Hyunyoung Kil

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.

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.

On the computational complexity of behavioral description-based web service composition

The behavioral description-based Web Service Composition (WSC) problem deals with the automatic construction of a coordinator web service that controls a set of web services to reach the goal states. Despite its importance and implications, very few studies exist on the computational complexities of the WSC problem. In this paper, to address this problem, we present four novel theoretical findings on the WSC problem: (1) solving the composition problem of deterministic web services for a restricted case (when the coordinator web service has complete information about the states of all web services) is PSPACE-complete; (2) solving the composition problem of deterministic web services for a general case (when the coordinator web service has incomplete information about the states of web services) is EXPSPACE-complete; (3) solving the composition problem of non-deterministic web services on complete information is EXP-complete and (4) solving the composition problem of non-deterministic web services on incomplete information (which is the most general case) is 2-EXP-complete. These findings suggest that more efforts to devise efficient approximation solutions to the WSC problem is needed.

Algorithms for Web Services Discovery and Composition Based on Syntactic and Semantic Service Descriptions

As the number of available Web services is rapidly increasing, the effective and efficient service discovery and composition becomes a pressing problem for value-added distributed applications. To address this problem, in this paper, we present efficient service discovery and composition algorithms that exploit both syntactic and semantic service descriptions of Web services

QoS-Driven Web Service Composition Using Learning-Based Depth First Search

The goal of the Web Service Composition (WSC) problem is to find an optimal composition of web services to satisfy a given request using their syntactic and/or semantic features. In this paper, in particular, we study the Quality of Services (QoS)-driven WSC problem to optimize service quality criteria, e.g., response time and/or throughput. We propose a novel solution based on Learning-based Depth First Search (LDFS). Given a set of web service descriptions including QoS information and a requirement web service, we reduce the QoS-driven WSC problem into a planning problem on a state-transition system. We then find the optimal solution for the problem using a dynamic programming based on LDFS which recently has shown a promising result.

Semantic web service composition via model checking techniques

The web service composition problem aims to find an optimal composition of web services to satisfy a given request by using their syntactic and/or semantic features when no single service satisfies it. In particular, the semantics of services helps a composition engine identify more correct, complete and optimal candidates as a solution. In this paper, we study the web service composition problem considering semantic aspects, i.e., exploiting the semantic relationship between parameters of web services. Given a set of web service descriptions, their semantic information and a requirement web service, we find the optimal composition that contains the shortest path of semantically well connected web services which satisfies the requirement. Our techniques are based on semantic matchmaking and three model checking techniques such as Boolean satisfiability solving, symbolic model checking, and abstraction and refinement technique. In the experiment, our proposal efficiently identifies optimal compositions of web services.

Efficient anytime algorithm for large-scale QoS-aware web service composition

The QoS-aware web service composition WSC problem aims at the fully automatic construction of a composite web service with the optimal accumulated QoS value. It is, however, intractable to solve the QoS-aware WSC problem for large scale instances since the problem corresponds to a global optimisation problem. That is, in the real world, traditional algorithms can require significant amount of time to finally find the optimal solution, and such an unexpected long delay is unfavourable to users. In this paper, we propose a novel anytime algorithm using dynamic beam widths for the QoS-aware WSC problem. Our algorithm generates early sub-optimal solutions and keeps improving the quality of the solution along with the execution time, up to the optimal solution if a client allows enough time. We empirically validate that our algorithm can identify composite web services with high quality much earlier than an optimal algorithm and the beam stack search.

Anytime algorithm for QoS web service composition

The QoS-aware web service composition (WSC) problem aims at the automatic construction of a composite web service with the optimal accumulated QoS value. It is, however, intractable to solve the QoS-aware WSC problem for large scale instances, since the problem corresponds to a global optimization problem. In this paper, we propose a novel anytime algorithm for the QoS-aware WSC problem to identify composite web services with high quality much earlier than an optimal algorithm and the beam stack search [3].

Behavioural description based web service composition using abstraction and refinement

The web service composition problem with respect to behavioural descriptions deals with the automatic synthesis of a coordinator web service that controls a set of web services to reach a goal state. Despite its importance, however, solving the problem for a general case when the coordinator has only partial observations remains doubly exponential in the number of variables in web service descriptions. Toward this challenge, we propose two novel signature preserving and subsuming approximation-based approaches using abstraction and refinement. Given a set of web service behavioural descriptions and a reachability goal, we automatically construct abstract web services which have less variables using over-approximation. If our method identifies a coordinator web service, the coordinator is guaranteed to control the given web services to reach the goal state no matter how they behave. Otherwise, our method refines the current abstraction by adding some variables that have strong dependency on the goal variables.