Slezak / Zhang / Kim Database Theory and Application

International Conference, DTA 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009, Proceedings
1. Auflage 2009
ISBN: 978-3-642-10583-8
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 64, 197 Seiten

Reihe: Communications in Computer and Information Science

This book constitutes the proceedings of the 2009 International Conference on Database Theory and Application, DTA 2009, held in conjunction with the International Conference on Future Generation Information Technology, FGIT 2009, on December 10-12, 2009, in Jeju Island, Korea. The FGIT 2009 conference received 1051 submissions in total, of which 301 papers were selected to be presented at one of the events taking place as part of it. The 22 papers presented in this volume were carefully reviewed and selected for presentation at DTA 2009. They focus on various aspects of database theory and application in computational sciences, mathematics and information technology and recent progress in these areas.

Autoren/Hrsg.

Slezak, Dominik

Zhang, Yanchun

Kim, Tai-Hoon

Ma, Jianhua

Inhaltsverzeichnis

1;Foreword;5
2;Preface;7
3;Organization;8
4;Table of Contents;9
5;Steganalysis for Reversible Data Hiding;11
5.1;Introduction;11
5.2;Reversible Data Hiding;12
5.2.1;Lossless Data Hiding Based on Histogram Modification of Difference Image;12
5.2.2;Lossless Data Hiding Based on Integer Wavelet Histogram Shifting;13
5.3;Proposed Steganalytic Methods;14
5.3.1;Steganalytic Method for DIH Method;14
5.3.2;Steganalytic Method for IWH Method;15
5.4;Experimental Results;17
5.5;Conclusions;18
5.6;References;18
6;An Incremental View Maintenance Approach Using Version Store in Warehousing Environment;19
6.1;Introduction;19
6.2;Existing Approaches for Maintaining Views;20
6.2.1;Basic Algorithm;20
6.2.2;RVAlgorithm;21
6.2.3;ECA (Eager Compensating Algorthm);21
6.2.4;Lazy Approach;21
6.3;The Possible Solution;22
6.3.1;Working of Our System;22
6.3.2;View Manager;23
6.3.3;Version Store;23
6.4;Performance Evaluation;24
6.4.1;Performance Based on Number of Messages;24
6.5;Conclusion;24
6.6;References;25
7;The Study of Synchronization Framework among Multi-datasets;27
7.1;Introduction;27
7.2;The Synchronization Framework;28
7.3;Conflict Resolving;30
7.4;Synchronization Methods;30
7.5;Case Studies;33
7.6;Conclusions;34
7.7;References;34
8;Clustering News Articles in NewsPage.com Using NTSO;36
8.1;Introduction;36
8.2;Previous Works;37
8.3;String Vectors;38
8.3.1;Definition of String Vector;38
8.3.2;Similarity Matrix;38
8.3.3;Average Semantic Similarity between Two String Vectors;39
8.3.4;Inter-word Set;39
8.4;NTSO;40
8.5;Experiments and Results;41
8.6;Conclusion;42
8.7;References;43
9;Categorizing News Articles Using NTC without Decomposition;44
9.1;Introduction;44
9.2;Previous Works;45
9.3;String Vector;46
9.4;Neural Text Categorizer;46
9.5;Empirical Results;47
9.6;Conclusion;49
9.7;References;49
10;A Comparative Analysis of XML Schema Languages;51
10.1;Introduction;51
10.2;Background;52
10.2.1;Schema Languages;52
10.3;Comparing Schema Languages;54
10.4;Conclusion;57
10.5;References;58
11;Mining Approximate Frequent Itemsets over Data Streams Using Window Sliding Techniques;59
11.1;Introduction;59
11.2;Preliminaries;60
11.3;MAFIM Algorithm;61
11.3.1;Basic Concept of MAFIM Method;61
11.3.2;Mining of Potential Frequent Itemsets within a Current Sliding Window;62
11.3.3;Mining Frequent Itemsets Insert and Delete Phase;64
11.3.4;Pruning Strategy;64
11.4;Experimental Results;65
11.5;Conclusion;66
11.6;References;66
12;Preserving Referential Integrity Constraints in XML Data Transformation;67
12.1;Introduction;67
12.2;Transformation on Referential Integrity Constraints;69
12.2.1;Transformation on XID;70
12.2.2;Transformation on XFK;72
12.3;Preservation of XML Referential Integrity Constraints;72
12.3.1;Preservation of XID;72
12.3.2;Preservation of XFK;73
12.4;Conclusions;74
12.5;References;74
13;Know-Ont: Engineering a Knowledge Ontology for an Enterprise;76
13.1;Introduction;76
13.2;Ontologies in Industrial Domain;77
13.3;Business Cases and Requirement Analysis;77
13.3.1;Analysis of Business Cases;78
13.4;Knowledge Ontology – Know-Ont;79
13.4.1;Know-Ont Specifications;80
13.4.2;A Use-Case Representation of Know-Ont Ontology;81
13.4.3;Querying Know-Ont Using SPARQL;82
13.5;Conclusion and Future Work;83
13.6;References;83
14;Transformation of Data with Constraints for Integration: An Information System Approach;84
14.1;Introduction;84
14.2;Data Transformation and Integration with Constraints: Overview;85
14.2.1;Relational to XML Data Transformation [R X];85
14.2.2;XML to Relational Data Transformation [X R];86
14.2.3;Relational to Relational Data Transformation [R R];86
14.2.4;XML to XML Data Transformation [X X];87
14.3;Data Transformation Framework in Heterogeneous Data Model;87
14.3.1;Schema Transformation [$\tau_{S}$(S)];88
14.3.2;Data Transformation [$\tau_{T/I}$(T/I)];88
14.3.3;Constraints Transformation [$\tau_{C}$(C)];88
14.4;Data Integration Framework with Constraints in Different Data Models for Information Systems;88
14.5;Conclusions;89
14.6;References;90
15;Comparative Analysis of XLMiner and Weka for Association Rule Mining and Clustering;92
15.1;Introduction;92
15.2;Data Normalization;93
15.3;Experimental Results;94
15.3.1;Association Rule Mining (ARM);94
15.3.2;Clustering;96
15.3.3;Analysis of Results;97
15.4;Conclusions and Step Ahead;98
15.5;References;99
16;Infobright for Analyzing Social Sciences Data;100
16.1;Introduction;100
16.2;Infobright;100
16.3;Social Sciences Data;101
16.3.1;Example of Social Sciences Data: Virtual Communities for Those Who Self-injure;101
16.3.2;Semantic Similarity Comparison Metrics;102
16.3.3;Existing Software for Analysis of Social Sciences Data;103
16.4;Infobright Implementation;103
16.4.1;Database Design;104
16.4.2;Database Definition and Loading;104
16.4.3;Database Query;106
16.5;Proposed Extensions to Infobright;106
16.6;Streamlining Extensions with Existing Infobright Methodology;107
16.7;Summary and Conclusions;107
16.8;References;108
17;Enhanced Statistics for Element-Centered XML Summaries;109
17.1;Introduction;109
17.2;Extending EXsum;110
17.3;Empirical Evaluation;113
17.4;Conclusion;116
17.5;References;116
18;Algorithm for Enumerating All Maximal Frequent Tree Patterns among Words in Tree-Structured Documents and Its Application;117
18.1;Introduction;117
18.2;Tree Association Pattern;118
18.3;Enumerating All Maximal Frequent TAPs from Tree-Structured Documents;120
18.4;Generating an XSLT Stylesheet Based on CPP;122
18.5;Concluding Remarks;123
18.6;References;124
19;A Method for Learning Bayesian Networks by Using Immune Binary Particle Swarm Optimization;125
19.1;Introduction;125
19.2;Bayesian Networks and MDL Metric;126
19.2.1;Bayesian Networks;126
19.2.2;The MDL Metric;126
19.3;Immune Binary Particle Swarm Optimization Method;127
19.4;Experiments;129
19.5;Conclusions;130
19.6;References;131
20;A Semantics-Preserving Approach for Extracting OWL Ontologies from UML Class Diagrams;132
20.1;Introduction;132
20.2;Proposed Approach;134
20.2.1;Formalization of UML Class Diagrams;134
20.2.2;Definition of OWL DL Ontologies;135
20.2.3;Translation Algorithm;138
20.3;Implementation and Experiments;141
20.3.1;Prototype Tool;141
20.3.2;Case Study;141
20.4;Related Work;144
20.5;Conclusions;145
20.6;References;145
21;Data Warehousing and Business Intelligence: Benchmark Project for the Platform Selection;147
21.1;Introduction;147
21.1.1;Success Criteria;149
21.2;Benchmarking Objectives;150
21.3;The Benchmark Approach;151
21.4;The Benchmark Planning;151
21.4.1;Cost Effectiveness Criteria;159
21.5;Conclusions;160
21.6;References;160
22;Automatic Extraction of Decision Rules from Non-deterministic Data Systems: Theoretical Foundations and SQL-Based Implementation;161
22.1;Introduction;161
22.2;Decision Rules in Deterministic Information Systems;163
22.3;Searching for Decision Rules Using SQL;164
22.4;Rules in Non-deterministic Information Systems;166
22.5;Searching for Decision Rules in NISs – SQL Perspective;169
22.6;Concluding Remarks;171
22.7;References;172
23;Soft Set Approach for Maximal Association Rules Mining;173
23.1;Introduction;173
23.2;Soft Set Theory;174
23.3;Soft Set Approach for Maximal Association Rules Mining;174
23.3.1;Transformation of a Transactional Database into a Soft Set;174
23.3.2;Taxonomy and Categorization Using Soft Set Theory;175
23.3.3;Soft Maximal Association Rules;175
23.3.4;Experimental Results;176
23.4;Conclusion;179
23.5;References;179
24;Soft Set Theoretic Approach for Dimensionality Reduction;181
24.1;Introduction;181
24.2;Related Works;182
24.3;Information Systems and Set Approximations;183
24.4;Soft Set Theory;184
24.5;Reduction in Information Systems Using Soft Set Theory;185
24.5.1;Multi-soft Sets, AND and OR Operations;185
24.5.2;Attribute Reduction;186
24.6;Conclusion;187
24.7;References;188
25;Rough Set Approach for Categorical Data Clustering;189
25.1;Introduction;189
25.2;Rough Set Approach for Selecting Clustering Attribute;190
25.2.1;Rough Set Theory;190
25.2.2;TR and MMR Techniques;191
25.3;Maximum Attributes Dependencies (MADE) Technique;191
25.3.1;MADE Technique;191
25.3.2;Complexity;192
25.3.3;Objects Splitting;193
25.4;Comparison Tests;193
25.4.1;Soybean Database;193
25.4.2;Zoo Database;194
25.4.3;Comparison Results;194
25.5;Conclusion;196
25.6;References;196
26;Author Index;197