Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data quality and data management tasks, including fact checking on social media networks and entity resolution. In this paper, we study the discovery problem of GEDs—finding a minimal cover of valid GEDs in a given graph data. We formalise the problem, and propose an effective and efficient approach to overcome major bottlenecks in GED discovery. In particular, we leverage existing graph partitioning algorithms to enable fast GED-scope discovery, and employ effective pruning strategies over the prohibitively large space of candidate dependencies. Furthermore, we define an interestingness measure for GEDs based on the minimum description length principle, to score and rank the mined cover set of GEDs. Finally, we demonstrate the scalability and effectiveness of our GED discovery approach through extensive experiments on real-world benchmark graph data sets; and present the usefulness of the discovered rules in different downstream data quality management applications.

Linked Open Data (LOD) is an ongoing effort in the Semantic Web community to build a massive public knowledge graph. The goal is to extend the Web by publishing various open datasets as RDF on the Web and then linking data items to other useful information from different data sources. With linked data, starting from a certain point in the graph, a person or machine can explore the graph to find other related data. In this paper, we develop a novel pipeline for graph-based biological data integration. By using our pipeline, users can easily glue heterogeneous biological ontologies, annotate sources with multiple join tables effectively, obtain a high-quality biological knowledge graph automatically, and enrich the knowledge graph with public biological ontologies finally. We implement a platform that realizes the proposed approach and conduct two case studies to evaluate the effectiveness and efficiency of our approach.

Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is an approach that is inflexible, incurs high costs, and leads to “silos” that prevent sharing data across different agencies or tasks. A standard approach to tackling this problem is to design a common ontology and to construct source descriptions which specify mappings between the sources and the ontology. Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Automatic semantic model has been gaining attention in data integration [5], federated data query [14] and knowledge graph construction [6]. This paper proposes an service-oriented architecture to create a correct semantic model, including annotating training data, training the machine learning model, and predict an accurate semantic model for new data source. Moreover, a holistic process for automatic semantic modeling is presented. By the usage of ASMaaS, historical semantic annotations for training machine learning model used in automatic semantic modeling can be shared, reducing costs of human resources from users. By specifying a well defined interface, users are able to have access to automatic semantic modeling process at any time, from anywhere. In addition, users must not be concerned with machine learning technologies and pipeline used in automatic semantic modeling, focusing mainly on the business itself.

Next (or successive) point-of-interest (POI) recommendation, which aims to predict where users are likely to go next, has recently emerged as a new research focus of POI recommendation. Most of the previous studies on next POI recommendation attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user's next move. However, few of the next POI recommendation approaches utilized the social influence of each user's friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. We also carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely, Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is highly efficient among six neural-network-based methods, four of which utilize the attention mechanism.

Summary Web service recommendation becomes an increasingly important issue when more and more services are published on the Internet. Many Web service recommendation methods have been proposed in recent years, most of which adopted collaborative filtering (CF) techniques. In general, these approaches have two limitations. Firstly, they rarely leverage user ratings since this kind of explicit feedback is difficult to collect for Web services. Secondly, the new user cold‐start problem is an inherent limitation of CF because the new users have not yet cast sufficient numbers of votes. In this paper, pseudo ratings of services constructed based on plenty of user‐service interactions, also known as a kind of implicit feedback, are provided to represent users' preferences on services. Based on these pseudo ratings, we present a novel Web service recommendation approach, which can alleviate the cold‐start problem by integrating contextual information and an online learning model. Experiments conducted on a real world data set show that, compared with the method without contextual information, our proposed approach that handles the cold start problem by integrating contextual information can achieve better F‐Measure performance (5.08 times increase on average). Moreover, the proposed online recommendation approach can dramatically decrease the time overhead while keeping the similar recommendation performance.

“Internet plus” application service recommendation is challenged by two issues: One is the increase in service volume and the disorderliness of the service organizations. A second is the diversification of user requirements. The research focus of this study was to investigate how to achieve more or dered aggregation and recommend services that meet the individualized requirements of users. This paper addresses the disorderliness of conventional service aggregation and considers the aggregation requirements of QoS weights with non-functional targets. Based on semantic relevance using the role (R), goal (G), process (P), service (S) demand metamodel, an RGPS association is proposed that is a weighted network for ordered QoS service aggregation. An individualized service recommendation method then is provided, based on an LSTM neural network with role and target backstepping using RGPS association network, that can achieve a high-quality precision service. Finally, a simulation experiment was carried out on service recommendations in the tourism domain, which verified the precision, effectiveness and application value of the service recommendation method.

With the broad adoption of service-oriented architecture, many software systems have been developed by composing loosely-coupled Web services. Service discovery, a critical step of building service-based systems (SBSs), aims to find a set of candidate services for each functional task to be performed by an SBS. The keyword-based search technology adopted by existing service registries is insufficient to retrieve semantically similar services for queries. Although many semantics-aware service discovery approaches have been proposed, they are hard to apply in practice due to the difficulties in ontology construction and semantic annotation. This paper aims to help service requesters (e.g., SBS designers) obtain relevant services accurately with a keyword query by exploiting domain knowledge about service functionalities (i.e., service goals) mined from textual descriptions of services. We firstly extract service goals from services’ textual descriptions using an NLP-based method and cluster service goals by measuring their semantic similarities. A query expansion approach is then proposed to help service requesters refine initial queries by recommending similar service goals. Finally, we develop a hybrid service discovery approach by integrating goal-based matching with two practical approaches: keyword-based and topic model-based. Experiments conducted on a real-world dataset show the effectiveness of our approach.

The papers in this special issue are designed to solicit innovative and promising methods and techniques related closely to cloud services in the era of Big Data. The concept of Cloud Service represents a prime facility and feature of services in a cloud computing environment that can be made available to users on demand. Due to the flexibility of cloud computing in scaling IT resources up and down, cloud services gradually become valuable to attract the gaze of researchers and engineers from both academia and industry when they are faced with dynamically changing business requirements. Different stakeholders, such as consumers, providers, and operators, are generating a vast amount of data on such services per minute on the Internet, which increasingly comes to show the “4V” characteristics of big data. Therefore, new methodologies and techniques are urgently required for designing, validating, developing, testing, and deploying cloud services on demand in this specific scenario based on big data, as well as for efficiently being adaptive to business dynamics and users’ explicit and implicit requirements.

This is the second part of a special issue on "Cloud Services Meet Big Data" organized to solicit innovative and promising methods and techniques related closely to cloud services in the era of Big Data. The seven papers included in this special investigate the most challenging issues in the areas of IaaS (Infrastructure as a Service) design and operations, requirements engineering and knowledge engineering for cloud services development, and service search and discovery.

In a business cloud environment, a reference business process model needs to be customized in order to meet the individualized requirements of each organization. Consequently, a reference process model is generally evolved to a couple of process variants, known as a process family. Currently, there exist some approaches and tools that can efficiently configure a reference process model. However, a key issue is how to manage co-evolution appropriately within a family of process models if the base process model of a process family is changed. Contemporary process management tools do not adequately support the management of such co-evolution. Each process variant in the process family has to be changed as a separate process model which often leads to redundancies and inefficiencies. In this article, we propose a novel approach for managing co-evolution of process families based on an aspect-oriented approach. Change options on base process model are abstracted and extracted as a pluggable component, which can be selectively reused for all members, and consequently, guides the co-evolution for the whole process family. In particular, the control-flow and data-flow relations between process extension and a member of process families can be built by leveraging process extensibility patterns. The correctness of our extension approach is proved based on graph theory. The whole approach in this article have been implemented as a working open-source prototype and tested against a real case study from the city logistic distribution domain as well as real data set from SAP reference models.

In the era of service-oriented software engineering (SOSE), service clustering is used to organize Web services, and it can help to enhance the efficiency and accuracy of service discovery. In order to improve the efficiency and accuracy of service clustering, this paper uses the self-join operation in relational database (RDB) to realize Web service clustering. Based on storing service information, it does the self-join operation towards the Input, Output, Precondition, Effect (IOPE) tables of Web services, which can enhance the efficiency of computing services similarity. The semantic reasoning relationship between concepts and the concept status path are used to do the calculation, which can improve the calculation accuracy. Finally, we use experiments to validate the effectiveness of the proposed methods.

With the rapid development of SOA (Service Oriented Architecture), an increasing number of Web services have been published on the Internet. How to recommend suitable Web services to users becomes a challenging problem. Existing Web services recommendation approaches based on collaborative filtering mainly focus on QoS (Quality of Service) prediction. Recommending services based on users' ratings on services are seldom reported since such explicit feedback data is difficult to collect. In this paper, we report a dataset of implicit feedback on real-world Web services, which consist of more than 280,000 user-service interaction records, 65,000 service users and 15,000 Web services or mashups. In addition, time is becoming an increasingly important factor in recommenders since time effects influence users' preferences to a large extent. Based on the collected dataset, we propose a time-aware service recommendation approach. Temporal information is sufficiently considered in our approach, where three time effects are analyzed and modeled including user bias shifting, Web service bias shifting, and user preference shifting. Experimental results show that the proposed approach outperforms seven existing collaborative filtering approaches on the prediction accuracy.

With the increasing number of Web services available on the Internet, how to recommend Web services to interested users effectively and efficiently remains to be a big challenge. At present, collaborative filtering (CF) is the most widely used technique in the design of recommender systems to handle information overload. For Web services, however, it is difficult for user to collect personalized QoS (Quality of Service)data and other explicit feedbacks such as ratings. In most cases, only a part of the implicit feedbacks (e.g., watchlist) is available in service registry. In this paper, we leverage implicit feedback from user's watchlist to build a CF-based recommender system for Web service. Our main contribution is to transform implicit feedbacks into explicit ratings to improve the accuracy of service recommendation. More specifically, we first construct binary user-service rating matrix according to the implicit feedback from the watchlist. Then, temporal and tag preference are combined into the original rating matrix to generate a more accurate pseudo rating matrix, which can reflect users' different preference on services in their own watchlists. Finally, we use traditional user-based CF method to produce a personalized service recommendation list with corresponding pseudo ratings. Moreover, the empirical experiments based on ProgrammableWeb show that compared with traditional log-based CF method, the recommender system with temporal and tag preference is more accurate and precise.

This paper describes the concept of workflow merge and methods for merging business processes. We append effect annotations to activities of business process, use RPSTs divided the process graph to fragments then accumulate these effects according to these SESE fragments, detect exact clone and approximate clone between the two process models, finally design a merging algorithm to consolidate two processes. It is shown that to avoid invalid merges, one choose merge unit is SESE fragments, we also raise issues of more complex merge problems, such as semantic annotations.

As one foundational technology of cloud computing, services computing is playing a critical role to enable provisioning of software as a service (SaaS). However, how to effectively and efficiently discover proper available services from the cloud of resources remains a big challenge. This paper reports our continuous efforts on semantic services discovery. We extend the Support Vector Machine (SVM)-based text clustering technique in the context of service-oriented categorization in a service repository, and propose an iterative process to incrementally enrich domain ontology. A popular Web 2.0 mashup platform is used as a testbed; and preliminary evaluation results are reported.

Currently there exist many kinds of semantic Web Service models on the internet. They are heterogeneous so that it is hard to understand and interoperate each other. In this paper, we study several mainstream semantic Web Service models, and extract meta-models for each from the perspective of semantic Web Service discovery. Based on this, we propose the universal meta-model for semantic Web Service registration within the background of meta-model framework for interoperability (MFI, ISO/IEC19763). Some cases are studied and infrastructure supporting our work is demonstrated. The work in the paper may be regarded as an extension to UDDI on service semantics facet, and has been proposed to ISO as ISO/IEC 19763 Part 7.

With the rapid development of service computing related technology, the number of application of service based system (SBS) in enterprise is increasing rapidly. Research on evolution of SBS is becoming more and more important. In this paper, we propose a taxonomy framework for evolution of SBS, which is illustrated from five perspectives: (a) motivations of SBS evolutionary changes (why), (b) stakeholders of SBS evolutionary changes (who), (c) locations of SBS evolutionary changes happening (where), (d) times of SBS evolutionary changes happening (when), (e) support mechanisms in the process of SBS evolutionary changes (how). Furthermore, propagation of evolutionary changes of SBS is analyzed in the paper.

With the development of web service applications, how to realize on-demand services selection according to user's personal requirements is a hot research work in modern times. WS-CDL (Web Services Choreography Description Language) is a W3C candidate recommendation for the description of peer-to-peer collaborations for the participants in web services interaction. However, the goal of the service choreography is lacking and we can't understand the information exchange from the whole point. This paper extends WS-CDL from the aspect of goal that services to implement, and this can lay the foundation of on-demand service selection. Finally, the feasibility of the proposed method is validated through a case study.

Cloud computing has been attracting much attention since its birth. How to cloudify software systems especially legacy applications in the cloud era is becoming increasingly important. Based on RGPS meta-model framework and International standards-ISO/IEC 19763, an architecture for cloudification of legacy applications is proposed, which consists of three parts: a Web portal, a SaaS service supermarket, and a SaaS application development platform. In this paper, we take an open source software as an example to illustrate the proposed approach. Based on the architecture and supporting techniques on software virtualization and multi-tenancy, we develop a prototype Cloud CRM to demonstrate the basic procedure for cloudification of legacy applications, as well as the feasibility of the proposed approach.

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).

Software has become a complex piece of work by the collective efforts of many. And it is often hard to predict what the final outcome will be. This transition poses new challenge to the software engineering (SE) community. By employing methods from the study of complex network, we investigate the object oriented (OO) software metrics from a different perspective. We incorporate the weighted methods per class (WMC) metric into our definition of the weighted OO software coupling network as the node weight. Empirical results from four open source OO software demonstrate power law distribution of weight and a clear correlation between the weight and the out degree. According to its definition, it suggests uneven distribution of function among classes and a close correlation between the functionality of a class and the number of classes it depending on. Further experiment shows similar distribution also exists between average LCOM and WMC as well as out degree. These discoveries will help uncover the underlying mechanisms of software evolution and will be useful for SE to cope with the emerged complexity in software as well as efficient test cases design

Complex information resources interoperability is becoming an important issue for information resources reuse and aggregation in Semantic Web services. Current registration standards are relatively weak in the description of complex logic relationship between information resources. This paper proposes a complex information resources registry method based on deeply semantic interoperability. The mothod builds complex information registry attribute model, defines complex information registry attribute reference, local and application ontologies and evolution rules of them in the model. The method builds a complex information registry knowledge base on the OWL commitment for ontology and its reasoning capability. At last, we take software component (a type of complex information resources) as an example to realize a type of complex information resources registry based on ontology. The preliminary experimental results show that above method is quite feasible for solving problems with real world sizes.

Software drift is a common phenomenon in software development processes, which may lead to process deviation and then affect software quality. Effectively controlling negative software drifts is the key to achieving acceptable, predictable, and dependable software evolution in the model-driven development. In this paper we put forward a general taxonomy to identify different drifts in development processes according to their effects on software development. Based on the taxonomy, categories of process drift and quality drift are detailedly introduced. Moreover, we propose an integration framework for different drifts to realize the integration between development process and product quality. Eventually, a case study from practical project development is shown to prove the validity of our identification framework.

How can we effectively measure the complexity of a modern complex software system has been a challenge for software engineers. Complex networks as a branch of complexity science are recently studied across many fields of science, and many large-scale software systems are proved to represent an important class of artificial complex networks. So, we introduce the relevant theories and methods of complex networks to analyze the topological/structural complexity of software systems, which is the key to measuring software complexity. Primarily, basic concepts, operational definitions, and measurement units of all parameters involved are presented respectively. Then, we propose a qualitative measure based on the structure entropy that measures the amount of uncertainty of the structural information, and on the linking weight that measures the influences of interactions or relationships between components of software systems on their overall topologies/structures. Eventually, some examples are used to demonstrate the feasibility and effectiveness of our method.

Web application modeling and implementation method are related with middleware platform tightly, and its models usually can’t be reused on different platform. In order to reuse web application models, it is necessary to raise the level of abstraction of models, constructing middleware platform independent models. Roles are meant to capture observable behavioral aspects of objects. Role models describe the set of valid object collaboration tasks. This paper proposes a method for constructing platform independent models for Web applications. It consists of a stepwise process, a template role model and a mapping from role model to class model. The method can improve the reusability of models at different level of abstraction. The paper shows how the method works by a simple example

In this paper, we first discuss Web software engineering and its biggest problem. Reusing previously developed frameworks and patterns is a good way to resolve this problem. Then we introduce the structure of Web software engineering and Struts framework. Lastly, we discuss some patterns that we get from order-management systems based on Struts framework.

Pattern language has long been paid attention for its intrinsic characters such as its domain-orientation, problem-orientation and solution-orientation. Much of domain knowledge has been captured in well established pattern language. This paper, based on an investigation and study on patterns and relation between them, proposes a pattern relation model, to formalize the semantic structure of a pattern language. We also utilize this model to facilitate the development of a framework, to show a concrete example of this useful model.