A graph-based framework for optimal semantic web service composition

Web services are self-described, loosely coupled software components that are network-accessible through standardized web protocols, whose characteristics are described in XML. One of the key promises of Web services is to provide better interoperability and to enable a faster integration between systems. In order to generate robust service oriented architectures, automatic composition algorithms are required in order to combine the functionality of many single services into composite services that are able to respond to demanding user requests, even when there is no single service capable of performing such task. Service composition consists of a combination of single services into composite services that are executed in sequence or in a different order, imposed by a set of control constructions that can be specified using standard languages such as OWL-s or BPEL4WS. In the last years several papers have dealt with composition of web services. Some approaches treat the service composition as a planning problem, where a sequence of actions lead from a initial state to a goal state. However, most of these proposals have some drawbacks: high complexity, high computational cost and inability to maximize the parallel execution of web services. Other approaches consider the problem as a graph search problem, where search algorithms are applied over a web service dependency graph in order to find a solution for a particular request. These proposals are simpler than their counterparts and also many can exploit the parallel execution of web services. However, most of these approaches rely on very complex dependency graphs that have not been optimized to remove data redundancy, which may negatively affect the overall performance and scalability of these techniques in large service registries. Therefore, it is necessary to identify, characterize and optimize the different tasks involved in the automatic service composition process in order to develop better strategies to efficiently obtain optimal solutions. The main goal of this dissertation is to develop a graph-based framework for automatic service composition that generate optimal input-output based compositions not only in terms of complexity of the solutions, but also in terms of overall quality of service solutions. More specifically, the objectives of this thesis are: (1) Analysis of the characteristics of services and compositions. The aim of this objective is to characterize and identify the main steps that are part for the service composition process. (2) Framework for automatic graph-based composition. This objective will focus on developing a framework that enables the efficient input-output based service composition, exploring the integration with other tasks that are part of the composition process, such as service discovery. (3) Development of optimal algorithms for automatic service composition. This objective focuses on the development of a set of algorithms and optimization techniques for the generation of optimal compositions, optimizing the complexity of the solutions and the overall Quality-of- Service. (4) Validation of the algorithms with standard datasets so they can be compared with other proposals

Software Reliability in Semantic Web Service Composition Applications

Web Service Composition allows the development of easily reconfigurable applications that can be quickly adapted to business changes. Due to the shift in paradigm from traditional systems, new approaches are needed in order to evaluate the reliability of web service composition applications. In this paper we present an approach based on intelligent agents for semiautomatic composition as well as methods for assessing reliability. Abstract web services, corresponding to a group of services that accomplishes a specific functionality are used as a mean of assuring better system reliability. The model can be extended with other Quality of Services – QoS attributes.Software Reliability, Web Service Composition, Intelligent Agents

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Semantics-aware planning methodology for automatic web service composition

Service-Oriented Computing (SOC) has been a major research topic in the past years. It is based on the idea of composing distributed applications even in heterogeneous environments by discovering and invoking network-available Web Services to accomplish some complex tasks when no existing service can satisfy the user request. Service-Oriented Architecture (SOA) is a key design principle to facilitate building of these autonomous, platform-independent Web Services. However, in distributed environments, the use of services without considering their underlying semantics, either functional semantics or quality guarantees can negatively affect a composition process by raising intermittent failures or leading to slow performance. More recently, Artificial Intelligence (AI) Planning technologies have been exploited to facilitate the automated composition. But most of the AI planning based algorithms do not scale well when the number of Web Services increases, and there is no guarantee that a solution for a composition problem will be found even if it exists. AI Planning Graph tries to address various limitations in traditional AI planning by providing a unique search space in a directed layered graph. However, the existing AI Planning Graph algorithm only focuses on finding complete solutions without taking account of other services which are not achieving the goals. It will result in the failure of creating such a graph in the case that many services are available, despite most of them being irrelevant to the goals. This dissertation puts forward a concept of building a more intelligent planning mechanism which should be a combination of semantics-aware service selection and a goal-directed planning algorithm. Based on this concept, a new planning system so-called Semantics Enhanced web service Mining (SEwsMining) has been developed. Semantic-aware service selection is achieved by calculating on-demand multi-attributes semantics similarity based on semantic annotations (QWSMO-Lite). The planning algorithm is a substantial revision of the AI GraphPlan algorithm. To reduce the size of planning graph, a bi-directional planning strategy has been developed

NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on Services Computing) on July 1