An Ontology for Formalising Agreement Patterns in Auction Markets

Knowledge and best practices on auction systems are cur- rently disseminated across the research literature, which limits its access, reuse, evaluation and feedback by practitioners. This article presents a systematic approach to collect this knowledge as design patterns, in order to provide assistance to software developers. An ontology has been de- _ned for formalising design patterns in auction systems, with the aim of improving its searchability by software developers. Finally, a case study illustrates how the proposed pattern ontology provides assistance in the development of a dynamic pricing model for an e-commerce servic

Enhanced ontology-based text classification algorithm for structurally organized documents

Text classification (TC) is an important foundation of information retrieval and text mining. The main task of a TC is to predict the text‟s class according to the type of tag given in advance. Most TC algorithms used terms in representing the document which does not consider the relations among the terms. These algorithms represent documents in a space where every word is assumed to be a dimension. As a result such representations generate high dimensionality which gives a negative effect on the classification performance. The objectives of this thesis are to formulate algorithms for classifying text by creating suitable feature vector and reducing the dimension of data which will enhance the classification accuracy. This research combines the ontology and text representation for classification by developing five algorithms. The first and second algorithms namely Concept Feature Vector (CFV) and Structure Feature Vector (SFV), create feature vector to represent the document. The third algorithm is the Ontology Based Text Classification (OBTC) and is designed to reduce the dimensionality of training sets. The fourth and fifth algorithms, Concept Feature Vector_Text Classification (CFV_TC) and Structure Feature Vector_Text Classification (SFV_TC) classify the document to its related set of classes. These proposed algorithms were tested on five different scientific paper datasets downloaded from different digital libraries and repositories. Experimental obtained from the proposed algorithm, CFV_TC and SFV_TC shown better average results in terms of precision, recall, f-measure and accuracy compared against SVM and RSS approaches. The work in this study contributes to exploring the related document in information retrieval and text mining research by using ontology in TC

Exploring the Integration of Agent-Based Modelling, Process Mining, and Business Process Management through a Text Analytics–Based Literature Review

Agent-based modelling and business process management are two interrelated yet distinct concepts. To explore the relationship between these two fields, we conducted a systematic literature review to investigate existing methods and identify research gaps in the integration of agent-based modelling, process mining, and business process management. Our search yielded 359 research papers, which were evaluated using predefined criteria and quality measures. This resulted in a final selection of forty-two papers. Our findings reveal several research gaps, including the need for enhanced validation methods, the modelling of complex agents and environments, and the integration of process mining and business process management with emerging technologies. Existing agent-based approaches within process mining and business process management have paved the way for identifying the validation methods for performance evaluation. The addressed research gaps primarily concern validation before delving deeper into specific research topics. These include improved validation methods, modelling of complex agents and environments, and a preliminary exploration of integrating process mining and business process management with emerging technologies

Predictive Framework for Imbalance Dataset

The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-processing and classification methodologies have been adapted in this research. Properties of the proposed framework include; developing an approach to correlate materials defects, developing an approach to represent data attributes features, analyzing various ratio and types of data re-sampling, analyzing the impact of data dimension reduction for various data size, and partitioning data size and algorithmic schemes against the prediction performance. Experimental results suggested that the class probability distribution function of a prediction model has to be closer to a training dataset; less skewed environment enable learning schemes to discover better function F in a bigger Fall space within a higher dimensional feature space, data sampling and partition size is appear to proportionally improve the precision and recall if class distribution ratios are balanced. A comparative study was also conducted and showed that the proposed approaches have performed better. This research was conducted based on limited number of datasets, test sets and variables. Thus, the obtained results are applicable only to the study domain with selected datasets. This research has introduced a new predictive maintenance framework which can be used in manufacturing industries to generate a prediction model based on the deterioration of process materials. Consequently, this may allow manufactures to conduct predictive maintenance not only for equipments but also process materials. The major contribution of this research is a step by step guideline which consists of methods/approaches in generating a prediction for process materials

A document based traceability model for test management

Software testing has became more complicated in the emergence of distributed network, real-time environment, third party software enablers and the need to test system at multiple integration levels. These scenarios have created more concern over the quality of software testing. The quality of software has been deteriorating due to inefficient and ineffective testing activities. One of the main flaws is due to ineffective use of test management to manage software documentations. In documentations, it is difficult to detect and trace bugs in some related documents of which traceability is the major concern. Currently, various studies have been conducted on test management, however very few have focused on document traceability in particular to support the error propagation with respect to documentation. The objective of this thesis is to develop a new traceability model that integrates software engineering documents to support test management. The artefacts refer to requirements, design, source code, test description and test result. The proposed model managed to tackle software traceability in both forward and backward propagations by implementing multi-bidirectional pointer. This platform enabled the test manager to navigate and capture a set of related artefacts to support test management process. A new prototype was developed to facilitate observation of software traceability on all related artefacts across the entire documentation lifecycle. The proposed model was then applied to a case study of a finished software development project with a complete set of software documents called the On-Board Automobile (OBA). The proposed model was evaluated qualitatively and quantitatively using the feature analysis, precision and recall, and expert validation. The evaluation results proved that the proposed model and its prototype were justified and significant to support test management

Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones

Jahresbericht Forschung und Entwicklung 2004

Forschungsjahresbericht 2004 der Fachhochschule Konstan

Towards an approach for weaving preferences into web services operation

Existing approaches on Web services privacy dominate solutions from a users’ perspective, giving little consideration to the preferences of Web service providers. The integration of service providers’ preferences into Web services’ operations is discussed in this paper. A Web service provider indicates peer Web services that it could interact with as well as the data that they could exchange with. We focus on Privacy and (trust) Partnership preferences based on which, we develop a Specification for Privacy and Partnership Preferences (S3P). This specification suggests a list of exceptional actions to deploy at run-time when these preferences are not met. An integration model of these preferences into Web services design is illustrated throughout a running scenario, and an implementation framework proves the S3P concept.Zakaria Maamar, Quan Z. Sheng, Yacine Atif, Sujith Samuel Mathew, Khouloud Boukad