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The Computing Journal has published a special issue titled "ICSOC 2020 special issue on service-oriented computing." Out of the 23 research papers accepted at the 18th International Conference on Service-Oriented Computing (ICSOC 2020), two were selected for this special issue after a rigorous peer review process. The lead guest editor for this issue is Boualem Benatallah from Dublin City University, Ireland. Other guest editors include Hakim Hacid from the Technology Innovation Institute in Abu Dhabi, UAE, Eleana Kafeza from Zayed University in Dubai, UAE, Fabio Martinelli from the Institute of Informatics and Telematics in Pisa, Italy, and Athman Bouguettaya from the University of Sydney in Australia. Springer Nature, the publisher, maintains a neutral stance on jurisdictional claims and institutional affiliations. [Extracted from the article]

Understanding the business (interaction) protocol supported by a service is very important for both clients and service providers: It allows developers to know how to write clients that interact with a service, and it allows development tools and runtime middleware to deliver functionalities that simplifies the service development life cycle. It also greatly facilitates the monitoring, visualization, and aggregation of interaction data. This paper presents an approach for discovering protocol models from real-world service interaction logs. It presents a novel discovery algorithm, which is widely applicable, robust to different kinds of imperfections often present in real-world service logs, and able to derive protocols of small sizes targeted for human understanding. As inferring the most precise and concise model is not always possible from imperfect service logs using purely automated method, the paper presents a novel method for user-driven refinement of the discovered protocol models. The proposed approach has been implemented and experimental results show its viability on both synthetic and real-world data sets. Index Terms--Web services, business protocols, noise handling in service logs, protocol discovery, protocol refinement.

This paper discusses a framework in which catalog service communities are built, linked for interaction, and constantly monitored and adapted over time. A catalog service community (represented as a peer node in a peer-to-peer network) in our system can be viewed as domain specific data integration mediators representing the domain knowledge and the registry information. The query routing among communities is performed to identify a set of data sources that are relevant to answering a given query. The system monitors the interactions between the communities to discover patterns that may lead to restructuring of the network (e.g., irrelevant peers removed, new relationships created, etc.). Index Terms--Catalog portals, e-catalogs, peer-to-peer (P2P), self-adaptivity.

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]

Objectives: Text classification is a recurrent goal in machine learning projects and a typical task in crowdsourcing platforms. Hybrid approaches, leveraging crowdsourcing and machine learning, work better than either in isolation and help to reduce crowdsourcing costs. One way to mix crowd and machine efforts is to have algorithms highlight passages from texts and feed these to the crowd for classification. In this paper, we present a dataset to study text highlighting generation and its impact on document classification. Data description: The dataset was created through two series of experiments where we first asked workers to (i) classify documents according to a relevance question and to highlight parts of the text that supported their decision, and on a second phase, (ii) to assess document relevance but supported by text highlighting of varying quality (six human-generated and six machine-generated highlighting conditions). The dataset features documents from two application domains: systematic literature reviews and product reviews, three document sizes, and three relevance questions of different levels of difficulty. We expect this dataset of 27,711 individual judgments from 1851 workers to benefit not only this specific problem domain, but the larger class of classification problems where crowdsourced datasets with individual judgments are scarce. [ABSTRACT FROM AUTHOR]

Objectives: Recently natural language interfaces (e.g., chatbots) have gained enormous attention. Such interfaces execute underlying application programming interfaces (APIs) based on the user's utterances to perform tasks (e.g., reporting weather). Supervised approaches for building such interfaces rely upon a large set of user utterances paired with APIs. Collecting such pairs is typically starts with obtaining initial utterances for a given API method. Generating initial utterances can be considered as a machine translation task in which an API method is translated into an utterance. However, the key challenge is the lack of training samples for training domain-independent translation models. In this paper, we propose a dataset for training supervised models to generate initial utterances for APIs. Data description: The dataset contains 14,370 pairs of API methods and utterances. It is built automatically by converting method descriptions of a large number of APIs to user utterances; and it is cleaned manually to ensure quality. The dataset is also accompanied with a set of microservices (e.g., translating API methods to utterances) which can facilitate the process of collecting training samples for building natural language interfaces. [ABSTRACT FROM AUTHOR]

Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar--all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives, and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions. [ABSTRACT FROM AUTHOR]

A user-centric technique for restructuring integrating e-catalogs is proposed. This technique is implemented in WebCatalog, a system that integrates online product catalogs which aims to continuously improve the organization of catalogs by responding to the ways customers navigate them.

The ubiquity of the World Wide Web offers an ideal opportunity for the deployment of highly distributed applications. Now that connectivity is no longer an issue, the attention has turned to providing a middleware infrastructure that will sustain data sharing among Web-accessible databases. We present a dynamic architecture and system for describing, locating, and accessing data from Web-accessible databases. We propose the use of flexible organizational constructs service links and coalitions to facilitate data organization, discovery, and sharing among Internet-accessible databases. A language is also proposed to support the definition an manipulation of these constructs. The implementation combines Java, CORBA, database API (JDBC), agent, and database technologies to support a scalable and portable architecture interconnecting large networks of heterogeneous and autonomous databases. We report on an experiment to provide uniform access to a Web of healthcare-related databases. Index Terms--Web databases, semistructured data, web querying, CORBA, web agent.

Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions. [ABSTRACT FROM AUTHOR]

Summary: In today's knowledge‐, service‐, and cloud‐based economy, an overwhelming amount of business‐related data are being generated at a fast rate daily from a wide range of sources. These data increasingly show all the typical properties of big data: wide physical distribution, diversity of formats, nonstandard data models, and independently managed and heterogeneous semantics. In this context, there is a need for new scalable and process‐aware services for querying, exploration, and analysis of process data in the enterprise because (1) process data analysis services should be capable of processing and querying large amount of data effectively and efficiently and, therefore, have to be able to scale well with the infrastructure's scale and (2) the querying services need to enable users to express their data analysis and querying needs using process‐aware abstractions rather than other lower‐level abstractions. In this paper, we introduce ProcessAtlas, ie, an extensible large‐scale process data querying and analysis platform for analyzing process data in the enterprise. The ProcessAtlas platform offers an extensible architecture by adopting a service‐based model so that new analytical services can be plugged into the platform. In ProcessAtlas, we present a domain‐specific model for representing process knowledge, ie, process‐level entities, abstractions, and the relationships among them modeled as graphs. We provide services for discovering, extracting, and analyzing process data. We provide efficient mapping and execution of process‐level queries into graph‐level queries by using scalable process query services to deal with the process data size growth and with the infrastructure's scale. We have implemented ProcessAtlas as a MapReduce‐based prototype and report on experiments performed on both synthetic and real‐world datasets. [ABSTRACT FROM AUTHOR]

Cloud services and applications prove indispensable amid today’s modern utility-based computing. The cloud has displayed a disruptive and growing impact on everyday computing tasks. However, facilitating the orchestration of cloud resources to build such cloud services and applications is yet to unleash its entire magnitude of power. Accordingly, it is paramount to devise a unified and comprehensive analysis framework to accelerate fundamental understanding of cloud resource orchestration in terms of concepts, paradigms, languages, models, and tools. This framework is essential to empower effective research, comprehension, comparison, and selection of cloud resource orchestration models, languages, platforms, and tools. This article provides such a comprehensive framework while analyzing the relevant state of the art in cloud resource orchestration from a novel and holistic viewpoint. [ABSTRACT FROM AUTHOR]