Seog-Chan Oh

Effective Web Service Composition in Diverse and Large-Scale Service Networks

The main research focus of Web services is to achieve the interoperability between distributed and heterogeneous applications. Therefore, flexible composition of Web services to fulfill the given challenging requirements is one of the most important objectives in this research field. However, until now, service composition has been largely an error-prone and tedious process. Furthermore, as the number of available web services increases, finding the right Web services to satisfy the given goal becomes intractable. In this paper, toward these issues, we propose an AI planning-based framework that enables the automatic composition of Web services, and explore the following issues. First, we formulate the Web-service composition problem in terms of AI planning and network optimization problems to investigate its complexity in detail. Second, we analyze publicly available Web service sets using network analysis techniques. Third, we develop a novel Web-service benchmark tool called WSBen. Fourth, we develop a novel AI planning-based heuristic Web-service composition algorithm named WSPR. Finally, we conduct extensive experiments to verify WSPR against state-of-the-art AI planners. It is our hope that both WSPR and WSBen will provide useful insights for researchers to develop Web-service discovery and composition algorithms, and software.

A comparative illustration of AI planning-based web services composition

As the number of available web services proliferates, finding right web services to fulfill a given goal becomes an important task. In particular, a problem of combining multiple web services to satisfy a single task, known as web services composition problem, has received much attention recently, and various solutions have been proposed. Among many proposed solutions, however, it is not clear to use which one in what scenarios. In this paper, to this end, we present: (1) a taxonomy and decision guideline of available solution spaces; (2) an overview of syntactic and semantic matching approaches, and (3) a comparative illustration of three representative solutions from the perspective of e-service workflows.

Web Service Planner (WSPR): An Effective and Scalable Web Service Composition Algorithm

As the emergence of service-oriented architecture provides a major boost for e-commerce agility, the number of available Web services is rapidly increasing. However, when there are a large number of Web services available and no single Web service satisfies the given request, one has to “compose†multiple Web services to fulfill the goal. In this article, toward this problem, we present an AI planning-based Web service composition algorithm named as WSPR. We evaluate the efficiency and effectiveness of WSPR using two publicly available test sets—EEE05 and ICEBE05. In addition, we analyze the two test sets and suggest several improvements to benchmark Web service composition better.

BF*: Web services discovery and composition as graph search problem

When there are a large number of Web services available (e.g., in the range of 1,000-10,000), it is non-trivial to quickly find Web services satisfying the given request. Furthermore, when no single Web service satisfies the given request fully, one needs to compose multiple Web services to fulfill the goal. Since the search space for such a composition problem is in general exponentially increasing, it is important to have wise decision on underlying data structures and search algorithms. Toward this problem, in this paper, we present a novel solution, named as BF* (BF-Star), that adopts the competitive A* as a search algorithm while utilizing the Bloom Filter as a succinct data structure.

WSBen: A Web Services Discovery and Composition Benchmark

A novel benchmark, WSBen, for testing Web services discovery and composition is presented. WSBen includes: (1) a collection of synthetic Web services (WSDL) files with diverse characteristics and sizes; (2) test discovery and composition queries and solutions; and (3) external files for statistical analysis and AI planners. Users can fine-tune the generated WSDL files using various parameters such as skewness or matching type. It is our hope that WSBen provides useful insights for researchers evaluating the performance of Web services discovery and composition algorithms and software

Graph Theoretic Topological Analysis of Web Service Networks

Using graph theory, we analyze the topological landscape of web service networks formed by real-world data set, either downloaded from web service repositories or crawled by a search engine. We first propose a flexible framework to study syntactic web service matchmaking in a unified manner. Under the framework, then, the data set is analyzed from diverse perspectives and granularity. By and large, the data set is shown to exhibit small world network well and power-law-like distribution to some extent. Finally, using random graph theory, we demonstrate how to accurately estimate the size of the giant component of such web service networks.

A Web Service Composition Framework Using Integer Programming with Non-functional Objectives and Constraints

In this paper, we propose a Web service composition framework that uses Integer Linear Programming with non-functional objectives and constraints, in addition to the syntactic matching of Web services features. We envision that when Web services are fully deployed and commercialized in the near future, the criteria of Web service composition to achieve objectives will vary depending on users needs or preferences from the number of Web services to non-functional objectives, such as costs, time, and/or reputation. Such non-functional attributes cannot be readily considered in planning-graph, constraint satisfaction, or propositional satisfiability techniques, which are predominantly logic-based. This paper shows how the proposed Integer Linear Programming framework can be utilized to compose Web services with non-functional attributes. This framework enables our composition software agent to identify the best composition result that satisfies both non-functional requirements as well as functional ones, namely, parameter matching. A preliminary implementation of the proposed idea and further research directions are also discussed.

WSPR*: Web-Service Planner Augmented with A* Algorithm

When there are a large number of web services in use, it is non-trivial to quickly find web services satisfying the given request. Furthermore, when no single web service satisfies the given request fully, one needs to “compose” multiple web services to fulfill the goal. It is important to have a wise decision on underlying web-service composition framework because the search space for such a composition problem is in general exponentially increasing. Furthermore, if semantic and QoS issues are concerned, the composition problems become easily intractable. Toward this problem, in this paper, we present WSPR* - Web-Service PlanneR augmented with A* algorithm (a Label Correcting variation algorithm) to compose web services effectively.

Semantic Web-Service Discovery and Composition Using Flexible Parameter Matching

When there are a large number of web services available and no single service satisfies the given request, one has to compose multiple web services to fulfill the goal, considering syntactic and semantic aspects. To address the web service composition issue, in this paper, we present a new composition algorithm by extending our previous work, WSRP (web-service planner) so as to determine relations between different parameter types during the process of service composition.

WSBen: A Web Services Discovery and Composition Benchmark Toolkit1

In this article, a novel benchmark toolkit, WSBen, for testing web services discovery and composition algorithms is presented. The WSBen includes: (1) a collection of synthetically generated web services files in WSDL format with diverse data and model characteristics; (2) queries for testing discovery and composition algorithms; (3) auxiliary files to do statistical analysis on the WSDL test sets; (4) converted WSDL test sets that conventional AI planners can read; and (5) a graphical interface to control all these behaviors. Users can fine-tune the generated WSDL test files by varying underlying network models. To illustrate the application of the WSBen, in addition, we present case studies from three domains: (1) web service composition; (2) AI planning; and (3) the laws of networks in Physics community. It is our hope that WSBen will provide useful insights in evaluating the performance of web services discovery and composition algorithms. The WSBen toolkit is available at: http://pike.psu.edu/sw/wsben/.