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 Mobile application recommendation has emerged as a pivotal domain within the realm of personalized recommendation systems. Traditional mobile application sequence recommendation approaches are predominantly dedicated to the pursuit of sophisticated sequence encoders to achieve more precise representations. However, existing sequence recommendation methods primarily consider the sequential order of historical App interactions, overlooking the time intervals between applications. This oversight hinders the model's capability to fully unearth the temporal correlations in user behavior, consequently limiting the accuracy and personalization of mobile application recommendations. Moreover, the interactions between users and mobile applications are typically sparse, which weakens the model's generalization capabilities. To address these issues, we propose a novel method for mobile application sequence recommendation, incorporating time interval‐aware attention and contrastive learning (called Ti‐CoRe). Specifically, this approach introduces a novel sequence augmentation strategy based on similarity replacement within a contrastive learning framework. By considering textual similarities between applications, this method selectively replaces applications that possess lower similarity scores to generate augmented sequences, increasing the diversity of the sample space and mitigating data sparsity. Furthermore, integrating a time interval‐aware mechanism into the BERT4Rec model, the paper presents a new T‐BERT encoder. It precisely assesses the influence of fluctuating time intervals on the prediction of the subsequent mobile application, thereby ensuring a more nuanced app representation. Experiments conducted on the 360APP real dataset demonstrate that Ti‐CoRe consistently outperforms various baseline models in terms of NDCG and HR 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.

Cloud computing optimizes service quality and resource efficiency via centralized hardware and computational resources. However, the predominantly centralized deployment and operation of cloud data centers increase the physical distance to end-users, leading to degraded service quality. Edge computing addresses this by offloading data processing and analysis tasks directly to devices at the network edge, reducing reliance on backhaul transmission and thus offering a more responsive solution for latency-sensitive applications. Nevertheless, ensuring that applications meet predefined Service Level Agreement (SLA) in resource-constrained edge environments remains challenging. To tackle these issues, this paper investigates elastic scaling strategies in cloud-edge collaborative settings. We propose an attention-enhanced bidirectional LSTM model (A-Bi-LSTM) for microservice workload prediction, and design an adaptive elastic scaling system named XScale. This system incorporates a fall-back scaling mechanism when predictions are unreliable and introduces a proactive load forwarding strategy to enhance overall edge node performance. Experimental results show that, compared to existing elastic scaling methods, XScale reduces SLA violations by 82.3%, increases average resource utilization by 17.4%, decreases average response time by 21.1%, and improves overall edge node performance by 36.3%.

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.

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 The advantages of federated learning in collaborative computing of deep learning make it a crucial approach for distributed architectures in recommender systems. However, existing federated recommender systems typically share unified item embeddings across all clients, which fails to capture user‐specific characteristics of items. How to adaptively retain both the commonality and individuality meanings of item embeddings in the recommendation becomes a critical challenge, while simultaneously preventing personalized information leakage. Therefore, this paper proposes a novel federated recommendation method (named 2P‐FedRec) that constructs a privacy‐preserving personalized recommender system in an adaptive manner. Specifically, this method employs an adaptive attention module to generate item representation containing global item embeddings (capturing cross‐user commonalities) and personalized embeddings (capturing user‐specific preferences), and utilizes two regularizers to guide the optimization of independence between these two embeddings. To protect user privacy, we also apply local differential privacy (LDP) with noise injection to the uploaded parameters, preventing the reconstruction of sensitive data. Extensive experiments on Epinions and Yelp datasets demonstrate that 2P‐FedRec outperforms the state‐of‐the‐art baselines while maintaining privacy.

ABSTRACT Microservice technology, as a flexible application architecture, has gained wide popularity in the field of Internet of Things (IoT). IoT applications are highly sensitive to latency, making it crucial to place microservices on appropriate edge servers in an edge computing environment. Failure to do so can significantly impact service quality and degrade user experience, posing a major challenge. Addressing the aforementioned issues, this paper proposes a multiobjective service deployment strategy for IoT devices based on reinforcement learning. The goal is to minimize service access delay for IoT devices and reduce the average energy consumption of edge servers in the context of mobile edge computing. To achieve this, we first establish a stochastic optimization model using the Markov decision process (MDP) framework to handle service deployment and resource allocation dynamically. This model captures key characteristics such as heterogeneity in edge server capabilities, dynamic geographic information of IoT devices, and uncertainty in microservice requests. To overcome challenges related to dimensionality, slow convergence, and the exploration–exploitation tradeoff in traditional reinforcement learning algorithms, we introduce deep reinforcement learning into the optimization of microservice deployment. Specifically, we propose the use of deep deterministic policy gradient (DDPG) to obtain a near‐optimal service deployment strategy without manual instructions. DDPG leverages the depth of the network to guide policy gradients and generate solutions that effectively balance exploration and exploitation. To evaluate the proposed approach, we implement the DPG‐MSP (DDPG‐based MicroService Placement) algorithm using real datasets and synthetic data. Comparative analysis with existing microservice deployment algorithms demonstrates the superiority of DDPG‐MSP in terms of performance, robustness, and scalability.

Optimizing multi-instance service composition and dynamic request routing has become a critical challenge in cloud-edge collaborative systems. Existing solutions struggle with effectively balancing performance, cost, and bandwidth constraints in dynamic and resource-constrained environments. In this work, we address these challenges by proposing the Removed Minimum Cost Flow (RMCF) algorithm, aiming to minimize average response time while considering constraints such as budget and bandwidth, making it well-suited for time-sensitive services in a cloud-edge collaborative service provision system. Simulations were conducted using real-world data from China Telecom’s base stations in Shanghai, and experiments in various scenarios were considered, including time-sensitive services and general application services, with key performance indicators such as time utility, cost-utility, request completion rate, and timeout rate. The experimental results demonstrate that RMCF achieves lower response times, superior performance, and improved cost-effectiveness compared to other baseline algorithms.

Unmanned aerial vehicles (UAVs)-assisted edge computing has emerged as an effective solution for providing contingency task offloading services when ground computing infrastructures are insufficient. However, UAVs face challenges in implementing efficient task offloading strategies due to their limited capabilities and the complexity of the privacy offloading problem. To address these challenges, this study constructs a digital twin (DT)-enabled UAV swarm-assisted secure computing model, which considers collaboration of devices, edges, and cloud resources. The model is designed to represent the three-tier computing environment as a DT virtual framework, allowing for the monitoring of network changes and the exploration of potential strategies. Furthermore, a joint optimization problem that considers time delay and energy consumption within encryption and decryption costs is formulated. To solve this problem, an entropy-enhanced proximal policy optimization-based multi-UAV assisted security-aware task offloading (EP-MUSTO) algorithm is proposed. In EP-MUSTO, the exploration capability is enhanced by utilizing an actor network with policy entropy, and the action cognition is improved through the parameterization of the hybrid action space. Experimental results demonstrate that compared with other advanced algorithms, EP-MUSTO achieves a reduction in security system costs and magnitude of convergence oscillations by at least 9.43% and 54.62%, respectively.

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.

In mobile edge computing (MEC) environment, effective microservices deployment significantly reduces vendor costs and minimizes application latency. However, existing literatures overlook the impact of dynamic characteristics such as the frequency of user requests and geographical location, and lack in-depth consideration of the types of microservices and their interaction frequencies. To address these issues, we propose TP-MDU, a novel two-stage deployment framework for microservices. This framework is designed to learn users’ dynamic behaviors and introduces, for the first time, a minimal deployment unit. Initially, TP-MDU generates minimal deployment units online, tailored to the types of microservices and their interaction frequencies. In the initial deployment phase, aiming for load balancing, it employs a simulated annealing algorithm to achieve a superior deployment plan. During the optimization scheduling phase, it utilizes reinforcement learning algorithms and introduces dynamic information and new optimization objectives. Previous deployment plans serve as the initial state for policy learning, thus facilitating more optimal deployment decisions. This paper evaluates the performance of TP-MDU using a real dataset from Australia’s EUA and some related synthetic data. The experimental results indicate that TP-MDU outperforms other representative algorithms in performance.

The latency of cloud-hosted composite applications increases due to extended transmission time from the centralized cloud to end-users, compromising service quality. Typical AI application scenarios like autonomous driving and smart cities demand low network latency. Edge computing addresses this by enabling data collection and analysis in nearby edge data centers, reducing user response time. However, in a complex edge-cloud computing environment, finding the optimal scaling scheme dynamically is crucial due to varying user response times and dynamic scaling costs near edge data centers. This paper proposes a predictive scaling method to adjust microservice container number based on user request fluctuations. Our prediction algorithm, a two-way GRU with an attention mechanism named A-Bi-GRU, aims to minimize scaling jitter. To achieve this, we introduce the concept of an observation window and employ a multi-objective optimization algorithm based on improved NSGA-II, named DP-GA, for microservice scaling across different locations within each window. The solution aims to minimize the average user response time and scaling costs, enabling intelligent dynamic scaling based on location awareness. Experimental results indicate that the proposed A-Bi-GRU forecasting algorithm achieves approximately a 30% improvement in prediction accuracy over traditional linear models such as LR and SVM, and about a 5–10% improvement compared to conventional recurrent neural networks like RNN and LSTM. Furthermore, the proposed DP-GA multi-objective optimization algorithm reduces average response time by roughly 80% and scaling cost by approximately 50%.

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.

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.

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.

In the era of the Internet of Things, the significant increase in data volume, time, and space complexity presents great challenges to workflow scheduling in resource-constrained clouds. This study proposes an efficient hybrid algorithm, denoted white shark optimization (WSO) algorithm with budget constraints (BC-WSO), designed to adhere to budget constraints. The primary objective of BC-WSO is to optimize the scheduling length and cost. This objective is achieved by employing a heuristic algorithm that utilizes the predicted makespan matrix (PMMS) alongside the WSO algorithm as its foundation. The PMMS can minimize the scheduling length of a workflow application and satisfy the task prioritization dependencies. BC-WSO incorporates PMMS into the population initialization phase to improve the accuracy of WSO and accelerate the convergence process. Extensive experiments in two real-world scientific workflow applications show that BC-WSO outperforms current state-of-the-art meta-heuristic algorithms in simultaneously optimizing scheduling length and cost.

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.

The concept of the sharing economy has gained broad recognition, and within this context, Sharing E-Bike Battery (SEB) have emerged as a focal point of societal interest. Despite the popularity, a notable discrepancy remains between user expectations regarding the remaining battery range of SEBs and the reality, leading to a pronounced inclination among users to find an available SEB during emergency situations. In response to this challenge, the integration of Artificial Intelligence of Things (AIoT) and battery-swap services has surfaced as a viable solution. In this paper, we propose a novel structural Transformer-based model, referred to as the SEB-Transformer, designed specifically for predicting the battery range of SEBs. The scenario is conceptualized as a dynamic heterogeneous graph that encapsulates the interactions between users and bicycles, providing a comprehensive framework for analysis. Furthermore, we incorporate the graph structure into the SEB-Transformer to facilitate the estimation of the remaining e-bike battery range, in conjunction with mean structural similarity, enhancing the prediction accuracy. By employing the predictions made by our model, we are able to dynamically adjust the optimal cycling routes for users in real-time, while also considering the strategic locations of charging stations, thereby optimizing the user experience. Empirically our results on real-world datasets demonstrate the superiority of our model against nine competitive baselines. These innovations, powered by AIoT, not only bridge the gap between user expectations and the physical limitations of battery range but also significantly improve the operational efficiency and sustainability of SEB services. Through these advancements, the shared electric bicycle ecosystem is evolving, making strides towards a more reliable, user-friendly, and sustainable mode of transportation.

Summary Web service recommendation as an emerging topic attracts increasing attention due to its important practical significance. As the number of available Web services continues to grow, users face the challenge of searching the most suitable services that meet their specific needs. Quality of service (QoS)‐based service recommendation becomes a popular approach to address this issue. However, existing QoS‐based service recommendation methods are inability to effectively capture valuable content and structural information from services. These methods often rely solely on low‐order explicit feature intersections in QoS information, do not fully utilize the high‐order implicit feature intersections, and ignore the rich semantic information existing in service descriptions and user preferences. To address this problem, this paper proposes a Web service recommendation method via combining knowledge distillation representation and DCNMIX quality prediction. This method combines content‐based and structure‐based service classification and service prediction based on multi‐dimensional service quality information. First, it builds a service relationship network using semantic features extracted from service descriptions. Second, it designs a graph neural network knowledge distillation framework. The teacher model extracts the knowledge of the graph neural network model, and the student model learns the structure‐based and feature‐based prior knowledge of the service relationship network. Then the student model is used to learn the knowledge of the teacher model, classify Web services, and obtain service representations. Finally, based on service representations and multi‐dimensional QoS information, it exploits the DCNMIX model to learn the explicit and implicit features intersections of Web services and obtain the prediction score and ranking of Web services. The experimental results on the ProgrammableWeb dataset show that the proposed method outperforms the state‐of‐the‐art baselines in terms of Recall, F1, Logloss, and AUC_ROC.

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.

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 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.

In the edge collaboration environment, the rapid development of smart terminal devices and the huge volume of service requests from smart terminal devices often lead to load imbalance and long communication delay of edge server. In this situation, it's becoming more and more important to efficiently deploy the edge servers to reduce service communication delay and balance the load of edge server, thus fully improving resource utilization of edge server and users' service experience. To this end, this paper proposes a co-deployment method for edge servers in edge collaboration environment by jointly optimizing communication delay and load balancing. It first clusters all communication base stations by the K-means algorithm to derive the most suitable area for edge server deployment. Then, on the premise of balancing the workload between edge servers and minimizing the communication delay between communication base stations and edge servers, the optimal deployment location of edge servers is solved iteratively by the particle swarm algorithm. Finally, validation experiments are conducted based on real data sets of Shanghai Telecom communication base stations, and the experimental results show that the overall performance of the proposed method is better compared with the typical methods such as Top-K, K-means, Random, and Genetic Algorithm.

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.

Food-oriented cross-modal retrieval aims to retrieve relevant recipes given food images or vice versa.The modality semantic gap between recipes and food images (text and image modalities) is the main challenge.Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenation only can capture the simple interactions rather than complex interactions between different recipe components.2) The image feature extraction based on convolutional neural networks only considers the local features and ignores the global features of an image, as well as the interactions between different extracted features.This paper proposes a novel method based on Latent Component Weight Factors and Global Information (LCWF-GI) to learn the robust recipe and image representations for food-oriented cross-modal retrieval.This proposed method integrates the textual embeddings of different recipe components into a compact embedding to represent the recipes with the latent component-specific weight factors.A transformer encoder is utilised to capture the intra-modality interactions and the importance of different extracted image features for enhanced image representations.Finally, the bi-directional triplet loss is further used to perform retrieval learning.Experimental results on the Recipe 1M dataset show that our LCWF-GI method achieves competent improvements.

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.

In Mobile Edge Computing (MEC) environment, service deployment for IoT application is a key issue that needs to be solved. Considering the knowledge of mobile users’ service requests and edge server’s processing capacity, the problem of microservice deployment in MEC environment is modelled as a non-linear optimization problem. An adaptive dynamic deployment optimization method called Adapt-SD has been proposed, which is based on Adam and weighted round-robin scheduling algorithm to solve this microservice deployment problem. In Adapt-SD, considering the hardware resource-constrained MEC environment, different numbers of microservice instances are deployed on different edge servers, and then microservice instances are invoked to achieve the minimum resource consumption cost while meeting user’s service access delay constraints. At the same time, Adapt-SD also ensures the work balance of edge servers. In this paper, real datasets from EUA in Australia and some synthetic datasets are utilized to measure the performance of Adapt-SD, which is compared with the existing microservice deployment algorithms. Experimental results show that Adapt-SD is superior to other representative deployment algorithms.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This paper presents the first computer aided software analysis tool based on HOOMT (High Order Object-oriented Modeling Technique), which provides facilities for structured object-oriented analysis by integrating structured analysis and object-oriented analysis. It contains a graphical user interface for HOOMT-based modeling, supports modeling information management, and provides functionalities for developing three component sub-models in HOOMT: High Order Object Model (HOOM), Hierarchical Object Information Flow Model (HOIFM), and Hierarchical State Transition Model (HSTM). It supports structured decomposition of high-order objects, processes, and states. We describe its system architecture and implementation in this paper.

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.

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.

In this paper, atomic services are orchestrated by a business process in the context of service composition. To enhance the quality of service composition, this paper introduces a behavior-based approach, which may alter the structure of business process aiming to preserving behavior semantics of the composite service. The result of the experiment shows that the quality of composite service can be improved in terms of performance time via the proposed approach. This paper presents a preliminary behavior-based restructure approach for service compositions to improve Quality of Service (QoS).