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Over the last decade, service selection and recommendation had been two strongly related service filtering steps. While service selection aims to filter the best available services according to QoS and contextual criteria, service recommendation refines the selection results by taking into account additional criteria, such as users feedbacks and ratings, similarities between users tastes, etc. However, the ever changing services environment, users tastes, as well as the perception and popularity of available services, rise a question regarding the appropriate means to capture and analyze such changes over time. Most service recommendation solutions are static and do not offer a multi-relational modeling of user-service interactions over time. Time is a contextual dimension that has, recently, received a lot of attention, leading to a new class of recommender systems, called time-aware recommender systems. In this work, we propose a service recommendation method that takes advantage of temporal knowledge graphs. As a de facto standard to model multiple and complex interactions between heterogeneous entities, knowledge graphs will serve as a historical knowledge base for our TASR system. We, first, model the user-service interactions over time, by constructing a temporal service knowledge graph (TSKG) that will be later enriched through a completion step. Second, to explore the TSKG and extract top-rated services, we use Convolutional Neural Networks (CNN) to embed the TSKG into a low-dimensional vector space, facilitating then its mining. Experimental studies have proven the effectiveness and accuracy of our approach, compared to traditional TASR methods and time-unaware KG-based recommendation. [ABSTRACT FROM AUTHOR]

With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics. [ABSTRACT FROM AUTHOR]

Business process compliance ensures that the business processes of an organisation are designed and executed according to the rules that enforce the compliance controls that govern the company. We faced the challenge of building a Business Process Compliance Management System (BPCMS) for a process-aware organisation that had to provide support for several needs that, despite having been identified in the literature, were only partially satisfied by existing approaches. The variability in the types of rules and their interpretation generally restricts the existing support for compliance checking to specific types of rules (e.g., rules affecting the control flow of the process), a specific phase of the business process management (BPM) lifecycle (e.g., design time or run time), or certain information systems (ISs) for data retrieval (e.g., process event logs). Motivated by this, we designed a conceptual framework for design-time and run-time compliance checking that relies on the use of mashups for rule specification and checking. It presents the following advantages: (i) an open-ended set of types rules can be specified by designing and connecting mashup components; (ii) (parts of) the definitions of the rules can be reused as needed; and (iii) the mashup-based compliance checking (MCC) system can be integrated with ISs of the organisation, enabling the verification of actual facts on actions performed during the execution of a process (e.g., the existence of a specific document in a concrete location). Design-time and run-time implementations of the framework were conducted and tested in a real setting. [ABSTRACT FROM AUTHOR]

With the boom of Web services, there is a growing need for visualizing service ecosystems to help people browse services and understand their functionalities and positions in the systems. One foundational step of building a proper visualization is to ensure accurate representations for the comprising services. However, it is not a trivial task as service profiles may not be sufficient for two significant reasons. First, while the services themselves being used in various scenarios, their profiles may not always precisely reflect all of them. Second, service profiles usually comprise quite a few universal background terms that cannot distinguish services. To address these two issues, we apply machine learning techniques to incrementally learn service representations in a whole. A tailored topic model is developed, named Service Representation-Latent Dirichlet Allocation (SR-LDA). The core idea is to learn more comprehensive and up-to-date information about services from the profiles of the involved service compositions (i.e., mashup profiles), while introducing a global filter to identify and filter out background terms. Both quantitative and qualitative experiments on a real-world dataset demonstrate that the proposed SR-LDA builds higher-quality service representations comparing with baselines. We further generate a knowledge map to visualize a service ecosystem based on the learned service representations. Such a knowledge map directly leads to the detection of four typical functionality patterns of Web services and serves the purpose of mashup creation. [ABSTRACT FROM AUTHOR]

Driven by the widespread application of Service-Oriented Architecture (SOA), an increasing number of services and mashups have been developed and published onto the Internet in the past decades. With the number keeping on burgeoning, predicting the tendency of services invocation will provide various roles in service ecosystems with promising opportunities. However, services invocation bear three unique characteristics, which give rise to difficulties in predicting them. First, enormous services show different and complicated traits, like periodicity, nonlinearity and nonstationarity. Second, services providing similar or compensatory functions make up intricate relationship. Third, the combination dependencies between mashups and their comprising component services further amplify the difficulty. Given these factors, we have developed a tailored model Multi-Step Piecewise Recurrent Neural Network (MSP-RNN) to predict the tendency of services invocation. In MSP-RNN, Long Short Term Memory (LSTM) units are used to extract universal features. Based on these features, we have developed a piecewise regressive mechanism to make prediction discriminatingly. Besides, we have developed a multi-step prediction strategy to further enhance prediction accuracy and robustness. Extensive experiments in real-world data set with interpretable analysis show that MSP-RNN predicts the tendency of services invocation more accurately, i.e., by 3.7 percent in terms of symmetric mean absolute percentage error (SMAPE), than state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Mashups are a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs, which are generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for their composition tasks. In particular, we propose a probabilistic matrix factorization approach with implicit correlation regularization to solve the recommendation problem and enhance the recommendation diversity. We conjecture that the co-invocation of APIs in real-world mashups is driven by both the explicit textual similarity and implicit correlations of APIs such as the similarity or the complementary relationship of APIs. We develop a latent variable model to uncover the latent correlations between APIs by analyzing their co-invocation patterns. We further explore the relationships of topics/categories to the proposed approach. We demonstrate the effectiveness of our approach by conducting extensive experiments on a real dataset crawled from ProgrammableWeb. [ABSTRACT FROM AUTHOR]

With the growing popularity of web services, more and more developers are composing multiple services into mashups. Developers show an increasing interest in non-popular services (i.e., long-tail ones), however, there are very scarce studies trying to address the long-tail web service recommendation problem. The major challenges for recommending long-tail services accurately include severe sparsity of historical usage data and unsatisfactory quality of description content. In this paper, we propose to build a deep learning framework to address these challenges and perform accurate long-tail recommendations. To tackle the problem of unsatisfactory quality of description content, we use stacked denoising autoencoders (SDAE) to perform feature extraction. Additionally, we impose the usage records in hot services as a regularization of the encoding output of SDAE, to provide feedback to content extraction. To address the sparsity of historical usage data, we learn the patterns of developers’ preference instead of modeling individual services. Our experimental results on a real-world dataset demonstrate that, with such joint autoencoder based feature representation and content-usage learning framework, the proposed algorithm outperforms the state-of-the-art baselines significantly. [ABSTRACT FROM AUTHOR]

Service composition remains an important topic where recommendation is widely recognized as a core mechanism. Existing works on service recommendation typically examine either association rules from mashup-service usage records, or latent topics from service descriptions. This paper moves one step further, by studying latent topic models over service collaboration history. A concept of service co-occurrence topic is coined, equipped with a mechanism developed to construct service co-occurrence documents. The key idea is to treat each service as a document and its co-occurring services as the bag of words in that document. Four gauges are constructed to measure self-co-occurrence of a specific service. A theoretical approach, Service Co-occurrence LDA (SeCo-LDA), is developed to extract latent service co-occurrence topics, including representative services and words, temporal strength, and services' impact on topics. Such derived knowledge of topics will help to reveal the trend of service composition, understand collaboration behaviors among services and lead to better service recommendation. To verify the effectiveness and efficiency of our approach, experiments on a real-world data set were conducted. Compared with methods of Apriori, content matching based on service description, and LDA using mashup-service usage records, our experiments show that SeCo-LDA can recommend service composition more effectively, i.e., 5% better in terms of Mean Average Precision than baselines. [ABSTRACT FROM AUTHOR]

This article proposes an approach for identifying and recommending scientific workflows for reuse and repurposing. Specifically, a scientific workflow is represented as a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows. A graph-skeleton based clustering technique is adopted for grouping layer hierarchies into clusters. Barycenters in each cluster are identified, which refer to core workflows in this cluster, for facilitating cluster identification and workflow ranking and recommendation. Experimental evaluation shows that our technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows with respect to specific requirements of scientific experiments. [ABSTRACT FROM PUBLISHER]

A cloud mashup is composed of multiple services with shared datasets and integrated functionalities. For example, the elastic compute cloud (EC2) provided by Amazon Web Service (AWS), the authentication and authorization services provided by Facebook, and the Map service provided by Google can all be mashed up to deliver real-time, personalized driving route recommendation service. To discover qualified services and compose them with guaranteed quality of service (QoS), we propose an integrated skyline query processing method for building up cloud mashup applications. We use a similarity test to achieve optimal localized skyline. This mashup method scales well with the growing number of cloud sites involved in the mashup applications. Faster skyline selection, reduced composition time, dataset sharing, and resources integration assure the QoS over multiple clouds. We experiment with the quality of web service (QWS) benchmark over 10,000 web services along six QoS dimensions. By utilizing block-elimination, data-space partitioning, and service similarity pruning, the skyline process is shortened by three times, when compared with two state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

Though the existing data service mashup tools are gaining acceptance, it is still challenging for developers with no or little programming skills to develop data service mashups for dealing with situational and ad-hoc business problems. The paper focuses on interactive recommendation in which assistance is provided in a context-sensitive manner when the mashup plan can't be determined in advance. The paper analyzes the problem with a motivating scenario of mashup building for criminal investigation. Inspired by the observation that there exist dataflow patterns for certain integration functionalities, a dataflow-pattern-based recommendation framework is proposed to solve the problems. The framework can not only recommend data services by discovering similar situations, but also recommend mashup patterns and target data services. We propose a method to analyze the relationships between data services and dataflow patterns through both mining history logs and matching the input/output parameters. Further, to recommend target data services, we propose a method to transform the data mashup plans into mixed graphs and apply the graph-based substructure pattern mining (gSpan) algorithm on them. Experiments show that the dataflow-pattern-based recommendation approach for data service mashup is effective and efficient. [ABSTRACT FROM PUBLISHER]

Mashup has emeraged as a promising way to allow developers to compose existed APIs (services) to create new or value-added services. With the rapid increasing number of services published on the Internet, service recommendation for automatic mashup creation gains a lot of momentum. Since mashup inherently requires services with different functions, the recommendation result should contain services from various categories. However, most existing recommendation approaches only rank all candidate services in a single list, which has two deficiencies. First, ranking services without considering to which categories they belong may lead to meaningless service ranking and affect the recommendation accuracy. Second, mashup developers are not always clear about which service categories they need and services in which categories cooperate better for mashup creation. Without explicitly recommending which service categories are relevant for mashup creation, it remains difficult for mashup developers to select proper services in a mixed ranking list, which lower the user friendliness of recommendation. To overcome these deficiencies, a novel category-aware service clustering and distributed recommending method is proposed for automatic mashup creation. First, a Kmeans variant (vKmeans) method based on topic model Latent Dirichlet Allocation is introduced for enhancing service categorization and providing a basis for recommendation. Second, on top of vKmeans, a service category relevance ranking (SCRR ) model, which combines machine learning and collaborative filtering, is developed to decompose mashup requirements and explicitly predict relevant service categories. Finally, a category-aware distributed service recommendation (CDSR) model, which is based on a distributed machine learning framework, is developed for predicting service ranking order within each category. Experiments on a real-world dataset have proved that the proposed approach not only gains significant improvement at precision rate but also enhances the diversity of recommendation results. [ABSTRACT FROM PUBLISHER]

Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques, while others are based on QoS measurement. However, service ecosystem is evolving over time with services publishing, prospering and perishing. Few existing methods consider or exploit the evolution of service ecosystem on service recommendation. This paper employs a probabilistic approach to predict the popularity of services to enhance the recommendation performance. A method is presented that extracts service evolution patterns by exploiting latent dirichlet allocation (LDA) and time series prediction. A time-aware service recommendation framework is established for mashup creation that conducts joint analysis of temporal information, content description and historical mashup-service usage in an evolving service ecosystem. Experiments on a real-world service repository, ProgrammableWeb.com, show that the proposed approach leads to a higher precision than traditional collaborative filtering and content matching methods, by taking into account temporal information. [ABSTRACT FROM PUBLISHER]

Service computing promotes a large number of web-delivered services, including web services, APIs and data feeds. Composing data, functionalities and even UI from these web-delivered services into a single web application, usually called service mashup, becomes a popular web development paradigm. The web-delivered services can be modeled as mashup components, while the development of mashup actually yields a set of inter-connected mashup components. The growing popularity of mashup components enriches functionality and user experiences, while the possible connections among components are complex and difficult to mashup developers, who might be non-professional programmers or even end-users, as actions over one component may have potential impacts on another. This paper proposes a novel approach for recommending developers in terms of navigation and completion of mashup components with a large-scale components repository. From data-driven perspective, we model the relationships between mashup components by a generic layered-graph model. Developers are allowed to select some initial components as starting point, while a graph-based algorithm recommends how to navigate to potentially relevant mashup components and complete the relevant mashup application. We experimentally demonstrate the efficiency and effectiveness of our approach for rapid mashup construction. [ABSTRACT FROM PUBLISHER]

The convergence of Services Computing and Web 2.0 gains a large space of opportunities to compose “situational” web applications from web-delivered services. However, the large number of services and the complexity of composition constraints make manual composition difficult to application developers, who might be non-professional programmers or even end-users. This paper presents a systematic data-driven approach to assisting situational application development. We first propose a technique to extract useful information from multiple sources to abstract service capabilities with a set tags. This supports intuitive expression of user’s desired composition goals by simple queries, without having to know underlying technical details. A planning technique then exploits composition solutions which can constitute the desired goals, even with some potential new interesting composition opportunities. A browser-based tool facilitates visual and iterative refinement of composition solutions, to finally come up with the satisfying outputs. A series of experiments demonstrate the efficiency and effectiveness of our approach. [ABSTRACT FROM PUBLISHER]

Mashup is a web technology that allows different service providers to flexibly integrate their expertise and to deliver highly customizable services to their customers. Data mashup is a special type of mashup application that aims at integrating data from multiple data providers depending on the user's request. However, integrating data from multiple sources brings about three challenges: 1) Simply joining multiple private data sets together would reveal the sensitive information to the other data providers. 2) The integrated (mashup) data could potentially sharpen the identification of individuals and, therefore, reveal their person-specific sensitive information that was not available before the mashup. 3) The mashup data from multiple sources often contain many data attributes. When enforcing a traditional privacy model, such as K-anonymity, the high-dimensional data would suffer from the problem known as the curse of high dimensionality, resulting in useless data for further data analysis. In this paper, we study and resolve a privacy problem in a real-life mashup application for the online advertising industry in social networks, and propose a service-oriented architecture along with a privacy-preserving data mashup algorithm to address the aforementioned challenges. Experiments on real-life data suggest that our proposed architecture and algorithm is effective for simultaneously preserving both privacy and information utility on the mashup data. To the best of our knowledge, this is the first work that integrates high-dimensional data for mashup service. [ABSTRACT FROM PUBLISHER]

We propose a service-oriented approach to generate and manage mashups. The proposed approach is realized using the Mashup Services System (MSS), a novel platform to support users to create, use, and manage mashups with little or no programming effort. The proposed approach relieves users from programming-intensive, error-prone, and largely nonreusable output process for creating and maintaining mashups. We describe the overall design of MSS and discuss and evaluate its main enabling technologies. [ABSTRACT FROM PUBLISHER]