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

A framework for Automatic Web Service Composition based on service dependency analysis

The practice of composing web services has received an increasing interest with the emerging application development architecture called Service Oriented Architecture (SOA). A web service composition can be done either manually or (semi-) automatically. Doing composition (semi-) automatically minimizes runtime problems that arise due to dynamic nature of runtime environments. However, the implementation of (semi-) automatic composition demands for the automation of a process model or a composition plan generation process. In addition, creating a composite service or applications from component services, that are developed and meant to work independently, causes unavoidable dependencies among the services involved. Consequently, in a composite service development, understanding, analyzing and tracking of such dependencies becomes important. This thesis views the process model generation sub-task of a service composition as a service dependency identifification and analysis problem. In this thesis, we propose a dependency based automatic process model generation methods. For this purpose, the following issues are explored. First, a top layer architecture with a composition engine is developed. The architecture gives a complete picture of dependency based automatic service composition. Second, the process model generation sub-task is formulated as a service dependency identification and analysis problem. Third, a two-stepped method for automatic process model generation, given a set of candidate web service descriptions, is proposed. The first step of the proposed approach deals with the identifification of potential direct and indirect dependencies between abstract services. The direct dependency extraction is done by assuming a semantic I/O matching of service parameters. The extraction of indirect dependency from direct dependency is done using a recursive algorithm derived from the transitive closure property. Alternatively the Warshall algorithm is used. The second step of the proposed approach deals with analysis of dependency information and generation of process model (PM) automatically. To execute this step, we propose two approaches: matrix based and graph based approaches. The matrix based approach utilizes both direct and indirect dependencies. This approach represents dependencies using matrix and takes advantages of a sorting algorithm. The matrix representation facilitates a simplistic mathematical dependency analysis for generating important indicators during automatic process model creation. The process model is generated using a sorting algorithm that uses the analysis result obtained from the dependency matrix as sorting criterion. The graph based approach uses only direct dependency among candidate services. As its name indicates, in this approach the extracted I/O dependencies are represented using a directed graph. A modifified topological sorting algorithm is used for generating a process model that shows the execution order of candidate services. Both of the proposed approaches (matrix and graph based approaches) recognize the existence of cyclic dependencies and provide ways of dealing with them. The resulting process model or composition plan from both approaches has a sequential, concurrent and loop control flows. Finally, the performance of the proposed approaches is studied theoretically as well as experimentally. For the experimental validation and evaluation purpose, the approaches are implemented in a prototype that facilitates the validation and evaluation of the approaches at a larger scale. An extensive experimental performance evaluation is done fifirst on each proposed approach. The two approaches are then compared and their pros and cons under difffferent scenarios are assessed

Automatic Dynamic Web Service Composition: A Survey and Problem Formalization

The aim of Web service composition is to arrange multiple services into workflows supplying complex user needs. Due to the huge amount of Web services and the need to supply dynamically varying user goals, it is necessary to perform the composition automatically. The objective of this article is to overview the issues of automatic dynamic Web service composition. We discuss the issues related to the semantics of services, which is important for automatic Web service composition. We propose a problem formalization contributing to the formal definition of the pre-/post-conditions, with possible value restrictions, and their relation to the semantics of services. We also provide an overview of several existing approaches dealing with the problem of Web service composition and discuss the current achievements in the field and depict some open research areas

A Web Service Composition Method Based on OpenAPI Semantic Annotations

Automatic Web service composition is a research direction aimed to improve the process of aggregating multiple Web services to create some new, specific functionality. The use of semantics is required as the proper semantic model with annotation standards is enabling the automation of reasoning required to solve non-trivial cases. Most previous models are limited in describing service parameters as concepts of a simple hierarchy. Our proposed method is increasing the expressiveness at the parameter level, using concept properties that define attributes expressed by name and type. Concept properties are inherited. The paper also describes how parameters are matched to create, in an automatic manner, valid compositions. Additionally, the composition algorithm is practically used on descriptions of Web services implemented by REST APIs expressed by OpenAPI specifications. Our proposal uses knowledge models (ontologies) to enhance these OpenAPI constructs with JSON-LD semantic annotations in order to obtain better compositions for involved services. We also propose an adjusted composition algorithm that extends the semantic knowledge defined by our model.Comment: International Conference on e-Business Engineering (ICEBE) 9 page