve spatial–temporal interaction resolution of the model, the method utilizes the down‐sampling cross‐convolution technique to contain more spatial–temporal history information and parses spatial–temporal interactions at different granularity. Experiments conducted with four real traffic flow datasets show that TPST consistently outperforms the other seven benchmark models, providing higher prediction accuracy with lower computational cost.

ABSTRACT Business process monitoring involves real‐time supervision of a series of activities carried out by an organization to achieve specific objectives. Process event monitoring points (PEMP) are used to pinpoint specific locations within a process model where expected events are anticipated to occur. However, in general business process modeling languages, such as business process model and notation (BPMN), there is a lack of explicit modeling for PEMPs. This article proposes a method for modeling pairwise event monitoring points in business process models to track the execution status of specific activities or process segments via BPMN extension. Specifically, process monitoring points are designed by expanding the modeling element of sequence flow. Moreover, the business process model is decomposed using the refined process structure tree (RPST) to verify the soundness of the designed pairwise monitoring points. The proposed method allows flexible monitoring at the process segment level. And the process monitoring data could be used for a clear understanding of business progress, especially useful in heterogeneous business process model to support decision‐making. Through case study from real‐world business processes, the effectiveness and usefulness of the proposed process monitoring modeling method is validated.

Business process modeling is widely used in modern organizations for business description. Business Process Modeling Notation (BPMN), as a de-facto modeling standard, represents business process models in graphical notations. Nevertheless, BPMN lacks intuitive modeling tasks for Industrial Internet application scenarios (e.g., IoT tasks and multi-instance tasks with constraints). Although there are some works on extending BPMN elements to improve the model representation, most of them stay in the conceptual model only without tool support, or they are confined to specific domains. In this paper, we extend both BPMN elements and attributes for application in the Industrial Internet context, and two modeling tools are implemented in a client version and Web version to support business process modeling via low-code, enabling the BPMN extension model from conceptual to executable level. Two real-world case studies in Industrial Internet are conducted to show the usefulness of process models with BPMN extension. Furthermore, a comprehensive user experiment is conducted to evaluate the extended process models and tools, and the experimental results show that process models with extension have better quality compared with traditional process models and provided tools are effective for business process modeling in Industrial Internet applicationsNote to Practitioners—This article was motivated by the problem of business process modeling for Industrial Internet. Practically, existing methods extend BPMN elements and add text annotations to enhance the representation of process models. Although these methods facilitate process participants to understand the process models in detail, most of them focus on conceptual modeling and increase the complexity of process models. Moreover, the process models and multi-instance business constraints of business processes involving IoT elements are difficult to be represented by using native BPMN. This article proposes a comprehensive business process modeling language, named BPMN$++$, which extend both new modeling elements for Industrial Internet business requirements and attributes for BPMN multi-instance tasks with the corresponding graphical notations. Then, BPMN$++$model could be executed on the process engine, enabling the model from conceptual to executable level. Finally, comprehensive experiments are conducted for BPMN$++$.

Learning resources in online learning systems typically adhere to uniform formats and settings, lacking flexibility and personalization to meet diverse learning needs and preferences. This inability to meet individualized learning needs and preferences has spurred research interest in personalized learning path recommendations. Many researchers have explored recommending learning path by leveraging user historical learning resource sequence to model personalized characteristics. However, these methods overlook the time information in the learning process and fail to interpret the dynamic shifts in learning preferences during recommendation. Therefore, we propose a method, termed TA-RL, for learning path recommendation, based on time-aware attention mechanism and reinforcement learning. First, we propose a novel time-aware attention mechanism to trace the evolving learning preferences of user, in which attention weights are computed using a context-aware time distance measure and the similarity between history learning resources. Then, we employ a Monte Carlo policy gradient reinforcement learning method to generate learning path recommendation based on learning preferences. We validate the effectiveness of our proposed method by comprehensive experiments on two real-world datasets.

The adoption of vehicular cloud computing for sequential recommendation offers flexible, reliable, and scalable computing resources in intelligent transportation systems. However, it also raises privacy concerns of drivers/passengers regarding the upload of sensitive data and models to vehicular cloud servers. To address this issue, we propose a novel privacy-preserving sequential recommendation method for intelligent transportation systems (named Grapeseed) based on split learning and variational autoencoder (VAE). Specifically, the vehicular client first inputs raw data into an encoder to produce latent variables locally and uploads these variables to the vehicular cloud server. Then, the vehicular cloud server generates and returns intermediate variables derived from these latent variables. Upon receiving these intermediate variables, the vehicular client calculates the final recommendation results. Extensive experiment results and analyses demonstrate that the proposed method improves both performance and communication efficiency between vehicular cloud servers and clients while preserving privacy.

With the advancement of service computing technology, software developers tend to consume a variety of Web APIs (Application Programming Interfaces, also named Web services) from Web API markets to create feature-rich Mashup applications to save time and cost. Under such a background, the ever-increasing number of Web APIs makes the service discovery become a challenge. Thus, Web API recommendation becomes an effective means for service discovery. However, the existing approaches to Web API recommendation still have limitations in extracting rich semantics sufficiently from functional description documents and service networks, resulting in a limited recommendation performance. To further improve the recommendation performance, this paper proposes an effective Web API recommendation approach via exploring textual and structural semantics with contrastive learning and joint training, named CLJT. On one side, discriminative feature representations from textual and structural semantics could be derived by contrastive learning with information correlation across views. On the other side, the derived representations could be applicable to Web API recommendation by joint training of the representation tasks and the recommendation task. Extensive experiments are conducted over a real-world dataset crawled from ProgrammableWeb.com. The experimental results demonstrate the superiority of the proposed approach compared to the baseline methods.

Increasingly low barriers to Internet applications allow a large number of ordinary users to become developers or users of Web services. However, confronted with massive services and complex application scenarios, users often struggle to filter out satisfactory services, in fact, even professional users find it difficult to describe their requirements specifically and accurately in many cases. In order to aggregate more feature information and mitigate the negative impact of low-quality user requirement description, we propose a novel group feature aggregation service recommendation framework (GFASR). Concretely, we first calculate the semantic similarity between users, and create a group for each user according to the similarity ranking. Furthermore, on the basis of learning neural embeddings of users, candidate services, and groups, we employ a dual-attention mechanism to capture effective feature (such as requirement description, service history invoked information, etc.) and preference information of group members for each user, thereby supplementing or enhancing the user’s feature representation. Finally, we aggregate and propagate the information of all embeddings, and a neural and attentional factorization machine model is used to recommend services for users. Comparative experiments on a real dataset demonstrate that our method significantly outperforms the state-of-the-art service recommendation models.

In the context of the rapid development of service-oriented computing and cloud computing, selecting the service that meets the user’s needs from an ever-increasing number of Web services is always challenging. Exploiting auxiliary information such as a Knowledge Graph (KG) can significantly improve the effectiveness of service recommendations. However, current KG-based service recommendation methods usually merely integrate the knowledge semantic information into the user-service interaction model, which ignores the importance of global structure and does not fully consider the in-depth learning of individual user preferences. To this end, this paper proposes a Multi-Level View Contrastive Learning for Service Recommendation (MCSR) approach to address the above challenges. In particular, unlike traditional approaches that consider only two views, we consider three views, i.e., the global structure view, the local collaboration view, and the semantic view. Specifically, the user-service graph is regarded as the collaboration view, the service-entity graph as the semantic view, and the user-service-entity graph as the structural view. By applying contrastive learning across these views at different levels, MCSR fully leverages graph features and structural information, integrating auxiliary relational semantics into user-service interaction modeling. Furthermore, recognizing that the influence of auxiliary information on interactions varies between users and services, a meta-network strategy enables adaptive, personalized knowledge transfer across views, significantly improving recommendation accuracy., since the influence of auxiliary information on interactions varies between users and services, a meta-network strategy enables adaptive, personalized knowledge transfer across views, significantly enhancing recommendation accuracy. The experimental results show that MCSR significantly outperforms current state-of-the-art methods, with the positive impact of its key components on the recommended performance verified by ablation experiments.

Mobile Virtual Reality (MVR), which aims to provide high-quality VR services to mobile devices of end users, has become the latest trend in virtual reality developments. The current MVR solution is to remotely render frame data from a cloud server, while the potential of edge computing in MVR is underexploited. In this paper, we propose a new approach named MUCVR to achieve high-quality interactive MVR collaboration for multiple users by exploiting edge computing. Firstly, we design “vertical” edge–cloud collaboration for VR task rendering, in which foreground interaction is offloaded to an edge server for rendering, while the background environment is rendered by the cloud server. Correspondingly, the VR device of a user is only responsible for decoding and displaying. Secondly, we propose the “horizontal” multi-user collaboration based on edge–edge cooperation, which synchronizes the data among edge servers. Finally, we implement the proposed MUCVR on an MVR device and the Unity VR application engine. The results show that MUCVR can effectively reduce the MVR service latency, improve the rendering performance, reduce the computing load on the VR device, and, ultimately, improve users' quality of experience.

With the rapid development of cloud computing and service computing, service recommendation systems play a crucial role in helping users efficiently filter the appropriate services. However, the sparsity of service data and the presence of noise in interactions make it extremely challenging to accurately capture user preferences. Existing service recommendation methods based on Graph Neural Networks (GNNs) primarily rely on ID aggregation, often neglecting the richness of textual semantics and are susceptible to interaction noise, resulting in suboptimal modeling of user-service relationships. Although Large Language Models (LLMs) demonstrate remarkable advantages in capturing textual semantics, current methods struggle to effectively align structural representations with textual representations, limiting improvements in recommendation performance. To address these challenges, we propose an innovative multi-semantic graph diffusion model for service recommendation, DiffMSR, which aims to align textual and structural representations while learning the generation process of interaction graphs in a denoising manner. This approach mitigates data sparsity and effectively reduces noise interference. Specifically, the model leverages LLMs to capture the textual semantic features of service descriptions and integrates them with structured semantic information from knowledge graphs. Through cross-semantic contrastive learning, it achieves heterogeneous semantic alignment. Furthermore, the model introduces a multisemantic diffusion-based generation framework, which iteratively denoises to construct high-quality user-service interaction graphs. This significantly enhances the multi-semantic awareness of user representations, thereby improving recommendation performance. Experiments on public service datasets demonstrate that DiffMSR outperforms existing state-of-the-art baseline methods, achieving improvements of 4.13% and 6.37% in recommendation accuracy and recall, respectively.

Programming language pre-training models have made significant progress in code representation learning in recent years. Although various methods, such as data flow and Abstract Syntax Tree (AST), have been widely applied to enhance code representation, there has been no research literature, up to date, specifically exploring the use of intermediate code of the source codes for code representation. For example, the intermediate code of Python, namely opcode, not only includes the data input and output stack processes during program execution, but also describes the specific execution order and control flow information. These features are not possessed in source code, data flow, AST and other structures or are difficult to directly reflect. In this paper, we propose OpCodeBERT1approach, which is the first to utilize Python opcode for code representation learning and improves code representation by encoding the underlying execution logic, comments, and source code. To support the training of opcode, we filter the public datasets to exclude unparsable data and innovatively propose an opcode-to-sequence mapping method to convert them into a form suitable for model input. In addition, we pre-train OpCodeBERT using a two-stage masked language modeling (MLM) and a multi-modal contrastive learning. To evaluate the effectiveness of OpCodeBERT, we have done experiment with multiple downstream tasks. The experimental results show that OpCodeBERT performs excellently on these tasks, validating the effectiveness of incorporating opcode and further demonstrating the feasibility of this method in code representation learning.

With the advance of service computing technology, the number of Web APIs has risen dramatically over the Internet. Users tend to use Web APIs to achieve their business needs. However, it is difficult for users to find and select the desirable ones due to the plethora of Web APIs. To address this problem, some collaborative filtering-based Web API recommendation methods have been proposed even though their performance is still far from satisfaction, since they only rely on Mashup-API interactions and feature interactions are not considered in the recommendation model. To further improve the recommendation performance, this paper proposes an interactive Web API recommendation method via exploring both Mashup-API interactions and functional description documents of Mashups and Web APIs. Specifically, LightGCN is employed to derive the node representations for the Mashup-API interaction graph, and BERT model is used for the text representations of functional description documents. Furthermore, the two presentations of both the Mashup and Web API are concatenated as the input of ANFM (Attentional Neural Factorization Machine) model, in which low and high-order feature interactions are fully modeled and the weights of feature interactions are trained by attention mechanism. Solid experiments are conducted over a real-world dataset and the experimental results indicate that the proposed method outperforms the baseline methods.

With the advancement of service computing technology, the Internet has witnessed an exponential proliferation of Web APIs. However, the selection of suitable APIs from this vast pool for Mashup creation poses a great challenge for users. Various Web API recommendation methods have been proposed to address this issue, aiming to simplify the complex selection process. Despite these efforts, limited studies have been conducted on complementary function recommendation. In this context, a general complementary Web API recommendation framework based on a learning model, named CoWAR, is designed to recommend complementary Web APIs tailored for Mashup creation, based on the user’s selected Web APIs. Specifically, we propose a data labeling algorithm to generate the labeled dataset based on Mashup-API interactions derived from historical Mashups and Web APIs. Additionally, we employ BERT model to generate representation vectors of Web APIs based on the functionality description documents. Subsequently, we utilize SANFM (Self-Attentional Neural Factorization Machines) to train the complementary Web API recommendation model with the labeled sample dataset based on representation vectors of Web APIs. To the best of our knowledge, this is the first work addressing the complementary function recommendation problem with a learning model. By conducting a set of experiments over a real-world dataset, the effectiveness of the proposed approach is validated. The experimental results demonstrate that the learning model outperforms the traditional machine learning-based models and several deep learning-based models.

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines.

Abstract The deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development.

With the large-scale commercial use of 5G technology, the era of Mobile Edge Computing with the Internet of Everything as the core is opening. Various computing resources are deployed to the edge of the network near the mobile smart terminal, forming a mobile edge environment for numerous application scenarios. Under this environment, the mobile edge network needs to use the path selection method to obtain one or more service data transmission paths and seamlessly migrates the service data to the most appropriate edge server, to ensure the continuity of edge services and reduce the resource occupation of the mobile edge network. Therefore, this paper proposes a method of Cost-awareServiceMigration Path Selection based on DeepReinforcement Learning (SMART), aiming to jointly optimize communication costs and communication delays under the premise of meeting service requirements. This method transforms the service migration path selection problem in the mobile edge environment into a bi-objective optimization problem under dual constraints, i.e., to find low-latency, low-cost and high-quality service migration paths while satisfying the constraints of computing power resources and transmission time of mobile smart terminals. Then, a DQN is used to construct the corresponding Markov chain decision model according to the problem scenario to find the optimal path for edge service migration. The proposed method learns to select the optimal edge service migration path through the interaction with the environment without obtaining a large amount of historical edge service migration path information in advance. The experimental results onShanghai (Beijing) Telecom mobile communication base station dataset and Shanghai (Beijing) taxi trajectory dataset show that the proposed method can efficiently select low-latency, low-cost, high-quality edge service migration paths in mobile edge environment when vehicles move continuously.It outperforms six typical edge service migration path selection methods, i.e.,Q-learning, A-Star, PLP, PLP/F, PLP/P, and Dijkstra, by at least 15% in all evaluation metrics except computational time.

Systems that integrate distributed databases and existing blockchain platforms have recently emerged, which conveniently leverage their respective strengths to build efficient, secure, and usable data sharing and collaboration environments for different organizations. However, the performance of such systems can be limited by the native blockchain platforms due to the high latency of transactions. In this paper, we present CoralDB, a bottom-up fully redesigned hybrid system of blockchain and database, aimed at enabling untrusted organizations to collaborate and share data efficiently and securely at the database level. The storage layer of CoralDB ensures data security and system throughput through key modules such as customized block structure, consensus mechanism, and transaction pool. On top of the storage layer, a database layer is introduced, which extends the blockchain of the storage layer by incorporating connection pools, collaborative tables, and query interfaces, to enhance the usability and efficiency of data collaboration and sharing. Extensive experimental results demonstrate that CoralDB provides security assurances at the level of blockchain and enables efficient decentralized data collaboration and sharing.

The number of online services is rapidly increasing due to the increased adoption of services-oriented technology. In this context, recommendation systems can provide high-quality Web services that meet Mashup developers’ expectations. The use of different kinds of auxiliary information in recommendation systems is commonplace. They enrich recommendation systems so that they can make relevant recommendations. Yet, knowledge graph-based service recommendation usually only considers the textual semantic information of the service and ignores the importance of discrete attribute information of the service for service recommendation. This may lead to the inability to comprehensively capture the multidimensional characteristics of services, thus affecting the accuracy and reliability of recommendations. To this end, this paper proposes a Web service recommendation method for Mashup creation that exploits pre-training representation and knowledge graphs as auxiliary information. Firstly, it uses the neural factorization machines and Doc2Vec to obtain the text semantic representation and the discrete attribute representation of Web services respectively. Secondly, it combines the text semantic representation and the discrete attribute representation to generate the pre-training representation as the input of knowledge graph convolutional networks. Thirdly, it constructs the Web services knowledge graph using Mashups, Web services, and related information and learns the preferences of Mashup developers and higher-order structural relations between Web services using knowledge graph convolutional networks to complete Web service recommendations. Finally, the proposed method is compared to the baselines, i.e., feature interaction-based (LR, FM, FFM, and NFM), KG-based (RippleNet and KGCN), and Doc2Vec for entity representation-based (DKGCN) Web service recommendation methods, using a real-world dataset from ProgrammableWeb. The experimental results show that the proposed method significantly improves the quality of recommendation in terms of the accuracy, recall, and Micro-F1.

With the wide adoption of SOA (Service Oriented Architecture) in software engineering, a large number of Web services have emerged to meet the Mashup development requirements. Due to the existence of numerous Web services with similar or identical functionalities, it is challenging for users to select the appropriate Web API for Mashup creation, which makes Web service recommendation an effective approach. The performance of current FM-based service recommendation methods is limited by the sparsity of semantic features related to their functionalities. Furthermore, the network structure features of Web services are often overlooked. However, these features are of great importance and should be incorporated into the service recommendation process. Based on the above considerations, this paper proposes a service recommendation model which fuses content information and network information. Firstly, service content information and network structure information are extracted respectively. Then, these two types of information are characterized separately, and their functional semantics are extracted. Finally, the above information is fused and processed by Neural and Attention Factorization Machine to obtain the final recommendation results. Experimental results show that fusing service network representation information can effectively improve the accuracy of service recommendation results.

With the rapid development of service computing, a large number of methods for Web service recommendation have been proposed. However, the existing approaches using Mashup description information ignore the fact that the users without knowledge of Web APIs are not able to describe their needs in detail, let alone find Web services that meet those needs and are compatible with each other. Meanwhile, most approaches that utilize Web API collaboration network based on Mashup-API invocation relationships do not effectively capture the local and global structure between APIs and mine hidden API compatibility information in the network. This paper introduces the KS-GNN model, a novel approach that utilizes graph neural network and auto-encoder techniques for Web API recommendation. Firstly, we utilize KeyBert to extract keywords related to Web services from functional descriptions. Then, we embed the extracted keywords and use their embedded representations as node representation vectors on the Web API collaboration network. Finally, considering local and global structural relationships in the Web API collaborative network and the network structural relationships for message passing, KS-GNN performs keyword searching on the Web API collaborative network, to recommend the top-K Web services that match the user’s query. Experimental results on the ProgrammableWeb dataset show that KS-GNN outperforms other deep learning-based factorization machine recommendation models. In the meantime, we also confirm that the method of extracting keywords using KeyBert outperforms other keyword extraction methods.

With the continually increasing number of Web services, it becomes a challenging task to efficiently and accurately provide Web services that meet developers' functional requirements. Existing heterogeneous graph-based service recommendation methods simply utilize the heterogeneous structural features of the service network and suffer from the missing and blurring of service interaction semantic information due to the characteristics of meta-paths. In fact, service node description documents contain fine-grained semantics generated by multifaceted topic-aware factors, but few efforts are committed to mining them. Therefore, a Web service recommendation method via combining topic-aware heterogeneous graph representation and interactive semantic enhancement is proposed in this paper. It employs an alternating two-step aggregation mechanism, including meta-path instance intra-decomposition and meta-path inter-integration, which uniquely aggregates topic-aware factors according to the inferred topic distributions while preserving structural semantics. Additionally, it introduces the topic prior knowledge guidance module to improve the quality of the inference's topic factors. Simultaneously, the method designs the interactive semantic enhancement module to address the missing and blurring of service interaction semantic information caused by meta-paths. The module explores complex interaction patterns among services and utilizes personalized knowledge meta-network to enhance contrastive learning of service interaction semantics, allowing the personalized knowledge transformer with adaptive contrastive enhancement. The experimental results on the real dataset of ProgrammableWeb show that compared with the other nine methods, the proposed method has better service recommendation performance on evaluation metrics HR and NDCG representing accuracy and satisfaction, respectively.

Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today’s integrated development environments. It has become an important research topic in the field of software engineering. Recent studies have shown that the method of code completion based on the Abstract Syntax Tree (AST) learns syntactic information about the code, which helps to improve the accuracy of code completion. However, when modeling neural networks for ASTs, the sequencing operation of nodes leads to the loss of their hierarchical structure information. Meanwhile, traditional neural networks cannot predict many Out-of-Vocabulary (OoV) words in the terminal node values of AST. To alleviate the above problem, in this paper, we propose a Multiple-Path Learning neural network model for code completion (MPL) based on an AST by learning from a large-scale corpus. In this model, multiple paths such as context path, root path, and terminal node path are established to understand different code features required for node prediction and improve code representation ability. Based on the principle of program local repeatability, it also adopts a replication mechanism to copy the appropriate OoV words from the local terminal node path as the prediction result, further improving the prediction accuracy. The experimental results show that the MPL model has better performance than existing methods on the code completion task.

With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method.

Summary Software developers usually search existing code snippets in open source code repositories to modify and reuse them. Therefore, how to get the right code snippet from the open‐source code repository quickly and accurately is the focus of current software development research. Nowadays, code search is one of the solutions. To improve the accuracy of source code feature information representation and the accuracy of code search. A multilayer self‐ attention residual network‐based code search model (MSARN‐CS) is proposed in this paper. In the MSARN‐CS model, not only the weight of each word in the code sequence unit is considered but also the effect of embedding between code sequence units is calculated. In addition, an optimization model of residuals is introduced to compensate for the loss of information in the code sequences during the model training. To verify the search effectiveness of the MSARN‐CS model, three other baseline models are compared on the basis of extensive source code data. The experimental results show that the MSARN‐CS model has better search results compared with the baseline model. For parameter Recall@1, the experimental result of MSARN‐CS model was 9.547, which as 100.90%, 73.87%, 60.37%, and 2.55% better compared to CODEnn, CRLCS, SAN‐CS‐ and SAN‐CS, respectively. For the parameter Recall@5, the results improved by 26.67%, 36.23%, 36.21%, and 1.63%, respectively, and for the parameter Recall@10, the results improved by 13.92%, 25.70%, 20.78%, and 2.23%, respectively. For the parameter mean reciprocal rank, the results improved by 52.89%, 76.17%, 63.38%, and 3.88%, respectively. For the parameter normalized discounted cumulative gain, the results improved by 54.22%, 60.55%, 50.28%, and 3.30%, respectively. The MSARN‐CS model proposed in the paper can effectively improve the accuracy of code search and enhance the programming efficiency of developers.

Summary Using imbalanced data in classification affect the accuracy. If the classification is based on imbalanced data directly, the results will have large deviations. A common approach to dealing with imbalanced data is to re‐structure the raw dataset via undersampling method. The undersampling method usually uses random or clustering approaches to trimming the majority class in the dataset, since some data in the majority class makes not contribute to classification model. In this paper a revised undersampling approach is proposed. First, we perform space compression in the vertical direction of the separating hyperplane. Then, a weighted random sampling hybrid ensemble learning method is carried out to make the sampled objects spread more widely near the separating hyperplane. Experiments with 7 under‐sampling methods on 21 imbalanced datasets show that our method has achieved good results.

Summary Industrial Internet is a promising technology combining industrial systems with Internet techniques to significantly improve production efficiency and reduce cost by cooperating with intelligent devices. Under industrial internet environment, a production process usually consists of multiple subtasks, and one or more types of product factors are needed to finish a subtask. Thus, the service composition under industrial application is more complex and challenging compared with traditional service composition under the Internet environment. In this article, we model the problem of service composition for production factors under industrial internet as a multiobjective optimization problem. To derive the optimal Pareto service composition plans, we propose a hybrid optimization algorithm, named TLBO‐TS, by combining the advantages of teaching‐learning‐based optimization algorithm and tabu search algorithm. Extensive experiments are conducted to compare with other population‐based optimization methods under a real‐world ship production process to verify the superiority of our approach.

With the increasing number of Web services, how to provide developers with Web services that meet their Mashup requirements accurately and efficiently has become a challenging problem. Therefore, focusing on the problem of “recommending appropriate services to build high-quality Mashup applications”, this paper proposes a Web service recommendation method via combining bilinear graph attention representation and xDeepFM (eXtreme Deep Factorization Machine) quality prediction. This method is based on content and structure-oriented service function classification and combines it with the service invocation prediction based on multi-dimensional quality attributes. Firstly, it uses the Word2Vec model to learn the latent semantic representations from service description documents. Then, it constructs the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggregator is used to model the pairwise interactions between neighbor service nodes. Integrated with the traditional weighted sum aggregator, a bilinear graph neural network (BGNN) with stronger node representation ability is constructed. It exploits BGNN to calculate the representation of service nodes in the network and divides services into different functionality clusters. Finally, the high-quality representation results are combined with multi-dimensional QoS attributes. Aiming at the Web services in the service cluster, it utilizes xDeepFM to model and mine the complex interactions between Web services” features, and predict and rank the invocation scores of Web services. The experimental results on the real dataset of ProgrammableWeb show that compared with the other ten methods, the proposed approach has better performance in terms ofAccuracy,Recall,F1,Logloss, andAUC, and has better performance in classification and recommendation.

The rapid growth in the number and diversity of Web service, coupled with the myriad of similar Web service in functionality, makes it challenging to find most suitable Web service for users to accelerate and accomplish Mashup development. Therefore, this article proposes a Web service recommendation method via integrating heterogeneous graph attention network representation and FiBiNET (Feature Importance and Bilinear feature Interaction NETwork) score prediction. In this method, first, a heterogeneous information service network is constructed by using composite service information, atomic service information, and their respective attribute information. Second, the meta-paths are defined according to different semantic information and service similarity matrixes are built by using commuting matrix and meta-path-based similarity measurement technology. A two-layer attention model is designed to calculate the node level attention and meta-path-level attention of the services respectively, and generate the feature representation of Web service. Third, for the Web services in the service cluster, combining their feature representations with multi-dimensional QoS attributes, the FiBiNET is exploited to dynamically learn the importance of features and complex feature interactions, and predict the score of Web services. Finally, the experiments are performed on the real Web service dataset. The experimental results show that the proposed method is better than the other nine methods in terms of accuracy, recall, F1, and AUC, and achieves better classification and recommendation quality.

Due to the rapid development of Web 2.0, a lot of Web services emerge over the Internet. How to efficiently manage Web services through classification is very important for Web service discovery. Although there have been a lot of works on Web services classification, they still have drawbacks. On one side, the feature extraction from the service repository is insufficient, which will influence the classification accuracy no matter what classification model is used. On the other side, the extracted features are used with the same weights and they lack depth fusion when training the classification model. In real-world application scenarios, different feature interactions often have different predictive capabilities, and not all feature interactions contain useful or positive information for estimating the target. In addition, both low-and high-order feature interactions are usually underlain real-world data. To solve the problems above, this paper proposes a novel Web services classification approach via fully exploring and integrating the content and structural semantics of Web services. In the proposed approach, the content representation is explored with the BERT-based document embedding model, and the structural representation is explored with the Node2vec network embedding model. Finally, attentional neural factorization machine is used for both the deep fusion of features and Web services classification. A set of experiments are done on real-world datasets crawled from Programmable Web. And solid experimental results show that the proposed approach outperforms the state-of-the-art approach and the other baselines.

Business process monitoring aims at identifying how well running processes are performing with respect to performance measures and objectives. The existing business process monitoring techniques focus on collecting and analyzing information on the way business processes themselves are executed. They neglect the evaluation of task-roles which play a key role in the performance of the whole business process execution. Different from the traditional perspective, this paper focuses on monitoring the behavior of task-roles and evaluating their performance with respect to timeliness. Specifically, this paper proposes to promote the performance of task-roles by time reminder via business process monitoring, which is implemented by semantic extension of BPMN elements. Further, we extend the information of process execution event log data, with which the performance of task-roles can be evaluated by analyzing the extended event log data. An empirical study of the proposed approach with real-world business processes reveals the effectiveness with respect to performance evaluation of task-roles.

SUMMARY As more and more companies and organizations encapsulate and publish their business data or resources to the Internet in the form of APIs, the number of web APIs has grown exponentially. For this reason, it has become challenging to quickly and effectively find web APIs from such a large‐scale web API collection, which meet the requirements of mashup developers. To this end, this article focuses on recommending suitable web APIs to build high‐quality mashups by classifying and integrating content‐oriented service functionality with service invocation prediction. The proposed web API recommendation method for mashup development uses graph attention representation and DeepFM quality prediction. First, it uses the web API composition and shared annotation relationships to construct a web API relationship network. Second, it applies the self‐attention mechanism to compute the attention coefficients of different neighboring nodes in the web API relationship network. So, for a specific web API node, the weighted sum of the importance of its neighboring nodes and features characterizes that web API node. Doing so ensures that the service can be divided more accurately into different functional clusters via high‐quality characterization. Third, for the web APIs in a cluster, the high‐quality representation results are combined with multidimensional quality of service attributes. It employs the DeepFM to model and mine complex interaction relationships between features and subsequently predict and rank the invocation scores of web APIs. Finally, experiments are compared and analyzed on real‐world web API datasets. It can be seen from the results of several groups of comparative experiments that the proposed method outperforms other nine baseline methods on accuracy, recall, F1, DCG, and AUC and achieved a good classification accuracy and recommendation effect.

Summary Prognostics and health management enables the prediction of future degradation and remaining useful life (RUL) for in‐service systems based on historical and contemporary data, showing promise for many practical applications. One major challenge for prognostics is the common occurrence of missing values in time‐series data, often caused by disruptions in sensor communication or hardware/software failures. Another major concern is that the sufficient prior knowledge of critical component degradation with a clear failure threshold is often not readily available in practice. These issues can significantly hinder the application of advanced signal and data analysis methods and consequently degrade the health management performance. In this article, we propose a novel data‐driven framework that is capable of providing accurate and reliable predictions of degradation and RUL. In this approach, one‐hot health state indicators are appended to the historical time series so that the model learns end‐of‐life automatically. A modified gate recurrent unit based variational autoencoder is employed in generative adversarial networks to model the temporal irregularity of the incomplete time series. Experiments on multivariate time‐series datasets collected from real‐world aeroengines verify that significant performance improvement can be achieved using the proposed model for robust long‐term prognostics.

Knowledge representation of networked systems is fundamental in many disciplines. To date, existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (nodes) are inherently complex in nature and often contain rich semantics or labels. For example, a user may belong to diverse interest groups of a social network, resulting in multi-label networks for many applications. A multi-label network not only has multiple labels for each node, the labels are often highly correlated making existing methods ineffective or even fail to handle such correlation for node representation learning. In this article, we propose a novel multi-label graph convolutional network (MuLGCN) for learning node representation. To fully explore label-label correlation and network topology structures, we propose to model a multi-label network as two Siamese GCNs: a node-node-label graph and a label-label-node graph. The two GCNs each handle one aspect of representation learning for nodes and labels, respectively, and are seamlessly integrated in one objective function. The learned label representations can effectively preserve the intra-label interaction and node label properties, and are aggregated to enhance the node representation learning under a unified training framework. Experiments and comparisons on multi-label node classification validate the effectiveness of our proposed approach.

Noise and inconsistency commonly exist in real-world information networks, due to the inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have been made to advance feature learning from networks, including the most recent graph convolutional networks (GCNs) or attention GCN, by integrating node content and topology structures. However, all existing methods consider networks as error-free sources and treat feature content in each node as independent and equally important to model node relations. Noisy node content, combined with sparse features, provides essential challenges for existing methods to be used in real-world noisy networks. In this article, we propose feature-based attention GCN (FA-GCN), a feature-attention graph convolution learning framework, to handle networks with noisy and sparse node content. To tackle noise and sparse content in each node, FA-GCN first employs a long short-term memory (LSTM) network to learn dense representation for each node feature. To model interactions between neighboring nodes, a feature-attention mechanism is introduced to allow neighboring nodes to learn and vary feature importance, with respect to their connections. By using a spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task. Experiments and validations, w.r.t. different noise levels, demonstrate that FA-GCN achieves better performance than the state-of-the-art methods in both noise-free and noisy network environments.

Genetic algorithms (GAs) have been widely applied in Steiner tree optimization problems. However, as the core operation, existing crossover operators for tree-based GAs suffer from producing illegal offspring trees. Therefore, some global link information must be adopted to ensure the connectivity of the offspring, which incurs heavy computation. To address this problem, this article proposes a new crossover mechanism, called leaf crossover (LC), which generates legal offspring by just exchanging partial parent chromosomes, requiring neither the global network link information, encoding/decoding nor repair operations. Our simulation study indicates that GAs with LC outperform GAs with existing crossover mechanisms in terms of not only producing better solutions but also converging faster in networks of varying sizes.

Clustering Web services can improve the quality and efficiency of service discovery and management within a service repository. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex and heterogeneous service relationship network. The rich network relations inherently reflect either positive or negative clustering association between Web services, which can be a strong supplement to service semantics for characterizing functional affinities between Web services. In this paper, we propose to cluster Web services by utilizing both description documents and the structural information from the service relationship network. We first learn the content semantic information from service description documents based on the widely used Doc2vec model, and meanwhile, learn the structural semantic information from the service relationship network based on a network representation learning algorithm. Then, we propose to pretrain the content and structural semantic information to obtain the most relevant and unified features through training a service classification model with partially labeled data. Finally, a spectral clustering algorithm is utilized for Web services clustering based on the above unified features with preserved content and structural semantics. Therefore, the proposed services clustering approach takes advantage of both service content semantic and service network structure semantic based similarity between services. Extensive experiments are conducted on a real-world dataset from ProgrammableWeb, composed of 12919 Web API services. Experimental results demonstrate that our approach yields an improvement of 4.78% in precision and 5.4% in recall over the state-of-the-art method.

Extensive efforts have been applied to develop efficient feature extraction algorithms, which aim to achieve optimal results in many fundamental tasks such as Web-based software service clustering, recommendation and composition. However, one common issue for existing methods is that mined features are problem dependent, causing poor generalization ability across different applications. Recent studies show that we can represent networked data (e.g., citation networks and social networks) as low-dimensional vectors with rich structure and content information preserved, which can then greatly facilitate many downstream tasks such as classification and clustering. In this article, we focus on the problem of Web service network embedding, which aims to learn low-dimensional vectors to represent services by encoding both Mashup-API composition structure and service functional content. We first propose a novel probabilistic topic model to predict potential links between Mashups and APIs in the service network. Then, we develop a Service Graph Convolutional Network (Service-GCN) to learn vector representations of services, where each service (e.g., Mashup or API) forms its representation through message passing between neighborhood services over the network. We evaluate the network embedding quality on two real-world datasets for downstream classification and clustering tasks. Experimental results show that the average performance of our method improves 20.7 percent (Micro-F1) in service classification and 19.0 percent (Accuracy) in Mashup clustering compared to the state-of-the-art, which verified the effectiveness of the proposed approach for learning vector representations of Web services.

QoS prediction for Web Services is becoming increasingly important for various QoS-aware Web Services management tasks. However, the existing methods for QoS prediction of Web Services have some drawbacks, such as poor performance in dealing with data sparsity, insufficient consideration of latent information in user-service interaction behavior, and no consideration on discriminating the weight of latent information. To address these shortcomings, this paper proposes a QoS Prediction approach via combining multi-component graph convolutional collaborative filtering and deep factorization machine. A user-service bipartite graph is constructed, and the edges of the graph are decomposed into multiple latent spaces with node-level attention to identify latent components. Then, the importances of latent components are determined, and they are aggregated to obtain the corresponding user-service embedding vectors. Finally, the embedding vectors are taken as the input of a deep factorization machines model to obtain the prediction of unknown QoS. Extensive experiments are conducted on a real-world dataset. The experimental results demonstrate that MGCCF-DFM achieves superior prediction accuracy in terms of mean absolute error (MAE) and root mean square error (RMSE) compared with the existing QoS prediction techniques.

Service classification helps to improve the efficiency of service discovery. Previous methods mainly focus on homogeneous graph-based service classification. However, due to the heterogeneity of service data in the real world, these methods cannot deal with many types of nodes and edges in service relationship network well, and lack the usage of rich semantic information. The emergence of heterogeneous graph attention network can effectively solve the problems, because it can more completely and naturally extracts the relationships and nodes from the service relationship network, and well distinguishes the importance of neighbor nodes and meta paths. Therefore, this paper proposes a heterogeneous graph attention network-enhanced Web service classification method. In this method, firstly, a heterogeneous information service network is constructed by using composite service information, atomic service information and their attribute information. Then, the meta path is defined according to different semantic information, and the similarity matrix of service is constructed by using the commuting matrix and the similarity measurement technology based on meta path. Finally, a two-layer attention model is designed to calculate the node-level attention and meta path-level attention of the service, so as to obtain the node-level representations and meta path-level representations of the services, and generate more representative embedding features of services for achieving more accurate service classification. Finally, the experimental results on real datasets of ProgrammableWeb show that our method is better than GAT, GCN, Metapath2Vec, Node2Vec, BiLSTM and LDA in terms of precision, recall and macro F1, and improves the accuracy of Web service classification.

Learning network representations of Web services plays a critical role in the service ecosystem and facilitates many downstream tasks, e.g., service composition, service recommendation, service clustering, and service classification, etc. However, the performance of most of the existing approaches is limited by the sparse and non-interaction relationships between services. Considering these shortcomings, by proposing a balance theory based weighted signed graph convolutional network, we explore a dedicated signed service link prediction method to expand accurate links in service relation networks. Concretely, we first define the positive and negative links based on historical prior knowledge concerning services, and then construct a signed service relation network. Furthermore, on the basis of quantifying the influence of different neighbor nodes, we employ balance theory to correctly aggregate and propagate the information across layers through a weighted signed graph convolutional network. Finally, we splice all service embeddings in pairs, and a multi-layer perceptron classifier is used to predict the links between services. Comparative experiments with six baselines demonstrate that our method significantly outperforms the state-of-the-art link prediction models.

Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.

Abstract With the rapid growth of the number of Web services on the Internet, how to classify Web services correctly and efficiently become particularly important in service management tasks, such as service discovery, service selection, service ranking, and service recommendation. Existing functionality‐based service classification techniques have some drawbacks: (1) the keyword order and context information are not considered; (2) the embedding features of keywords are taken as equal importance to learn the classification model; (3) the topic number is hard to determine manually. Due to these drawbacks, the accuracy of service classification needs to be improved further. At present, deep learning techniques show the strong power in modeling complex and nonlinear function relationship. Thus, to address the problems above, this paper exploits attention mechanism to combine the local implicit state vector of Bidirectional Long Short‐Term Memory Network (BiLSTM) and the global hierarchical Dirichlet process (HDP) topic vector, and proposes a Web service classification approach with topical attention‐based BiLSTM. Specifically, BiLSTM is used to automatically learn the keyword feature representations of Web services. Then, the topic vectors of Web service documents are obtained with HDP by offline training, and topic attention mechanism is adopted to strengthen the feature representation by discriminating the importance or weight of different keywords in Web service documents. Finally, the enhanced Web service feature representation is used as the input of a softmax neural network layer to perform the classification prediction for Web services. Extensive experiments are conducted to validate the effectiveness of the proposed approach.

Summary Condition monitoring and fault diagnosis of main bearings of large‐scale wind turbines is critical for improving its reliability and reducing operating and maintenance costs, especially in the early stages. To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi‐layer perceptron (SSAE‐MLP) with a new framework by utilizing supervisory control and data acquisition (SCADA) data for wind turbine main bearing temperature prediction. After the SCADA parameter variables related to the temperature change of the main bearing are extracted, the input characteristic vector is constructed. Then, the multiple sparse autoencoders are stacked to learn the deep features inside the input data by applying the greedy layerwise unsupervised learning algorithm. Finally, a regression predictor is added to the top layer of the stacked sparse autoencoder model for supervised learning to fine‐tune the overall network. Comparative experiments show that the proposed approach has superior performance for wind turbine main bearing temperature prediction.

Summary With the increasing number of Web services, Web service discovery for service‐oriented application development has become more important. Clustering or classifying Web services according to their functionalities is an effective way for Web service discovery. Extracting latent topic features from service description by exploiting topic model can improve the accuracy of service classification. However, most of them simply treat the description document as a set of flat word features without considering the varying importance of different features as well as sequential relations between features. In this article, we proposed a Web service classification approach based on information gain theory and bidirectional long short‐term memory with attention mechanism for accuracy Web service classification by considering fine‐grained factors implicit in Web service description. The comparative experiments are performed on ProgrammableWeb dataset, and show that the proposed method achieves a significant improvement compared with baseline methods.

The wide adoption of Service Oriented Architecture (SOA) has driven the creation of a massive amount of applications on the Internet, which includes the popular Mashups composed from multiple existing Web APIs. The availability of a large number of Web APIs with diverse functionalities on the Web makes it difficult for users to find APIs meeting their needs for Mashup development. To relieve this difficulty, recommending Web APIs for Mashup development has become an effective solution. A dozen of service recommendation approaches were proposed based on multi-dimensional features extracted from the service repository over the last couple of years, e.g., similarity based matching methods, matrix factorization based models, and factorization machine based models. Among these existing works, Factorization Machine (FM) based models, in particular the deep learning based FM models, have shown better performance compared with other conventional collaborative filtering techniques. Despite their superiority, the deep learning based FMs still have some strong model assumptions that can harm the recommendation accuracy. For example, it models factorized interactions with the same weight and ignores the non-linear and complex inherent structure in data. In a real-world service recommendation scenario, different predictor variables usually have different predictive power and not all features are predictable for estimating the target. Also, higher-order feature interactions are usually underlain in complex user-service environments. To address these deficiencies, this paper proposes a hybrid factorization machine model with a novel neural network architecture, named NAFM, which integrates a deep neural network to capture the non-linear and complex feature interactions and uses an attention mechanism to capture the varying importance of feature interactions. Comprehensive experiments are conducted on a real-world dataset from ProgrammableWeb. The experimental results show that the proposed approach outperforms the existing state-of-the-art models for service recommendation.

Classifying Web services plays a critical role in several fundamental service management tasks, such as service discovery, selection, ranking, and recommendation. However, traditional Web service classification approaches usually difficult to dispose unstructured sparse documents and underutilize the rich network relations. The consideration of multiple document representation schemes can ameliorate the former problem, whereas an appropriate network representation method could be a positive solution to the latter problem. In this paper, we propose a general Web service classification framework via deep fusion of structured and unstructured features, named LDNM. Firstly, we transform each service document into feature vectors by using two document representation methods: topic distribution based on LDA, and neural-network-based document embedding model known as Doc2vec. Then we obtain structured representation vectors which stem from service invoking and tagging graphs by applying Node2vec. Finally, we fuse these features and train a service classifier by using an MLP neural network. Comprehensive experiments are conducted on real-world datasets to demonstrate the effectiveness of the proposed approach.

Tagging systems have been widely used as a major way of managing Web service resources. Many portals such as ProgrammableWeb and BioCatalogue allow users to create manual tags annotating Web services and their compositions (e.g., mashups). This is extremely helpful for managing and retrieving enormous Web service data. In the past few years, many tag recommendation approaches have been proposed for Web services that contain few or no tags. Most of them only exploit the textual content or tag service matrix information. Sometimes those approaches suffer from the data sparsity problem, especially when Web services have only few tags or their auxiliary textual contents are hard to be obtained. In real world, a plenty of relationships are available in recommendation systems, e.g., the composition relationship between services and the annotation relationship between mashups and tags. These multi-relational data can be utilized as additional features to improve the recommendation performance. In this paper, we exploit various types of relationships as features and propose a novel topic-sensitive approach based on the Factorization Machines for mashup tag recommendation. Factorization Machines is utilized to model the pair-wise interactions between all features and predict adequate tags for mashups. In this approach, we first obtain the latent topics of all tags as well as the description documents for mashups and APIs based on a novel probabilistic topic model. Then, a multi-relational network by mining various relationships from the Web service data is constructed. Various auxiliary informations are subsequently extracted from the network to train the Factorization Machines. The proposed model is evaluated on three real-world datasets and the experimental results show that it outperforms several state-of-the-art methods.

With the development of 5G, the advancement of basic infrastructure has led to considerable development in related research and technology. It also promotes the development of various smart devices and social platforms. More and more people are now using smart devices to post their reviews right after something happens. In order to keep pace with this trend, we propose a method to analyze users’ sentiment by using their text data. When analyzing users’ text data, it is noted that a user’s review may contain many aspects. Traditional text classification methods used by smart devices, however, usually ignore the importance of multiple aspects of a review. Additionally, most algorithms usually ignore the network structure information between the words in a sentence and the sentence itself. To address these issues, we propose a novel dual‐level attention‐based heterogeneous graph convolutional network for aspect‐based sentiment classification which minds more context information through information propagation along with graphs. Particularly, we first propose a flexible HIN (heterogeneous information network) framework to model the user‐generated reviews. This framework can integrate various types of additional information and capture their relationships to alleviate semantic sparsity of some labeled data. This framework can also leverage the full advantage of the hidden network structure information through information propagation along with graphs. Then, we propose a dual‐level attention‐based heterogeneous graph convolutional network (DAHGCN), which includes node‐level and type‐level attentions. The attention mechanisms can analyze the importance of different adjacent nodes and the importance of different types of nodes for the current node. The experimental results on three real‐world datasets demonstrated the effectiveness and reliability of our model.

With the rapid growth of mobile applications in major app stores, it is hard for users to choose their desired mobile applications. Therefore, it is necessary to provide a high-quality mobile application recommendation mechanism to meet the user's expectation. However, the existing recommendation methods are still not accurate enough in the embedding representations of users and mobile applications. Based on the neural graph collaborative filtering technique, we propose a mobile application recommendation method based on user interaction to solve this problem. First of all, by introducing the high-order connectivity between users and mobile applications, it exploits the embedding propagation to capture the collaborative filtering signals along the graph structure to further refine the embedding representations between mobile applications and users. Then, the user preferences for different mobile applications are predicted through inner product, and the recommendation task is completed. The real dataset of Kaggle is used to evaluate our approach and the experimental results show that our recommendation method can achieve the best results in different evaluation metrics. It can effectively improve the recommendation accuracy for mobile applications.

With the wide adoption of SOA (Service Oriented Architecture), a massive amount of innovative applications emerge on the Internet. One of the popular representations is Mashup composed of multiple Web APIs. Recommending desirable Web APIs to develop Mashup applications has attracted much attention. A dozen of service recommendation approaches are proposed by incorporating multi-dimensional features extracted from service repository into recommendation models. Among the existing works, factorization machine based models show better performance than traditional collaborative filtering techniques in accuracy. However, they either model factorized interactions with the same weight or neglect the non-linear and complex inherent structure of real-world data. In real-world applications, different predictor variables usually have different predictive power, and not all features contain useful signal for estimating the target. Moreover, higher-order feature interactions are usually underlain in real-world data. To address these drawbacks, this paper proposes a hybrid factorization machine model with a novel neural network architecture named NAFM by integrating deep neural network to capture the non-linear feature interactions and attention mechanism to capture the different importance of feature interactions. Comprehensive experiments on a real-world dataset show that the proposed approach outperforms the other state-of-the-art models for service recommendation.

With the explosive growth of Web APIs on the Internet, it is a challenge to recommend desirable Web APIs from multiple ecosystems to develop a Mashup. Most existing API service recommendation methods focus on functional semantic similarity, but underutilize the rich network relations which inherently reflect either positive or negative relevance between services. Moreover, in the recommendation process, they usually pay too much attention to the interactions between Mashups and APIs, but ignore the cooperation between APIs. In this paper, we propose a novel method named SRAWN (Structure Reinforcing and Attribute Weakening Network) based API recommendation approach for Mashup creation. Specifically, we first design a feature extractor layer to capture structure relationship and attribute information from an API relation network graph by introducing a GAT2VEC framework, and obtain representation vectors corresponding to each API. Then, a matching evolving layer is proposed to capture the matching evolving process between APIs. At this layer, APIs are chosen incrementally to composite a Mashup, and the embedding vectors of the Mashup's existing composition features are updated adaptively based on diverse candidate APIs, by introducing a Deep Interest Network. Comprehensive experiments on a real-world dataset show that SRAWN outperforms the other state-of-the-art solutions.

Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization.

With the rapid development of Internet, the number of Web services is increasing sharply, which makes it more difficult for Mashup developers to find suitable Web services. Nowadays, there are numerous methods to improve Web service recommendation, but it is still a challenging problem to recommend Web services with both good accuracy and satisfying diversity. Collaborative filtering is a common algorithm in recommendation system, but it often faces serious cold start and sparsity problems. To alleviate the above problems, this paper proposes a Web service recommendation method based on knowledge graph convolutional network and Doc2Vec. First of all, it constructs the knowledge graph of Web services based on the additional information such as the categories, developers, scope of application of Web services, and adopts knowledge graph convolutional networks to mine the higher-order relationship between Web service and the preference information of Mashups. Secondly, it employs Doc2Vec to mine the semantics of Web service description documents, and integrates the Mashup preference information and the Mashup semantic information in the training process, so as to predict Web services needed for Mashup development. Finally, the experiment is conducted on the latest Programmable Web dataset and the experimental results show that the recommended performance of the proposed method is better than that of FM, NCF, CKE, RippleNet, KGCN.

Summary Traditional market basket recommendation approaches normally cannot well recommend unpopular commodities in big data environment. To address such problem and deal with large datasets of practical supermarkets, this paper presents a market basket recommendation framework and proposes an Extended algorithm based on Collaborative Filtering and Association Rule mining, named ECFAR. The ECFAR covers two sub‐algorithms. First, a parallel FP‐Growth algorithm is used for mining association rules on Spark, which is designed to increase the efficiency of processing big data. Then, a parallel similar commodity discovery method based on matrix factorization is proposed. By analyzing a real‐world sales dataset collected from a local supermarket group, extensive experiments are conducted to verify its effectiveness.

Summary Resource usage prediction is increasingly important in cloud computing environments, and CPU usage prediction is especially helpful for improving the efficiency of resource provisioning and reducing energy consumption of cloud datacenters. However, accurate CPU usage prediction remains a challenge and few works have been done on predicting CPU usage of physical machines in cloud datacenters. In this article, we present a deep belief network (DBN) and particle swarm optimization (PSO) based CPU usage prediction algorithm, which is named DP‐CUPA and aimed to provide more accurate prediction results. The DP‐CUPA consists of three main steps. First, the historic data on CPU usage are preprocessed and normalized. Then, the autoregressive model and grey model are adopted as base prediction models and trained to provide extra input information for training DBN. Finally, the PSO is used to estimate DBN parameters and the DBN neural network is trained to predict CPU usage. The effectiveness of the DP‐CUPA is evaluated by extensive experiments with a real‐world dataset of Google cluster usage trace.

Summary Recommender systems are becoming increasingly critical to the success of commerce sales. In spite of their benefits, they suffer from some major challenges including recommendation quality such as the accuracy, diversity, and novelty of recommendations. In the context of retail business, the novelty of recommendations is of especial importance because it can directly affect customers' probabilities of buying commodity and whether to visit stores again. However, tradition algorithms for retail commodity recommendation never consider the problem of improving the novelty of recommendations. To address this, a novel multiarmed bandit and gradient boosting decision tree‐based retail commodity recommendation approach is proposed in this article, which is named MGRCR. It can increase recommendations' novelty while maintaining comparable levels of in the context of retailing. The effectiveness of our proposed approach has been proved by comprehensive experiments with real‐world commerce datasets and different state‐of‐the‐art recommendation techniques.

The rapid growth in the number and diversity of Web APIs, coupled with the myriad of functionally similar Web APIs, makes it difficult to find most suitable Web APIs for users to accelerate and accomplish Mashup development. Even if the existing methods show improvements in Web APIs recommendation, it is still challenging to recommend Web APIs with high accuracy and good diversity. In this paper, we propose an integrated content and network-based service clustering and Web APIs recommendation method for Mashup development. This method, first develop a two-level topic model by using the relationship among Mashup services to mine the latent useful and novel topics for better service clustering accuracy. Moreover, based on the clustering results of Mashups, it designs a collaborative filtering (CF) based Web APIs recommendation algorithm. This algorithm, exploits the implicit co-invocation relationship between Web APIs inferred from the historical invocation history between Mashups clusters and the corresponding Web APIs, to recommend diverse Web APIs for each Mashups clusters. The method is expected to not only find much better matched Mashups with high accuracy, but also diversify the recommendation result of Web APIs with full coverage. Finally, based on a real-world dataset from ProgrammableWeb, we conduct a comprehensive evaluation to measure the performance of our method. Compared with existing methods, experimental results show that our method significantly improves the accuracy and diversity of recommendation results in terms of precision, recall, purity, entropy, DCG and HMD.

The advent of Service-Oriented Architecture (SOA) has brought a fundamental shift in the way in which distributed applications are implemented. An overwhelming number of Web-based services (e.g., APIs and Mashups) have leveraged this shift and furthered development. Applications designed with SOA principles are typically characterized by frequent dependencies with one another in the form of heterogeneous networks, i.e., annotation relations between tags and services, and composition relations between Mashups and APIs. Although prior work has shown the utility gained by exploring these networks, their analysis is still in its infancy. This article develops an approach to learning representations of the Web service network, which seeks to embed Web services in low-dimensional continuous vectors with preserved information of the network structure, functional tags, and service descriptions, such that services with similar functional properties and network structures are mapped together in the learned latent space. We first propose a topic generative model for constructing two topic distribution networks (Mashup-Topic and API-Topic) from the service content. Then, we present an efficient optimization process to derive low-dimensional vector representations of Web services from a tri-layer bipartite network with the Mashup-Topic and API-Topic networks on two ends and the Mashup-API composition network in the middle. Experiments on real-word datasets have verified that our approach is effective to learn robust low-rank service representations, i.e., 25% F1-measure gain over the state-of-the-art in Web service recommendation task.

Clustering Web services can promote the quality of services discovery and management within a service repository. Traditional clustering methods primarily focus on using the semantic distance between service features, i.e., latent topics learned from WSDL documents, to measure the service content similarity between Web services. Few works exploited the structural information generated during the usage of Web services, i.e., the service compositing and tagging behaviors. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex service relationship network. The rich network relations inherently reflect either positive or negative categorical relevance between services, which can be strong supplement of service semantics in characterizing the functional affinities between services. In this paper, we propose to utilize the services relationship network for augmented services clustering algorithm design. We first learn semantic information from service descriptions based on the widely used Doc2Vec model. Then, we propose a revised K-means algorithm for service clustering that benefits simultaneously from service semantics and network relations, where the service relations are previously preserved in a set of low-dimensional vectors achieved based on a recently proposed network embedding technique. Experiments on a real-world dataset demonstrated that the proposed clustering approach yields an improvement of 6.89% than the state-of-the-art.

The service-oriented architecture makes it possible for developers to create value-added Mashup applications by composing multiple available Web services. Due to the overwhelming number of Web services online, it is often hard and time-consuming for developers to find their desired ones from the entire service repository. In the past, various approaches aim at recommending Web services for automatic Mashup creation have been proposed, i.e., TFIDF, collaborative filtering and topic model-based methods, which rely on the original service descriptions given by service providers. However, most traditional methods fail to capture the function-related features of services since words contained in service descriptions usually correspond to different intent aspects (e.g., functional and non-functional related). To tackle this problem, we propose a tag attention-based recurrent neural networks model for Web service recommendation. The model consists of two Siamese bidirectional Long Short-Term Memory (LSTM) networks, which jointly learn two embeddings representing the functional features of Web services and the functional requirements of Mashups. In addition, by considering the tags of services as functional context information, the model can learn to assign attention scores to different words in service descriptions according to their intent importance, thus words used to reveal the functional properties of Web service will be given special attention. We compare our approach with the state-of-the-art methods (e.g., RTM, Word2vec, etc.) on a real-world dataset crawled from ProgrammableWeb, and the experimental results demonstrate the effectiveness of the proposed model.

Service recommendation is a fundamental task in many application environments (e.g., Mashup creation and cloud computing). In the past, various methods have been proposed to facilitate the service selection process based on the original functional descriptions. However, the mined features from the descriptions are usually too sparse for training a well-performed model. In addition, most methods neglect to differentiate the weights of various features, while words included in descriptions usually exhibit different intentions (e.g., functional or non-functional). To address these challenges, in this paper we propose a text expansion and deep model-based approach for service recommendation. Specifically, we first expand the description of services at sentence level based on a novel probabilistic topic model that learns topics of words, sentences and descriptions in a stratified fashion. The expansion process can bridge the vocabulary gap between services and user queries with the collective semantic similarity of sentences and descriptions. Then, we propose a Long Short-Term Memory-based model to recommend services with two attention mechanisms - a functional attention mechanism that takes tags as functional prior to mine the function-related features of services and Mashups, and a contextual attention mechanism that considers Mashup requirements as application scenario to help select the most appropriate services. We evaluate the proposed approach on a real-world dataset and the results show it has an improvement of 34 percent in F-measure over the basic LSTM model.

Due to the rapid growth in both the number and diversity of Web services on the web, it becomes increasingly difficult for us to find the desired and appropriate Web services nowadays. Clustering Web services according to their functionalities becomes an efficient way to facilitate the Web services discovery as well as the services management. Existing methods for Web services clustering mostly focus on utilizing directly key features from WSDL documents, e.g., input/output parameters and keywords from description text. Probabilistic topic model Latent Dirichlet Allocation (LDA) is also adopted, which extracts latent topic features of WSDL documents to represent Web services, to improve the accuracy of Web services clustering. However, the power of the basic LDA model for clustering is limited to some extent. Some auxiliary features can be exploited to enhance the ability of LDA. Since the word vectors obtained by Word2vec is with higher quality than those obtained by LDA model, we propose, in this paper, an augmented LDA model (named WE-LDA) which leverages the high-quality word vectors to improve the performance of Web services clustering. In WE-LDA, the word vectors obtained by Word2vec are clustered into word clusters by K-means++ algorithm and these word clusters are incorporated to semi-supervise the LDA training process, which can elicit better distributed representations of Web services. A comprehensive experiment is conducted to validate the performance of the proposed method based on a ground truth dataset crawled from ProgrammableWeb. Compared with the state-of-the-art, our approach has an average improvement of 5.3% of the clustering accuracy with various metrics.

Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships, and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user's past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile, respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.

Search personalization that considers the social dimension of the web has attracted a significant volume of research in recent years. A user profile is usually needed to represent a user?s interests in order to tailor future searches. Previous research has typically constructed a profile solely from a user?s usage information. When the user has only limited activities in the system, the effect of the user profile on search is also constrained. This research addresses the setting where a user has only a limited amount of usage information. We build enhanced user profiles from a set of annotations and resources that users have marked, together with an external knowledge base constructed according to usage histories. We present two probabilistic latent topic models to simultaneously incorporate social annotations, documents and the external knowledge base. Our web search strategy is achieved using personalized social query expansion. We introduce a topical query expansion model to enhance the search by utilizing individual user profiles. The proposed approaches have been intensively evaluated on a large public social annotation dataset. Results show that our models significantly outperformed existing personalized query expansion methods which use user profiles solely built from past usage information in personalized search.

The rapid growth in the number and diversity of Mashup services, coupled with the myriad of functionally similar Mashup services, makes it difficult to find suitable Mashup services to develop Mashup-based software applications due to an unprecedentedly large number of choices of Mashup services. Even if the existing latent factor based methods show significant improvements in Mashup service clustering and discovery, it is still challenging to find Mashup services with high accuracy due to overlooking of relationships among Mashup services. The relationships among Mashup services actually can be exploited in mining latent functional factors to improve the accuracy of clustering and discovery. In this paper, we propose a Mashup service clustering method based on an integration of service content and network via exploiting a two-level topic model. This method, firstly designs a two-level topic model to mine latent topics for representing functional features of Mashup services. Secondly, it uses two different random walk processes to derive and incorporate the topic distribution of Mashup services at service network level into the topic distribution of Mashup services at the service content level. Thirdly, K-means and Agnes algorithm are used to perform Mashup service clustering based on latent topics' similarity. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing clustering approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, purity and entropy.

Collaborative filtering approach based on rating is one of the most broadly used service recommendation approach. However, rating data is very sparse in most service recommender systems, which seriously impacts the precision of service recommendation. In view of this problem, a usage-based service recommendation approach is proposed in this paper. What is special about this approach is that usage information instead of rating data is recruited to infer user interest. Some experiments are implemented to verify the efficient of this approach.

Mashups are prevalent Service-Oriented Architecture (SOA) based applications consisting of multiple Web Application Programming Interfaces (APIs) and content. Tags have been extensively used to organize and index mashup services. However, people favor manual tags creation in the past. This approach demands user intervention, which is extremely time-consuming and probes to errors. In this paper we propose a novel Mashup-API-Tag model for automatic mashup tag recommendation. The model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to discover the latent topics. Then the semantic similarity between Web APIs and mashups can be acquired. Subsequently, tags of chosen APIs are recommended to a mashup where the mashup and the APIs are most similar. In addition, we develop a tag filtering algorithm to select the most relevant tags for recommendation. The experimental results on a real world dataset prove that our approach outperforms other methods, including frequency-based methods and the methods that only consider the composition relationships and the annotation relationships separately.

Quality of services (QoS) is an important concern in Web service recommendation or selection. Predicting QoS values of Web services based on their historical QoS records is an effective way to acquire Web service QoS, and thus has attracted considerable research interests. Recently, matrix factorization (MF), a well-known model-based collaborative filtering (CF) technique, has been successfully applied to the Web service QoS prediction. It is generally believed that MF can significantly outperform traditional memory-based CF techniques. However, previous work seldom considered the influence of the underlying network on Web service QoS when adopting MF for Web service QoS prediction. Hence, the prediction performance is not good enough. In this paper, we propose a network-aware Web service QoS prediction approach by integrating MF with the network map. By employing the network map, network distances between service users can be measured and neighborhoods of users are identified. Then, the traditional MF model is revamped by incorporating the constraint term that neighbor users are likely to perceive similar QoS of Web services. Experiments conducted on two real-world Web service datasets indicate that our approach outperforms previous MF and CF-based approaches in prediction accuracy.

The reliability and security of multimedia contents in transmission, communications, storage, and usage have attracted special attention. Robust image hashing, also referred to as perceptual image hashing, is widely applied in multimedia authentication and forensics, image retrieval, image indexing, and digital image watermarking. In this work, a novel robust image hashing method based on quaternion Zernike moments (QZMs) is proposed. QZMs offer a sound way to jointly deal with the three channels of color images without discarding chrominance information; the generated hash is thus shorter than the hash of three channels separately processing. The proposed approach's performance was evaluated on the color images database of UCID and compared with several recent and efficient methods. These experiments show that the proposed scheme provides a short hash in length that is robust to most common image content-preserving manipulations like JPEG compression, filtering, noise, scaling, and large angle rotation operations.

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users’ potential requirements has become of paramount importance. Existing web service recommendation approaches mainly focus on predicting missing QoS values of web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web services may exist in a recommendation list. In this paper, we propose a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services. User's interests and QoS preferences on web services are first mined by exploring the web service usage history. Then we compute scores of web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web service graph based on the functional similarity between web services. Finally, we present an innovative diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.

Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.

Recently, collaborative filtering has been applied to QoS-aware Web service recommendation. However, it cannot make recommendations for users that have invoked only a very small number of services because of data sparsity. In addition, these methods do not know how confident they are in their recommendations. Based on the fact that QoS values of web services are usually subject to the locations of users, a few works assume that the additional knowledge of users' locations can be used to better deal with the data sparsity issue, since a user only needs to know the users near to him/her. On the other hand, the sparsity of user-service invocations forces the location-aware method to consider the QoS experiences of users not near enough, which may decrease its precision. In order to find a good trade-off between coverage and precision, we propose a random walk method combining location-aware and collaborative filtering method for web service recommendation. The random walk method allows us to define and to measure the confidence of a recommendation. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world web service dataset, and compared the method with existing collaborative filtering methods.

Summary To assess the quality of services (QoS) in service selection, collaborative service QoS prediction has recently garnered increasing attention. They focus on exploring the historical QoS information generated by interactions between users and services. However, they may suffer from the data sparsity issue because interactions between users and services are usually sparse in real scenarios. They also seldom consider the network environments of users and services, which surely will affect cloud service QoS. To address the data sparsity issue and improve the QoS prediction accuracy, the following paper proposes a collaborative QoS prediction method with location‐based data smoothing. The method first computes neighborhoods of users and services based on their locations which provide a basis for data smoothing. It then combines user‐based and service‐based collaborative filtering techniques to make QoS predictions. Experiments conducted using a real service invocation dataset validate the performance of the proposed QoS prediction method. Copyright © 2015 John Wiley & Sons, Ltd.

Service-oriented computing and Web services are becoming more and more popular, enabling organizations to use the Web as a market for selling their own Web services and consuming existing Web services from others. Nevertheless, with the increasing adoption and presence of Web services, it becomes more difficult to find the most appropriate Web service that satisfies both users' functional and nonfunctional requirements. In this paper, we propose an effective Web service ranking approach based on collaborative filtering (CF) by exploring the user behavior, in which the invocation and query history are used to infer the potential user behavior. CF-based user similarity is calculated through similar invocations and similar queries (including functional query and QoS query) between users. Three aspects of Web services-functional relevance, CF based score, and QoS utility, are all considered for the final Web service ranking. To avoid the impact of different units, range, and distribution of variables, three ranks are calculated for the three factors respectively. The final Web service ranking is obtained by using a rank aggregation method based on rank positions. We also propose effective evaluation metrics to evaluate our approach. Large-scale experiments are conducted based on a real world Web service dataset. Experimental results show that the proposed approach outperforms the existing approach on the rank performance.

Cloud computing promises a scalable infrastructure for processing big data applications such as medical data analysis. Cross-cloud service composition provides a concrete approach capable for large-scale big data processing. However, the complexity of potential compositions of cloud services calls for new composition and aggregation methods, especially when some private clouds refuse to disclose all details of their service transaction records due to business privacy concerns in cross-cloud scenarios. Moreover, the credibility of cross-clouds and on-line service compositions will become suspicional, if a cloud fails to deliver its services according to its “promised” quality. In view of these challenges, we propose a privacy-aware cross-cloud service composition method, named HireSome-II (History record-based Service optimization method) based on its previous basic version HireSome-I. In our method, to enhance the credibility of a composition plan, the evaluation of a service is promoted by some of its QoS history records, rather than its advertised QoS values. Besides, the k-means algorithm is introduced into our method as a data filtering tool to select representative history records. As a result, HireSome-II can protect cloud privacy, as a cloud is not required to unveil all its transaction records. Furthermore, it significantly reduces the time complexity of developing a cross-cloud service composition plan as only representative ones are recruited, which is demanded for big data processing. Simulation and analytical results demonstrate the validity of our method compared to a benchmark.

With the development of services computing and cloud computing, number of Web services has increased rapidly, and it becomes quite popular for developers to combine different Web services to build innovative Mash up applications. How to quickly locate desired Web service for developers, however, is still a challenging problem that needs to be addressed. Most existing related work employed keyword-based method to search services and focused on matching users' queries with semantic or syntactic Web service description. They seldom took advantage of relationships between services to improve the performance of service searching. This paper presents a correlation search method by making use of several relationships between Web services, to recommend a user with services that are similar, composable or potentially composable to a target service. One important advantage of this method is that it can guide users to find desired services promptly, and thus improves efficiency of the service discovery process. To mine the different relationships between services, several efficient algorithms are presented. Case studies and experiments show, the above correlation search method not only can recommend Web services to users that are relevant to the users' interest, but also can predict composable relationships between services with high performance.

Recommending trusted services to users is of paramount value in service-oriented environments. Reputation has been widely used to measure the trustworthiness of services, and various reputation models for service recommendation have been proposed. Reputation is basically a global trust score obtained by aggregating trust from a community of users, which could be conflicting with an individual's personal opinion on the service. Evaluating a service's trustworthiness locally based on the evaluating user's own or his/her friends' experiences is sometimes more accurate. However, local trust assessment may fail to work when no trust path from an evaluating user to a target service exists. This paper proposes a hybrid trust-aware service recommendation method for service-oriented environment with social networks via combining global trust and local trust evaluation. A global trust metric and a local trust metric are firstly presented, and then a strategy for combining them to predict the final trust of service is proposed. To evaluate the proposed method's performance, we conducted several simulations based on a synthesized dataset. The simulation results show that our proposed method outperforms the other methods in service recommendation.

Information retrieval (IR) systems exploit relevant information when tailoring search results to individual information needs. However, current search experience becomes poor without considering similar queries entered by previous searchers. In the following paper, we discuss a solution to this problem, which combines collaborative filtering algorithms with traditional IR models to enable EIR. We also present various iterative refinement methods for improving the raw performance of this system. We validate our theories in an experiment using queries extracted from the click-through log of a commercial search engine. According to our results, an IR system employing iteratively refined, collaborative retrieval significantly outperforms various baseline retrieval models.

With the rapid development of Web2.0 and its related technologies, Mashup services (i.e., Web applications created by combining two or more Web APIs) are becoming a hot research topic. The explosion of Mashup services, especially the functionally similar or equivalent services, however, make services discovery more difficult than ever. In this paper, we present an approach to recommend Mashup services to users based on user interest and social network of services. This approach firstly extracts users' interests from their Mashup service usage history and builds a social network based on social relationships information among Mashup services, Web APIs and their tags. The approach then leverages the target user's interest and the social network to perform Mashup service recommendation. Large-scale experiments based on a real-world Mashup service dataset show that our proposed approach can effectively recommend Mashup services to users with excellent performance. Moreover, a Mashup service recommendation prototype system is developed.

Perception of trustworthiness of service providers is a fundamental need in service selection. Trust propagation has been used to predict trustworthiness of service providers in service-oriented social networks. However, existing trust propagation methods may suffer from a scalability problem, i.e., their computation time is likely too high to be acceptable in practice, especially when they are applied to very large-scale service-oriented social networks. Moreover, they rarely consider the structural properties of social networks to optimize their performance. This paper proposes an efficient trust propagation scheme for predicting trust in service-oriented social networks. It exploits the specific structural properties of social networks and builds an advanced data structure from preprocessing to improve the efficiency of trust propagation. Our scheme can support multiple trust propagation strategies. Experiments show that our scheme is much more efficient than well-known trust propagation methods in trust prediction, while its trust prediction results are as accurate as theirs in service-oriented social networks.

This special issue of Concurrency and Computation: Practice and Experience contains selected highquality papers from the 2011 International Conference on Cloud and Green Computing (CGC2011) which was held on December 11-13, 2011 in Sydney, Australia [1].The CGC conference series aims to provide an international forum for the presentation and discussion of research and development trends regarding cloud and green computing.CGC2011 attracted many international attendants, allowing deep discussion and the exchange of ideas and results related to ongoing research among attendants.Many research and development efforts have been made in the field of cloud and green computing such as [2][3][4][5][6][7][8][9][10][11].More and more people from different areas are trying to facilitate the techniques from their respective areas to tackle tough issues in cloud and green computing such as resource scheduling, security and privacy, service provision, power aware computation and storage, and data service query issues.This special issue aims to accommodate a range of papers from different perspectives and areas to provide some different views and hints for cloud and green computing research.This special issue contains eight papers based on those that were presented at CGC2011.They are listed as [12][13][14][15][16][17][18][19].Research problems in these papers have been analyzed systematically, and for specific approaches or models, evaluation has been performed to demonstrate their feasibility and advantages.The papers were selected on this basis and also peer reviewed thoroughly.They are summarized in the succeeding texts.Paper [12] develops an adaptive service selection method for cross-cloud service composition.It can dynamically select proper services with near-optimal performance for adapting to changes in time.A case study is presented to demonstrate the performance.Paper [13] attempts to identify the role of contextual properties of enterprise systems architecture in relation to service migration to cloud computing.It points out that cloud computing requires consumers to relinquish their ownership of and control over most architectural elements to cloud providers.The simulation is conducted to evaluate the feasibility of the proposed method.Paper [14] proposes an economic and energy aware cloud cost model in this regard.The model supports the decision-making process to be applied with business cases and enables cloud consumers and cloud providers to define their own business strategies and to analyze the respective impact on their business.Paper [15] focuses on latency in global cloud service provision.This paper investigates if latency in terms of simple ping measurements can be used as an indicator for other QoS parameters such as jitter and throughput.Corresponding experiments are conducted to demonstrate performance.Paper [16] presents a number of policies that can be applied to multiuse clusters where computers are shared between interactive users and high throughput computing.The paper also evaluates policies by trace-driven simulations to determine the effects on power consumed by the high throughput workload and impact on high throughput users.The experiment results demonstrate significant power saving with proposed policies.Paper [17] designs an efficient data and task co-scheduling strategy for scheduling datasets and tasks together.Simulation was conducted on the well-known Tianhe supercomputer platform.Simulation results demonstrate that the proposed strategy can effectively improve workflows performance while reducing the total volume of data transfer across data centers.

SUMMARY The employment of batch processing in workflow is to model and schedule activity instances in multiple workflow cases of the same workflow type to optimize business processes execution dynamically. Although our previous works have preliminarily investigated its model and implementation, it is still necessary to deal with its model design problem. Process mining techniques allow for the automated discovery of process models from event logs and have received notable attentions in researches recently. Following these researches, this paper proposes an approach to mine batch processing workflow models from event logs by considering the batch processing relations among activity instances in multiple workflow cases. The notion of batch processing feature and its corresponding mining algorithm are also presented for discovering the batch processing area in the model by using the input and output data information of activity instances in events. The algorithms presented in this paper can help to enhance the applicability of existing process mining approaches and broaden the process mining spectrum. Copyright © 2013 John Wiley & Sons, Ltd.

Web services are very prevalent nowadays. Recommending Web services that users are interested in becomes an interesting and challenging research problem. In this paper, we present AWSR (Active Web Service Recommendation), an effective Web service recommendation system based on users' usage history to actively recommend Web services to users. AWSR extracts user's functional interests and QoS preferences from his/her usage history. Similarity between user's functional interests and a candidate Web service is calculated first. A hybrid new metric of similarity is developed to combine functional similarity measurement and nonfunctional similarity measurement based on comprehensive QoS of Web services. The AWSR ranks publicly available Web services based on values of the hybrid metric of similarity, so that a Top-K Web service recommendation list is created for a user. AWSR has been implemented and deployed on the Web. By conducting large-scale experiments based on a real-world Web services dataset, it is shown that our system effectively recommends Web services based on users functional interests and non-functional requirements with excellent performance.

Collaborative filtering is one of widely used Web service recommendation techniques. In QoS-based Web service recommendation, predicting missing QoS values of services is often required. There have been several methods of Web service recommendation based on collaborative filtering, but seldom have they considered locations of both users and services in predicting QoS values of Web services. Actually, locations of users or services do have remarkable impacts on values of QoS factors, such as response time, throughput, and reliability. In this paper, we propose a method of location-aware collaborative filtering to recommend Web services to users by incorporating locations of both users and services. Different from existing user-based collaborative filtering for finding similar users for a target user, instead of searching entire set of users, we concentrate on users physically near to the target user. Similarly, we also modify existing service similarity measurement of collaborative filtering by employing service location information. After finding similar users and services, we use the similarity measurement to predict missing QoS values based on a hybrid collaborative filtering technique. Web service candidates with the top QoS values are recommended to users. To validate our method, we conduct series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that the location-aware method improves performance of recommendation significantly.

Trustworthiness plays an important role in service selection and usage. However, it is not easy to define and compute the service trustworthiness because of its subject meaning and also the different views on it. In this paper, we describe the meaning of trustworthiness and the computation method for trustworthiness fusion. Through extracting trustworthiness from service provider, service requestor and service broker, we creatively adopted D-S (Dempster-Shafer) evident theory to fuse the tripartite trustworthiness. Finally, we completed some comparison experiments on our web service supermarket platform and certified the efficiency of our method.

Users usually have different prospective even they input a same keyword to search Web services. It is a challenge to personalize web service search engine as more and more keyword-like Web services becoming available on Internet. User interest plays an important role in personalizing search result. Therefore, through interest extraction, Web service search engine is personalized. At last, an experiment is presented to demonstrate the feasibility of the method.

For services that have similar functionalities, if they are published by different cloud platforms, it is a challenge to evaluate them, for satisfying different end users' personal preferences. In view of this challenge, a method for web service ranking, named WSRank, is investigated in cloud environment in this paper. It aims at ranking different Web services published by different cloud platforms, taking advantage of Page Rank principle. At last, a case study and experiment are presented to demonstrate the feasibility of the method.

Roving patrol plays a very important role in strengthening safety within campuses or communities through their patrol around the target. In fact, there maybe exist some patrolmen who do not work efficiently. Therefore, it is useful to quantitatively evaluate their roving effectiveness. The widespread use of global positioning system (GPS) and GPS data logger have generated huge amount of trajectory data. It is a good way to evaluate the effectiveness. In this paper, we present the roving patrol effectiveness evaluation (RPEE) model by GPS Trajectory. Firstly, GPS data is pre-processed by redundancy reducing, trajectory segmentation and abnormality filtering. And then, evaluation index system is established based on the indicators extracted from trajectories by using statistical analysis. Finally, RPEE is conducted by using fuzzy comprehensive evaluation method. In this procedure, the weight distribution for indicators is determined by analytic hierarchy process. We evaluate the model by using the GPS data collected at 11 distinct patrol areas over a period of six months in the real world. The experimental results show that: indicators, such as the total effective patrol time, the phenomenon of staying, and the roving patrols at each checkpoint, etc., can be obtained objectively for RPEE.

Activity instance oriented handling is a new means for vertical optimization of process cases. Unlike our previous batch processing mechanism in workflows, it focuses on the data characteristics of activity instances and utilizes explicit knowledge for execution optimization. This paper introduces its concept and investigates its modeling and enactment mechanisms. It uses activity instance pattern as a base to model and represent the knowledge for execution optimization. The system design for activity instance oriented handling and related algorithms are also proposed.

Collaborative filtering is one of widely used Web service recommendation techniques. There have been several methods of Web service selection and recommendation based on collaborative filtering, but seldom have they considered personalized influence of users and services. In this paper, we present an effective personalized collaborative filtering method for Web service recommendation. A key component of Web service recommendation techniques is computation of similarity measurement of Web services. Different from the Pearson Correlation Coefficient (PCC) similarity measurement, we take into account the personalized influence of services when computing similarity measurement between users and personalized influence of services. Based on the similarity measurement model of Web services, we develop an effective Personalized Hybrid Collaborative Filtering (PHCF) technique by integrating personalized user-based algorithm and personalized item-based algorithm. We conduct series of experiments based on real Web service QoS dataset WSRec [11] which contains more than 1.5 millions test results of 150 service users in different countries on 100 publicly available Web services located all over the world. Experimental results show that the method improves accuracy of recommendation of Web services significantly.

Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the user's own discretion. The selection does not consider multiple requests for the same functional web services. Usually, there are multiple service requests for the same functional web service in practice. In such situations, conflicts occur when too many requesters select the same best web service. This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. It uses Euclidean distance with weights to measure degree of matching of services based on QoS. A 0-1 integral programming model for maximizing the sum of matching degree is created and consequently, a global optimal service selection algorithm is developed. The model, together with a universal and feasible optimal service selection algorithm, is implemented for global optimal service selection for multiple requesters (GOSSMR). Furthermore, to enhance its efficiency, Skyline GOSSMR is proposed. Time complexity of the algorithms is analyzed. We evaluate performance of the algorithms and the system through simulations. The simulation results demonstrate that they are more effective than existing ones.

Abstract Batch processing in workflow schedules activity instances in multiple workflow cases of the same workflow type to run as a group. It can optimize business processes execution dynamically. To achieve this goal, it is necessary to define a dataflow operation language to group and ungroup the data in multiple cases of a workflow. Though our previous work has preliminarily investigated the model and its implementation, there is still lack of a formally defined model. In this paper, we first propose a method that is based on a partial vector space to model the dataflow in multiple workflow cases. Based on this model, the data operation primitives for batch processing are specified and defined formally. Since most WfMSs (Workflow Management Systems) use RDBMS (relational database management system) to store their data currently, an SQL (Structured Query Language)‐like implementation language, namely DBOL (Data Batch Operation Language), is proposed. Evaluation experiments have also been done to show its performance. Copyright © 2011 John Wiley & Sons, Ltd.

The instance-aspect oriented workflow management system is to vertically combine multiple workflow activity instances and submit them for execution as a whole according to some batch or combination logics. It is inspired by the idea of aspect-oriented programming methodology and aims at improving the execution efficiency of business processes. Traditional workflow systems do not support workflow model with instance aspects. In our previous work, we have studied workflow instance modeling technology. This paper makes a research on the principles, methods and implementation of a workflow visual GUI tool for modeling instance aspects in workflow. It is based on an open source GUI tool, Together Workflow Editor, and makes some expansion in instance aspect functionality.

Abstract Multi‐granularity of time and time zone difference are two aspects of a time management model (TMM) for distributed workflow management system (DWfS). There are some recent literatures concerning each of them. However, current researches on DWfS have not put them together but have built up an integrated model. As business and scientific collaboration processes across continents become more and more frequent, it is increasingly important to build an integrated model to take them as a whole to make the time transformation and temporal verification in collaboration processes easy and efficient. Aiming at solving this problem, an integrated TMM, DWfS‐TMM, is proposed in this paper. The DWfS‐TMM model consists of a set of general time ontology and a set of general rules for unified transformation between different time zones and different time granularity units. The representation of build‐time and run‐time temporal constraints in workflow processes and the temporal consistency checking method in this model is investigated. Finally, a real case study is investigated and it is shown from this case study that the presented model is useful and practicable. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The time model for business cooperation across organizations and in business service grid has a special and important requirement, i.e. workday calendar model (WdCM). Workflow plays an important role in modelling business processes across multi‐enterprises or in service grid environments. However, current research in workflow time models does not pay sufficient attention to the differences in workday calendar between individual organizations. This paper aims to solve this problem by clarifying some basic concepts involved in the workday calendar models and presenting an XML‐based framework for workday calendar expressions, i.e. WdCM. Based on this WdCM, temporal constraints and the time optimization issue of workflow processes are investigated. Finally, a case study is analysed to demonstrate the feasibility of our workday calendar model. Copyright © 2007 John Wiley & Sons, Ltd.

UDDI registration center provides a set of management mechanism for Web services providers to publish their Web service and for Web service consumers to inquire what they needs. It solves the problem of Web service description, discovery and Integration. Web services graph is the semantic index established on the UDDI registration center according to the logical relations between web services [1,2]. It can enhance the Web service discovery efficiency. jUDDI is an open source Java implementation of UDDI specification for Web Services. Based on the research on UDDI and Web service graph, this article takes jUDDI as an example to show how the traditional UDDI registry center can be extended so as to supporting Web service graph.

From the view of distributed application, it has the two important points to build ontology: sharing domain knowledge, reuse of domain knowledge. There are some problems in the present distributed applications based on ontology: It may use different terms for a same concept in different systems and it does not facilitate the information interchange between heterogeneous systems. Also, it lacks of a rigorous formal logic description for verification. Considering the application in the Internet, in this paper, we present a uniform expression format based on XML/RDF to describe the ontology, and extend PSL by creating new PSL Extensions to define the ontology of location, organization, resources, activity, process and product. These provide the mobile agent application system formal description language that supports the verification of the system and the translation with other application systems.

In a workflow management system, two facts are often observed: (1) the processing capacities for activities may not be fully utilized all the time, even that for critical activities; and (2) the lifecycle of a task can be partitioned into two parts: preparation phase and actual execution phase. In order to shorten the lifecycle of a workflow process instance, it is better to overlap the actual execution of a task and the preparation of its successor as long as possible. Aiming at this goal, this paper introduces the concept of task pre-dispatching into workflow management systems and analyzes when and under what circumstances a task should be pre-dispatched. Then a formalized workflow model as well as a scheduling algorithm is presented to support it. Finally, it is shown from a quantitative analysis that our approach is useful in practice.

Using e-commerce over the Internet is very cheap and convenient, which enlarges the view of enterprises and lets the enterprises have more chances to select their partners. To achieve these goals, we need the support of information systems, in which workflow management system is of significance. The inherent characteristics of a workflow system make it suitable to implement the cross organization management. Nowadays, however, systems which support cross organization management are not common. When developing a supply chain management system for a big motorcycle corporation in China, we have to construct an inter-enterprises workflow Architecture over Internet for the system by ourselves. The main part of the Architecture is a workflow supported inner supply chain and an integrated interface, through which the whole supply chain system cross several independent enterprises is constructed and the information exchange is fulfilled. This paper introduces how the system is designed and implemented in detail.