Miltiadis Petridis

A case based reasoning approach for the monitoring of business workflows

This paper presents an approach for the intelligent diagnosis and monitoring of business workflows based on operation data in the form of temporal log data. The representation of workflow related case knowledge in this research using graphs is explained. The workflow process is orchestrated by a software system using BPEL technologies within a service-oriented architecture. Workflow cases are represented in terms of events and their corresponding temporal relationships. The matching and CBR retrieval mechanisms used in this research are explained and the architecture of an integrated intelligent monitoring system is shown. The paper contains an evaluation of the approach based on experiments on real data from a university quality assurance exam moderation system. The experiments and the evaluation of the approach is presented and is shown that a graph matching based similarity measure is capable to diagnose problems within business workflows. Finally, further work on the system and the extension to a full intelligent monitoring and process optimisation system is presented.

Flowes: An intelligent computational fluid dynamics system

Abstract In this paper, experience in the design, implementation and usage of an experimental computational fluid dynamics software package incorporating an intelligent knowledge-based component is described. The types and sources of knowledge which can be included in a system are discussed with reference to a simple example, and the advantages of their inclusion in a system are given. A “blackboard” architecture for the system is described, and the importance of software engineering aspects of the total system is pointed out. Examples of the types of rule are given, and their mode of operation. The paper concludes with a summary of the general lessons to be learned from this example development concerning the incorporation of intelligence into engineering software.

The integration of an intelligent knowledge-based system into engineering software using the blackboard structure

Over recent years there has been an increase in the use of generic Computational Fluid Dynamics (CFD) software packages spread across various application fields. This has created the need for the integration of expertise into CFD software. Expertise can be integrated into CFD software in the form of an Intelligent Knowledge-Based System (IKBS). The advantages of integrating intelligence into generic engineering software are discussed with a special view to software engineering considerations. The software modelling cycle of a typical engineering problem is identified and the respective expertise and user control needed for each modelling phase is shown. The requirements of an IKBS for CFD software are discussed and compared to current practice. The blackboard software architecture is presented. This is shown to be appropriate for the integration of an IKBS into an engineering software package. This is demonstrated through the presentation of the prototype CFD software package FLOWES.

A Design for Reliable CFD Software

The viewpoint of this paper is that the special needs of computational software in terms of reliability must be considered at the initial stages of a software development. The paper describes an experimental implementation of a design which has been developed in order to address some key features of software reliability in computational fluid dynamies. The areas considered important for reliability are the software engineering, problem set-up results validation and the integration of an IKBS.

Visualizing a temporal consistency checker

The notion of time plays a vital and ubiquitous role of a common universal reference. In knowledge-based systems, temporal information is usually represented in terms of a collection of statements, together with the corresponding temporal reference. This paper introduces a visualized consistency checker for temporal reference. It allows expression of both absolute and relative temporal knowledge, and provides visual representation of temporal references in terms of directed and partially weighted graphs. Based on the temporal reference of a given scenario, the visualized checker can deliver a verdict to the user as to whether the scenario is temporally consistent or not, and provide the corresponding analysis/diagnosis.

Theory And Models For Temporal Reasoning

This paper presents a general time theory of point-based intervals. Four types of time intervals are addressed in the theory on an equal footing, and the collection of time points are characterised as a special kind of time intervals. Axioms characterising the nature of time, such as linearity, density and extension are also given. This provides a sound foundation for temporal reasoning in the domain of artificial intelligence. It is shown that this general theory forms a basis for some well-known temporal models. The original axioms on which each of these temporal models have been based may be established as theorems of the theory by means of some appropriate interpretations.