Iterchanging Discrete Event Simulationprocess Interaction Modelsusing The Web Ontology Language - Owl

Discrete event simulation development requires significant investments in time and resources. Descriptions of discrete event simulation models are associated with world views, including the process interaction orientation. Historically, these models have been encoded using high-level programming languages or special purpose, typically vendor-specific, simulation languages. These approaches complicate simulation model reuse and interchange. The current document-centric World Wide Web is evolving into a Semantic Web that communicates information using ontologies. The Web Ontology Language OWL, was used to encode a Process Interaction Modeling Ontology for Discrete Event Simulations (PIMODES). The PIMODES ontology was developed using ontology engineering processes. Software was developed to demonstrate the feasibility of interchanging models from commercial simulation packages using PIMODES as an intermediate representation. The purpose of PIMODES is to provide a vendor-neutral open representation to support model interchange. Model interchange enables reuse and provides an opportunity to improve simulation quality, reduce development costs, and reduce development times

Tool Support for Performance Modeling and Optimization

Most of the available modeling and simulation tools for performance analysis do not support model optimization sufficiently. One reason for this unsatisfactory situation is the lack of universally applicable and adaptive optimization strategies. Another reason is that modeling and simulation tools usually have a monolithic software design, which is difficult to extend with experimentation functionality. Such functionality has gained on importance in recent years due to the capability of an automatic extraction of valuable information and knowledge out of complex models. One of the most important experimentation goals is to find model parameter settings, which produce optimal model behavior. In this paper, we elaborate on the design of a powerful optimization component and its integration into existing modeling and simulation tools. For that purpose, we propose a hybrid integration approach being a combination of loose document-based and tight invocation-based integration concepts. Beside the integration concept for the optimization component, we also give a detailed insight into the applied optimization strategies. © 2006, IGI Global. All rights reserved

Infraestructura tecnológica de servicios semánticos para la Web Semántica

This project aims at creating a network of distributed interoperable semantic services for building more complex ones. These services will be available in semantic Web service libraries, so that they can be invoked by other systems (e.g., semantic portals, software agents, etc.). Thus, to accomplish this objective, the project proposes: a) To create specific technology for developing and composing Semantic Web Services. b) To migrate the WebODE ontology development workbench to this new distributed interoperable semantic service architecture. c) To develop new semantic services (ontology learning, ontology mappings, incremental ontology evaluation, and ontology evolution). d) To develop technological support that eases semantic portal interoperability, using Web services and Semantic Web Services. The project results will be open source, so as to improve their technological transfer. The quality of these results is ensured by a benchmarking process. Keywords: Ontologies and Semantic We

Dynamic process modelling for business engineering and information systems evaluation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is concerned with the pre-implementation evaluation of investments in Information Systems (IS). IS evaluation is important as organisations need to assess the financial justifiability of business change proposals that include (but usually are not limited to) the introduction of IS applications. More specifically, this research addresses the problem of benefits assessment within IS evaluation. We contend that benefits assessment should not be performed at the level of the IS application, as most extant evaluation methods advocate. Instead, to study the dynamics and the interactions of the IS applications with their surrounding environment, we propose to adopt the business process as the analytic lens of evaluation and to assess the impacts of IS on organisational, rather than on technical, performance indicators. Drawing on these propositions, this research investigates the potential of dynamic process modelling (via discrete-event simulation) as a facilitator of IS evaluation. We argue that, in order to be effective evaluation tools, business process models should be able to explicitly incorporate the effects of IS introduction on business performance, an issue that is found to be under-researched in previous literature. The above findings serve as the central theme for the development of a design theory of IS evaluation by simulation. The theory provides prescriptive elements that refer both to the design products of the evaluation and the design process by which these products can come into reality. The theory draws on a set of kernel theories from the business engineering domain and proposes a set of meta-requirements that should be satisfied by business process models, a meta-design structure that meets these requirements, and a design method that provides guidance in applying the theoretical propositions in practice. The design theory is developed and empirically tested by means of two real-life case studies. The first study is used to complement the findings of a literature review and to drive the development of the design theory's components, while the second study is employed to validate and further enhance the theory's propositions. The research results support the arguments for simulation-assisted IS evaluation and demonstrate the contribution of the design theory to the field

A Model Based Framework for IoT-Aware Business Process Management

IoT-aware Business Processes (BPs) that exchange data with Internet of Things (IoT) devices, briefly referred to as IoT-aware BPs, are gaining momentum in the BPM field. Introducing IoT technologies from the early stages of the BP development process requires dealing with the complexity and heterogeneity of such technologies at design and analysis time. This paper analyzes widely used IoT frameworks and ontologies to introduce a BPMN extension that improves the expressiveness of relevant BP modeling notations and allows an appropriate representation of IoT devices from both an architectural and a behavioral perspective. In the BP management field, the use of simulation-based approaches is recognized as an effective technology for analyzing BPs. Simulation models need to be parameterized according to relevant properties of the process under study. Unfortunately, such parameters may change during the process operational life, thus making the simulation model invalid with respect to the actual process behavior. To ease the analysis of IoT-aware BPs, this paper introduces a model-driven method for the automated development of digital twins of actual business processes. The proposed method also exploits data retrieved by IoT sensors to automatically reconfigure the simulation model, to make the digital twin continuously coherent and compliant with its actual counterpart

Industrial agents in the era of service-oriented architectures and cloudbased industrial infrastructures

The umbrella paradigm underpinning novel collaborative industrial systems is to consider the set of intelligent system units as a conglomerate of distributed, autonomous, intelligent, proactive, fault-tolerant, and reusable units, which operate as a set of cooperating entities (Colombo and Karnouskos, 2009). These entities are forming an evolvable infrastructure, entering and/or going out (plug-in/plugout) in an asynchronous manner. Moreover, these entities, having each of them their own functionalities, data, and associated information are now connected and able to interact. They are capable of working in a proactive manner, initiating collaborative actions and dynamically interacting with each other in order to achieve both local and global objectives.info:eu-repo/semantics/publishedVersio