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

Smart Technologies for Precision Assembly

This open access book constitutes the refereed post-conference proceedings of the 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, held virtually in December 2020. The 16 revised full papers and 10 revised short papers presented together with 1 keynote paper were carefully reviewed and selected from numerous submissions. The papers address topics such as assembly design and planning; assembly operations; assembly cells and systems; human centred assembly; and assistance methods in assembly

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

Investigation of service selection algorithms for grid services

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Additionally, Grid computing has also leveraged web services to define standard interfaces for Grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are assigned to the high volume of incoming requests efficiently, it is important to have a robust service selection algorithm. The selection algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current Quality of Service (QoS) standards. In this research, two service selection algorithms, namely the Particle Swarm Intelligence based Service Selection Algorithm (PSI Selection Algorithm) based on the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique, and the Constraint Satisfaction based Selection (CSS) algorithm, are proposed. The proposed selection algorithms are designed to achieve the following goals: handling large number of incoming requests simultaneously; achieving high match scores in the case of competitive matching of similar types of incoming requests; assigning each services efficiently to all the incoming requests; providing the service requesters the flexibility to provide multiple service selection criteria based on a QoS metric; selecting the appropriate services for the incoming requests within a reasonable time. Next, the two algorithms are verified by a standard assignment problem algorithm called the Munkres algorithm. The feasibility and the accuracy of the proposed algorithms are then tested using various evaluation methods. These evaluations are based on various real world scenarios to check the accuracy of the algorithm, which is primarily based on how closely the requests are being matched to the available services based on the QoS parameters provided by the requesters

A semantic framework for event-driven service composition

Title from PDF of title page, viewed on September 14, 2011VitaDissertation advisor: Yugyung LeeIncludes bibliographical references (p. 289-329)Thesis (Ph.D)--School of Computing and Engineering. University of Missouri--Kansas City, 2011Service Oriented Architecture (SOA) has become a popular paradigm for designing distributed systems where loosely coupled services (i.e. computational entities) can be integrated seamlessly to provide complex composite services. Key challenges are discovery of the required services using their formal descriptions and their coherent composition in a timely manner. Most service descriptions are written in XML-based languages that are syntactic, creating linguistic ambiguity during service matchmaking. Furthermore, existing models that implement SOA have mostly middleware-controlled synchronous request/replybased runtime binding of services that incur undesirable service latency. In addition, they impose expensive state monitoring overhead on the middleware. Some newer event-driven models introduce asynchronous publish/subscribe-based event notifications to consumer applications and services. However, they require an event-library that stores definitions of all possible system events, which is impractical in an open and dynamic system. The objective of this study is to efficiently address on-demand consumer requests with minimum service latency and maximum consumer utility. It focuses on semantic eventdriven service composition. For efficient semantic service discovery, the dissertation proposes a novel service learning algorithm called Semantic Taxonomic Clustering (STC). The algorithm utilizes semantic service descriptions to cluster services into functional categories for pruning search space during service discovery and composition. STC utilizes a dynamic bit-encoding algorithm called DL-Encoding that enables linear time bit operationbased semantic matchmaking as compared to expensive reasoner-based semantic matchmaking. The algorithm shows significant improvement in performance and accuracy over some of the important service category algorithms reported in the literature. A novel user-friendly and computationally efficient query model called Desire-based Query Model (DQM) is proposed for formally specifying service queries. STC and DQM serve as the building block for the dual framework that is the core contribution of this dissertation: (i) centralized ALNet (Activity Logic Network) platform and (ii) distributed agentbased SMARTSPACE platform. The former incorporates a middleware controlled service composition algorithm called ALNetComposer while the latter includes the SmartDeal purely distributed composition algorithm. The query response accuracy and performance were evaluated for both the algorithms under simulated event-driven SOA environments. The experimental results show that various environmental parameters, such as domain diversity and scope, size and complexity of the SOA system, and dynamicity of the SOA system, significantly affect accuracy and performance of the proposed model. This dissertation demonstrates that the functionality and scalability of the proposed framework are acceptable for relatively static and domain specific environments as well as large, diverse, and highly dynamic environments. In summary, this dissertation addresses the key design issues and problems in the area of asynchronous and pro-active event-driven service composition.Introduction -- Research background -- Semantic service matchmaking & query modeling -- Service organization by learning service category -- ALNet: event-driven platform for service composition -- SMARTSPACE: distributed multi-agent based event-handeling -- Conclusion & future wor

Extending Web Service Architecture with a Quality Component: Web Service Architecture and Quality Component

The Web service technology provides standard mechanisms for describing the interface of the services available on the Web, as well as protocols for locating such services and invoking them. Each Web service has an associated Web Services Description Language (WSDL) document which describes how it works and how to invoke it. Such document is registered at a Universal Description, Discovery and Integration (UDDI) registry that provides a discovery service for the WSDL descriptions. The Web services architecture consists of three components: Service Provider, Service Requester and UDDI Registry, and the interactions between them through publish, find, and bind operations. Between finding and binding steps there is another crucial step, which is not fully considered by current approaches. This is the step of selection. The UDDI service registry hosts hundreds of similar Web services, which makes it difficult for the service requesters to choose from them, as the selection is based on the functional properties only. However, many similar services are differentiated by their quality criteria. Therefore, quality criteria are important to be considered in the web service selection. This thesis proposes a quality-based Web service architecture (QWSA) that extends the current Web service architecture with a quality server. The quality server consists of four main components: quality manager, quality matchmaker, quality report analyzer, and quality database. The main purpose of quality server is to assist service requester to select the best available service that fulfils his/her preference by matching between a service requester’s quality requirement and the service providers’ quality specifications. In addition, this thesis reports the development of a quality matchmaking process (QMP) based on the proposed architecture by building a quality service selection system (QSSS). This QSSS has been verified and validated using a case study of Amazon E-commerce service (ECS)

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Machine learning applied to the context of Poker

A combinação de princípios da teoria de jogo e metodologias de machine learning aplicados ao contexto de formular estratégias ótimas para jogos está a angariar interesse por parte de uma porção crescentemente significativa da comunidade científica, tornando-se o jogo do Poker num candidato de estudo popular devido à sua natureza de informação imperfeita. Avanços nesta área possuem vastas aplicações em cenários do mundo real, e a área de investigação de inteligência artificial demonstra que o interesse relativo a este objeto de estudo está longe de desaparecer, com investigadores do Facebook e Carnegie Mellon a apresentar, em 2019, o primeiro agente de jogo autónomo de Poker provado como ganhador num cenário com múltiplos jogadores, uma conquista relativamente à anterior especificação do estado da arte, que fora desenvolvida para jogos de apenas 2 jogadores. Este estudo pretende explorar as características de jogos estocásticos de informação imperfeita, recolhendo informação acerca dos avanços nas metodologias disponibilizados por parte de investigadores de forma a desenvolver um agente autónomo de jogo que se pretende inserir na classificação de "utility-maximizing decision-maker".The combination of game theory principles and machine learning methodologies applied to encountering optimal strategies for games is garnering interest from an increasing large portion of the scientific community, with the game of Poker being a popular study subject due to its imperfect information nature. Advancements in this area have a wide array of applications in real-world scenarios, and the field of artificial intelligent studies show that the interest regarding this object of study is yet to fade, with researchers from Facebook and Carnegie Mellon presenting, in 2019, the world’s first autonomous Poker playing agent that is proven to be profitable while confronting multiple players at a time, an achievement in relation to the previous state of the art specification, which was developed for two player games only. This study intends to explore the characteristics of stochastic games of imperfect information, gathering information regarding the advancements in methodologies made available by researchers in order to ultimately develop an autonomous agent intended to adhere to the classification of a utility-maximizing decision-maker