Filipe / Aalst / Cordeiro Enterprise Information Systems

10th International Conference, ICEIS 2008, Barcelona, Spain, June 12-16, 2008, Revised Selected Papers
2009
ISBN: 978-3-642-00670-8
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 19, 366 Seiten

Reihe: Lecture Notes in Business Information Processing

This books contains the best papers of the 10th International Conference on Enterprise Information Systems, ICEIS 2008, held in Barcelona, Spain, in June 2008. Two invited papers are presented together with 24 papers which were carefully reviewed and selected from 62 full papers accepted for presentation at the conference itself (out of 665 submissions). The topics covered are: databases and information systems integration, artificial intelligence and decision support systems, information systems analysis and specification, software agents and internet computing, and human-computer interaction.

Autoren/Hrsg.

Aalst, Will

Mylopoulos, John

Szyperski, Clemens

Filipe, Joaquim

Jo,

Cordeiro, José

Inhaltsverzeichnis

1;Title Page;2
2;Preface;5
3;Organization;6
4;Table of Contents;13
5;Invited Papers;16
5.1;The Link between Paper and Information Systems;17
5.1.1;Introduction;17
5.1.2;Digital Pen and Paper;18
5.1.3;Interactive Paper;20
5.1.4;Accessing Information;22
5.1.5;Capturing Information;25
5.1.6;Conclusions;27
5.1.7;References;27
5.2;Service Engineering for the Internet of Services;29
5.2.1;Introduction;29
5.2.2;Marketplaces for the Internet of Services;30
5.2.2.1;What Are Services?;30
5.2.2.2;Discovery, Invocation and/or Execution of Services;32
5.2.2.3;Atomic vs. Composite Services;33
5.2.3;Requirements for the IoS and Marketplaces;34
5.2.4;Service Engineering;36
5.2.4.1;Definition;36
5.2.4.2;The ISE Methodology;37
5.2.4.3;Service Model Integration;38
5.2.5;Conclusions;40
5.2.6;References;40
6;Part I Databases and Information Systems Integration;42
6.1;Bringing the XML and Semantic Web Worlds Closer: Transforming XML into RDF and Embedding XPath into SPARQL;43
6.1.1;Introduction;43
6.1.2;Further Related Work;44
6.1.3;Comparison of XML/RDF and XPath/SPARQL;45
6.1.3.1;XPath and XQuery Data Model and XPath Query Language;45
6.1.3.2;RDF Data Model and SPARQL;46
6.1.4;Translation of XPath Subqueries into SPARQL Queries;48
6.1.4.1;Translation of Data;48
6.1.4.2;Translation of Queries;49
6.1.4.3;Translation of Result;50
6.1.5;Performance Analysis;52
6.1.6;Conclusions;53
6.1.7;References;53
6.1.8;Appendix;55
6.2;A Framework for Semi-automatic Data Integration;58
6.2.1;Introduction;58
6.2.2;Data Integration;60
6.2.3;Matching;60
6.2.3.1;Identifier Constraints and Attribute Relations;62
6.2.4;Mapping;66
6.2.4.1;FCA-Based Mapping Generation;66
6.2.5;Conclusions;71
6.2.6;References;72
6.3;Experiences with Industrial Ontology Engineering;73
6.3.1;Introduction;73
6.3.2;The Subsea Petroleum Industry;74
6.3.3;Semantic Web and Interoperability;77
6.3.4;Developing Oil and Gas Ontologies;78
6.3.5;Industrial Adoption of Semantic Standards;81
6.3.6;Conclusions;82
6.3.7;References;83
6.4;A Semiotic Approach to Quality in Specifications of Software Measures;85
6.4.1;Introduction;85
6.4.2;A Semiotic Quality Framework;86
6.4.2.1;Specification of the Framework;86
6.4.2.2;Discussion;92
6.4.3;An Evaluation of Database Design Measures;94
6.4.4;Conclusions;96
6.4.5;References;97
6.5;Hybrid Computational Models for Software Cost Prediction: An Approach Using Artificial Neural Networks and Genetic Algorithms;99
6.5.1;Introduction;99
6.5.2;Related Work;101
6.5.3;Datasets and Performance Metrics;102
6.5.3.1;Datasets Description;102
6.5.3.2;Performance Metrics;103
6.5.4;Experimental Approach;104
6.5.4.1;A Basic ANN-Model Approach;104
6.5.4.2;A Hybrid Model Approach;107
6.5.5;Conclusions;110
6.5.6;References;112
7;Part II Artificial Intelligence and Decision Support Systems;113
7.1;How to Semantically Enhance a Data Mining Process?;114
7.1.1;Introduction;114
7.1.2;Related Works;115
7.1.2.1;Knowledge Integration in Data Mining;115
7.1.2.2;Ontology Driven Information System (ODIS);116
7.1.2.3;Ontology-Based Validation Methods;116
7.1.3;KEOPS Methodology;117
7.1.3.1;Business Understanding;118
7.1.3.2;Data Understanding;118
7.1.3.3;Data Preparation;120
7.1.3.4;Evaluation;121
7.1.4;Experiments;122
7.1.5;Discussion;124
7.1.6;Conclusions;125
7.1.7;References;126
7.2;Next-Generation Misuse and Anomaly Prevention System;128
7.2.1;Introduction;128
7.2.2;Architecture and Approach;130
7.2.2.1;ESIDE-Depian Knowledge Model Generation Process;131
7.2.2.2;Connection Tracking and Payload Analysis Bayesian Experts Knowledge Model Generation;132
7.2.2.3;Naive Bayesian Network of the Expert Modules;133
7.2.3;The Structural Learning Challenge;134
7.2.3.1;PC-Algorithm Application;134
7.2.3.2;Partial Bayesian Structures Unifying Process;136
7.2.4;Evaluation;136
7.2.5;Conclusions and Future Lines;138
7.2.6;References;139
7.3;Discovering Multi-perspective Process Models: The Case of Loosely-Structured Processes;141
7.3.1;Introduction;141
7.3.2;Formal Framework;143
7.3.3;A Method for the Discovery of Multi-perspective Process Models;144
7.3.3.1;ProcessMining Technique;144
7.3.3.2;Log Restructuring;145
7.3.4;Implementation;146
7.3.5;Case Study;148
7.3.5.1;Application Scenario;148
7.3.5.2;Evaluation Setting;149
7.3.5.3;Experimental Results;149
7.3.6;Conclusions;153
7.3.7;References;153
7.4;Tackling the Debugging Challenge of Rule Based Systems;155
7.4.1;Introduction;155
7.4.1.1;The Debugging Challenge;155
7.4.1.2;Overview;156
7.4.2;Experiences;156
7.4.3;Analyis;157
7.4.4;Core Principles;160
7.4.5;Supporting Rule Base Development;161
7.4.5.1;Test, Debug and Rule Creation as Integrated Activity;161
7.4.5.2;Anomalies;162
7.4.5.3;Visualization;163
7.4.5.4;Debugging;163
7.4.6;Conclusions;164
7.4.7;References;164
7.5;Semantic Annotation of EPC Models in Engineering Domains to Facilitate an Automated Identification of Common Modelling Practices;166
7.5.1;Introduction;166
7.5.2;An Outline of the Overall Approach: Identification of Common Modelling Practices;168
7.5.3;Reference Ontology;171
7.5.3.1;Process Knowledge Base;173
7.5.4;Semantic Annotation Process;175
7.5.4.1;Term Extractor;175
7.5.4.2;Term Normalizer;175
7.5.4.3;Semantic Pattern Analyzer;176
7.5.4.4;Ontology Instance Generator;179
7.5.5;Related Work;179
7.5.6;Conclusions;180
7.5.7;References;181
8;Part III Information Systems Analysis and Specification;183
8.1;Tool Support for the Integration of Light-Weight Ontologies;184
8.1.1;Introduction;184
8.1.2;Related Work;185
8.1.3;Semantic Correspondences;187
8.1.4;Integration Algorithm;188
8.1.5;Correspondence Integrity;191
8.1.6;Tool Support;194
8.1.7;Conclusions;195
8.1.8;References;196
8.2;Business Process Modeling for Non-uniform Work;197
8.2.1;Introduction;197
8.2.2;Research Methodology;198
8.2.2.1;Data Collection;199
8.2.2.2;Data Analysis;199
8.2.3;Case Description;200
8.2.4;Modeling for Non-uniformity of Work;204
8.2.5;Discussion;207
8.2.6;References;208
8.3;Association Rules and Cosine Similarities in Ontology Relationship Learning;210
8.3.1;Introduction;210
8.3.2;Learning Ontology Relationships;211
8.3.3;Association Rules for Text Mining;212
8.3.4;Learning Relationships for Project Management Ontology;213
8.3.5;An Alternative Relationship Learning Method;214
8.3.6;Evaluation;215
8.3.7;Related Work;218
8.3.8;Conclusions;219
8.3.9;References;220
8.4;Compositional Model-Checking Verification of Critical Systems;222
8.4.1;Introduction;222
8.4.2;Compositional Verification Approach;223
8.4.3;Integrated Elements;226
8.4.3.1;MEDISTAM–RT;226
8.4.3.2;CCTL;226
8.4.3.3;CSP+T;226
8.4.4;Our Verification Integrated View;227
8.4.5;Case Study;228
8.4.5.1;Properties Specification;229
8.4.5.2;DDBM Modelling;230
8.4.5.3;Component Verification;230
8.4.5.4;Discussion of Results;232
8.4.6;Conclusions;233
8.4.7;References;233
8.5;Model-Driven Web Engineering in the CMS Domain: A Preliminary Research Applying SME;235
8.5.1;Introduction;235
8.5.2;Background;237
8.5.2.1;The OOWSWeb Engineering Method;237
8.5.3;The OOWS Method Metamodel;238
8.5.4;Analysis of the OOWS Method in the CMS Domain;241
8.5.4.1;User Registration Use Case;241
8.5.4.2;Issues Detected and Method Improvements;242
8.5.5;Conclusions and Further Research;244
8.5.6;References;245
9;Part IV Software Agents and Internet Computing;247
9.1;Binary Serialization for Mobile XForms Services;248
9.1.1;Introduction;248
9.1.2;Problem Statement;249
9.1.3;XebuOverview;250
9.1.4;Initial Assessment;251
9.1.5;Measurements;253
9.1.6;Conclusions;256
9.1.7;Future Work;257
9.1.8;References;258
9.2;An Efficient Neighbourhood Estimation Technique for Making Recommendations;260
9.2.1;Introduction;260
9.2.2;Related Work;261
9.2.3;Taxonomy Product Recommender;262
9.2.3.1;Item Taxonomy Model;262
9.2.3.2;Recommendation Generation;262
9.2.4;Proposed Approach;263
9.2.4.1;Relative Distance Filtering;264
9.2.4.2;Reference User Selection;265
9.2.4.3;Proposed RDF Implementation;266
9.2.5;Experiments;268
9.2.5.1;Experiment Setup;268
9.2.5.2;Result Analysis;269
9.2.6;Conclusions;271
9.2.7;References;271
9.3;Improve Recommendation Quality with Item Taxonomic Information;272
9.3.1;Introduction;272
9.3.2;Related Work;273
9.3.3;Proposed Approach;274
9.3.3.1;System Model;274
9.3.3.2;Cluster-Based User Neighbourhood;275
9.3.3.3;Taxonomic Preferences Extraction;276
9.3.3.4;Hybrid Taxonomy Recommender;278
9.3.4;Experimentation;280
9.3.4.1;Data Acquisition;280
9.3.4.2;Experiment Framework;280
9.3.4.3;Evaluation Metrics;281
9.3.4.4;Experiment Result;282
9.3.5;Conclusions;285
9.3.6;References;285
9.4;Adapting Integration Architectures Based on Semantic Web Services to Industrial Needs;287
9.4.1;Introduction;287
9.4.2;Research Approach;288
9.4.2.1;Survey Design;289
9.4.2.2;Questionnaire;289
9.4.2.3;Expert Panel;289
9.4.2.4;Survey System;290
9.4.3;Results;290
9.4.3.1;Strengths;290
9.4.3.2;Weaknesses;292
9.4.3.3;Opportunities;294
9.4.3.4;Threats;296
9.4.4;Discussion;298
9.4.5;Conclusions;299
9.4.6;References;299
10;Part V Human-Computer Interaction;301
10.1;“Fact or Fiction?” Imposing Legitimacy for Trustworthy Information on the Web: A Qualitative Inquiry;302
10.1.1;Introduction;302
10.1.1.1;Contextualizing the Problems of Trust within W-MIE - Web Based Information for Islamic Content Sharing Sites;304
10.1.1.2;Research Assumptions;305
10.1.2;Theoretical Backgrounds;306
10.1.3;Methodology;307
10.1.3.1;Interview – Semantic Meanings of Trustworthy Elements;307
10.1.4;Results - Transcripts of Focus Groups and Interviews;308
10.1.5;Discussions and Conclusions;309
10.1.6;References;309
10.2;Enabling End Users to Proactively Tailor Underspecified, Human-Centric Business Processes: “Programming by Example” of Weakly-Structured Process Models;312
10.2.1;Introduction;312
10.2.2;Addressed Problem Areas;313
10.2.3;Collaborative Task Manager (CTM);314
10.2.3.1;CTM To-Do List;314
10.2.3.2;Transfer of Tasks and Deliverables;315
10.2.3.3;Process Overview and Navigation;316
10.2.3.4;Process Model Adaptation and Reuse;317
10.2.3.5;Task Pattern Evolution;319
10.2.4;CTM Evaluation;320
10.2.4.1;Setting and Extent of Use;320
10.2.4.2;Findings;321
10.2.5;Conclusions and Future Work;323
10.2.6;References;324
10.3;Enhancing User Experience on the Web via Microformats-Based Recommendations;326
10.3.1;Introduction;326
10.3.2;Microformats;327
10.3.2.1;Important Features;327
10.3.2.2;Representative Microformats;328
10.3.2.3;Examples;328
10.3.3;Proposed Recommending System;330
10.3.3.1;Data Model;330
10.3.3.2;Algorithms;331
10.3.3.3;User Interaction;332
10.3.4;Aspects regarding the Implementation;333
10.3.4.1;Data Collecting and Display Module;333
10.3.4.2;Data Storage and Prediction Module;335
10.3.4.3;Usage Scenario;336
10.3.5;Related Approaches;336
10.3.5.1;Tools;336
10.3.5.2;Websites;337
10.3.6;Conclusions;337
10.3.7;References;338
10.4;Designing Universally Accessible Mobile Multimodal Artefacts;339
10.4.1;Introduction;339
10.4.2;Concepts and Related Work;340
10.4.3;MAFra - Mobile Artefact Framework;341
10.4.3.1;UAMMA - Universally Accessible Mobile Multimodal Artefacts;341
10.4.3.2;Related Tools;342
10.4.4;Prototyping Process;345
10.4.4.1;Low-Fidelity Prototypes;345
10.4.4.2;High-Fidelity Prototypes;348
10.4.4.3;Recognition-Based Interaction Prototypes;350
10.4.5;Conclusions and Future Work;351
10.4.6;References;351
10.5;Dissection of a Visualization On-Demand Server;353
10.5.1;Introduction;353
10.5.2;Related Work;355
10.5.2.1;Information Visualization;355
10.5.2.2;Service Oriented Architectures;357
10.5.2.3;Visualization Service;357
10.5.3;A Visualization On-Demand Architecture;358
10.5.3.1;Architecture;358
10.5.3.2;Strategy of Access;360
10.5.3.3;Personalization;360
10.5.4;Prototype;361
10.5.5;Discussions and Perspectives;363
10.5.6;Conclusions;364
10.5.7;References;364
11;Author Index;366