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.

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.

The industrial Internet integrates industrial systems with Internet technologies, significantly enhancing production efficiency and reducing costs through collaboration with intelligent devices. Under the context of the industrial Internet, a production process typically comprises multiple tasks, each task requiring one or more types of production factors, which makes the service composition of production factors more complex and challenging compared to the traditional service composition under Web environments. To optimize the service composition of production factors under industrial Internet, this paper proposes a collaboration-aware service composition optimization model by considering the collaboration relationships among services. Specifically, both historical collaboration and present collaboration are considered, where the historical collaboration relationships among services include the collaboration between two directly-followed tasks and collaboration within one task, and the present collaboration relationships include the collaboration among the selected services in present service composition. To derive the optimal service composition plan, we propose using the advanced teaching-learning-based optimization algorithm (TLBO). Extensive experiments are conducted to compare with other population-based optimization methods under a real-world ship production process to verify the superiority of our approach. Experimental results show that the integration of collaboration relationships improves the overall quality of the optimized service composition plans in terms of time and cost, and the selected TLBO is more effective to derive the optimized service composition plans than other population-based optimization methods.

As Mashup technology advances, the number of Web APIs available continues to increase year by year. As a result, identifying and selecting suitable Web APIs from the wide array of options remains a challenging issue. To address this issue, interactive Web API recommendation has been introduced to simplify the process of Web API selection, assisting users and developers in invoking APIs to meet their business or software development needs. Although there have been some collaborative filtering-based methods for Web API recommendation, the representation of Mashups and APIs is not accurate enough due to the sparsity of Mashup-API interactions. As a result, their performance is often limited. To enhance the recommendation performance, we propose an interactive Web API recommendation approach based on composition-supervised LightGCL, named CS-LightGCL. CS-LightGCL derives Mashup and Web API embeddings by utilizing a simple yet effective graph contrastive learning paradigm LightGCL in which composition-supervised learning is designed and included to guide the embedding process simultaneously. Extensive experiments on a real-world dataset demonstrate that CS-LightGCL outperforms the baseline methods. The code is available at: https://github.com/IntelligentServiceLab/CS-LightGCL.

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

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

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

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

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

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

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

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

With the development of service computing technology, the number of publicly available Web APIs online has increased dramatically. Developers tend to use Web APIs to implement their software development requirements. However, due to the large number of Web APIs, how to select the appropriate Web API from the huge resource library for Mashup development has become a challenge. To solve this problem, some researchers have proposed Web API recommendation methods based on collaborative filtering or matrix factorization. However, the Web API recommendation performance is still limited due to the reliance on Mashup- API interaction and the ignorance of Mashup and Web API description documents. Moreover, the Mashup-API interaction matrix is extremely sparse, resulting in low accuracy in matrix factorization and collaborative filtering. To further improve the recommendation performance, we propose a novel joint matrix factorization method for the Mashup-API interaction matrix by incorporating the Mashup-Mashup similarity matrix and the API-API similarity matrix. A set of experiments are conducted on a real-world dataset, and the experimental results show that the proposed method outperforms the baselines.

With the wide use of Massive Open Online Courses (MOOCs) and online education, the large number and variety of educational resources on the online course open platform make it difficult for users to choose, and the requirements for course recommendation algorithms and model performance are getting higher and higher. The existing course recommendation algorithms often ignore the interactive information between n learners and courses and the course descriptions, resulting in poor recommendation performance. To solve the problems above, our work proposes an online course recommendation approach via exploring user interactions and course description documents. Specifically, LightGCN model is used to represent the user-course interaction graph to derive the structural embeddings of users and courses. Moreover, BERT model is used to represent the course description documents to derive the description embeddings of courses. Based on the three embeddings, ANFM (Attention Neural Factorization Machine) model is applied to perform online course recommendation by modeling low-order and high-order feature interactions, and learning the weights of the feature interactions through the attention mechanism. Finally, solid experiments are conducted over a real-world public dataset. Experimental results show that the proposed approach outperforms the baseline methods under NDCG, Recall and Precision.

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

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

The NIR-to-RGB spectral domain translation is a formidable task due to the inherent spectral mapping ambiguities within NIR inputs and RGB outputs. Thus, existing methods fail to reconcile the tension between maintaining texture detail fidelity and achieving diverse color variations. In this paper, we propose a Multi-scale HSV Color Feature Embedding Network (MCFNet) that decomposes the mapping process into three sub-tasks, including NIR texture maintenance, coarse geometry reconstruction, and RGB color prediction. Thus, we propose three critical modules for each corresponding sub-task: the Texture Preserving Block (TPB), the HSV Color Feature Embedding Module (HSV-CFEM), and the Geometry Reconstruction Module (GRM). These modules contribute to our MCFNet by methodically tackling spectral translation through a series of escalating resolutions, progressively enriching images with color and texture fidelity in a scale-coherent fashion. The proposed MCFNet demonstrates substantial performance gains over the NIR image colorization task. The code is available at: https://github.com/AlexYangxx/MCFNet.

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

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

With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline.

With the development of Mashup technique, the number of Web APIs released on the Web continues to grow year by year. However, it is a challenging issue to find and select the desirable Web APIs among the large amount of Web APIs. Consequently, interactive Web API recommendation is used to alleviate the difficulty of service selection, when users or developers try to invoke Web APIs for solving their business requirements or software development requirements. Currently, there are several collaborative filtering based approaches proposed for Web API recommendation, while their recommendation performance is limited on both optimality and scalability. This paper proposes a light neural graph collaborative filtering based Web API recommendation approach, named LNGCF. Specifically, LNGCF learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted summation of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train. A set of experiments are conducted on a real-world dataset. Experimental results demonstrate the substantial improvements on both optimality and scalability over the baselines.

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

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

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

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

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

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

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

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.

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 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.

Summary There are usually process model variants for a business as its application and evolution. Configurable process modeling is an effective approach for the design and development of business process models by reuse. However, the existing configurable process modeling approaches focus on traditional activity‐centric business processes, which is unfeasible for the new modeling paradigm of artifact‐centric business processes. To solve the problem, we propose a configurable modeling framework, especially for artifact‐centric business processes. To derive the integrated model for multiple process model variants, we propose a merger operation for artifact‐centric process model variants. We get a configurable artifact‐centric process model by identifying configurable points in the integrated model by common and variable characteristics. Moreover, the associated configuration alternatives are set accordingly for configurable points, in which the property of data privacy is considered. New artifact‐centric process models can be derived by configuration based on the behavior of a configurable model. To facilitate the process configuration, guidelines are analyzed based on the notion of process element relation graph. A case study is conducted to illustrate the effectiveness of our approach with a real‐world business process developed for Real Estate Administration (REA) in Hangzhou, China.

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

The 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.

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.

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.

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