Lee / Kim Advances in Software Engineering

International Conference, ASEA 2008, and Its Special Sessions, Sanya, Hainan Island, China, December 13-15, 2008. Revised Selected Papers
1. Auflage 2009
ISBN: 978-3-642-10242-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 30, 250 Seiten

Reihe: Communications in Computer and Information Science

This volume constitutes the selected and extended papers of the International Conference on Advances in Software Engineering, ASEA 2008, and Its Special Sessions, as well as the International Conference on Advanced Science and Technology, AST 2009. The book includes the special sessions on software development and practical application (SDPA 2008), on software engineering for persistent computing systems (SEPCS 2008), and on security and privacy issues in ubiquitous healthcare technology and service (SPI-UHTS 2008).

Autoren/Hrsg.

Lee, Changhoon

Kim, Tai-Hoon

Inhaltsverzeichnis

1;Preface;5
2;Organization;6
3;Table of Contents;8
4;An Enhanced Content Distribution Method Using Metadata Annotation in CDN;10
4.1;Introduction;10
4.2;Related Works;11
4.2.1;Basic Concept and Organization of CDN;11
4.3;Contents Distribution and Management (CDM) System Using Metadata;12
4.3.1;CDM System Architecture;13
4.3.2;Contents Transmission Method Based on Segment;14
4.3.3;Management of Contents Using Metadata;16
4.3.4;Metadata Definition for Contents Management;18
4.4;Implementation and Evaluation;18
4.4.1;Implementation and Simulation Environment;18
4.5;Conclusion;21
4.6;References;21
5;Feature Tracking in Long Video Sequences Using Point Trajectories;23
5.1;Introduction;23
5.2;Related Work;24
5.2.1;KLT-Based Feature Tracking;24
5.2.2;SIFT-Based Feature Tracking;25
5.3;Improving the Feature Tracking Accuracy;26
5.3.1;Feature Extraction;26
5.3.2;State Modeling for Feature Tracking;27
5.3.3;Rejection of Outliers;28
5.3.4;Tracking Feature Points;28
5.4;Experimental Results;29
5.4.1;Feature Tracking in Short Simple Camera Movement;32
5.4.2;Feature Tracking in General Camera Movement;32
5.4.3;Error Analysis Using Epipolar Distance;32
5.5;Conclusion;35
5.6;References;36
6;A Model-Driven Framework for Dynamic Web Application Development;38
6.1;Introduction;38
6.2;Related Work;39
6.3;Systems Architecture;41
6.3.1;The XML Schema of the Web User-Interface Components;41
6.3.2;The Navigational Model XML Description File;44
6.3.3;The Visible Graphical Editor;45
6.3.4;Application Code Generator;48
6.4;Conclusions;50
6.5;References;50
7;Experience with MOF-Based Meta-modeling of Component-Based Systems;52
7.1;Introduction;52
7.1.1;Goal and Structure of the Paper;53
7.2;Background;54
7.2.1;Models and Meta-models;54
7.2.2;Component Models;55
7.3;Applying MOF in Component Model Design and Implementation;56
7.3.1;Defining Semantics of a Component Model;57
7.3.2;Infrastructure Creation;58
7.3.3;Creation of Runtime Management Tools;59
7.4;Evaluation and Related Work;59
7.5;Conclusion;62
7.6;References;62
8;Closely Spaced Multipath Channel Estimation in CDMA Networks Using Divided Difference Filter;64
8.1;Introduction;64
8.2;Channel and Signal Model;65
8.3;Parameter Estimation Using the Divided Difference Filter;67
8.3.1;Overview of DDF Algorithm;67
8.4;Application to Channel Estimation with Multipath/ Multiuser Model;68
8.5;Conclusion;72
8.6;References;73
9;Building “Bag of Conception” Model Based on DBpedia;75
9.1;Introduction;75
9.2;DBpedia;76
9.3;Compiling DBpedia Knowledge into Document Representation;79
9.3.1;Preliminaries;79
9.3.2;Conceptual Document Representation;81
9.4;Experiments;83
9.4.1;Experimental Framework: Design and Architecture;83
9.4.2;Datasets and Platform;84
9.4.3;Results and Discussion;84
9.5;Conclusion;86
9.6;References;86
10;Study on the Performance Support Vector Machine by Parameter Optimized;88
10.1;Introduction;88
10.2;Brief Introduction of Support Vector Machine[1~3];89
10.3;Rough Sets Theory;90
10.4;Genetic Algorithms[9~11];91
10.5;System Overview;93
10.5.1;Algorithm of RST-Based Feature Reduce;94
10.5.2;Chromosome Design;95
10.5.3;Fitness Function;96
10.6;Experiments;96
10.6.1;Experiment Environment;96
10.6.2;Data Set;97
10.6.3;The Performance Measure;97
10.6.4;Simulate;97
10.7;Conclusion;100
10.8;References;100
11;A Tiling Bound for Pairwise Global Sequence Alignment;102
11.1;Introduction;102
11.2;Method;103
11.2.1;Computation Time and Tiling Set Choice;105
11.3;References;106
12;Discovering Decision Tree Based Diabetes Prediction Model;108
12.1;Introduction;108
12.1.1;RapidMiner;109
12.1.2;Project Plan and Data Set;109
12.2;Data Preprocessing;110
12.2.1;Feature Identification and Categorization;110
12.2.2;Outlier Removal and Feature Selection;111
12.2.3;Data Normalization;112
12.2.4;Numerical Data Discretization;112
12.3;Data Analysis;113
12.4;Finding Hidden Relationships;114
12.5;Constructing the Prediction Model;115
12.6;Conclusion and Future Work;117
12.7;References;117
13;Application of Formal Concept Analysis in Model-Based Testing;119
13.1;Introduction;119
13.2;Related Work;120
13.3;Formal Concept Analysis;121
13.4;Model-Based Test Suite Reduction with FCA;123
13.4.1;Formal Context of Transition Coverage;125
13.4.2;Sufficiency of Transition Coverage;127
13.4.3;Reduction of Test Suite;127
13.5;Conclusion;131
13.6;References;131
14;The Prediction Model of Software Reliability Based on Fractals;133
14.1;Introduction;133
14.2;Power Law;135
14.3;The Software Failure Prediction Based on Fractals;136
14.4;Conclusions;143
14.5;References;143
15;New Approach to Information Sharing Using Linguistic Threshold Schemes;146
15.1;Introduction;146
15.2;Secret Sharing Algorithms;147
15.3;Protocol for Grammar Extension during Shadow Generation;148
15.4;Grammar Approach for Information Sharing;149
15.5;Grammars for Converting Bit Blocks;151
15.6;Conclusion;153
15.7;References;154
16;An Innovative Approach for Generating Static UML Models from Natural Language Requirements;156
16.1;Introduction;156
16.2;The Methodology;157
16.2.1;Natural Language Requirements:;157
16.2.2;Features of Natural Language Processing Tools Used for Developing SUGAR:;160
16.2.3;Syntactic Reconstruction;161
16.2.4;Rational Unified Approach;162
16.3;Static UML Model Generator from Analysis of Requirements (SUGAR);162
16.3.1;Overview;163
16.3.2;Architecture;163
16.4;Use Case Driven Object Oriented Analysis and Design;165
16.4.1;Use-case Model Generator;165
16.4.2;Class Model Generator;165
16.5;SUGAR- Case Study;166
16.5.1;Use Case Model Generation;167
16.5.2;Class Model Generation;168
16.6;Conclusions;170
16.7;References;171
17;A Hybrid Approach for Designing an Adaptive User Interface: IDSS and BDI Agents;173
17.1;Introduction;173
17.1.1;Motivation;174
17.2;Related Research;176
17.3;Decision Support Systems;178
17.3.1;Literature Survey;178
17.3.2;Intelligent Decision Support System (IDSS);180
17.3.3;Agent Integration in DSS;181
17.4;The Boiler Combustion Management System;182
17.5;Basic Notions in BDI Architectures in MAS;184
17.6;Proposed Approach;186
17.6.1;Architecture of the Adaptive User Interface Sub-system (IS);186
17.6.2;Adaptivity Mechanism;187
17.6.3;Implementing the UI Architecture;187
17.7;Conclusions and Future Work;189
17.8;References;190
18;Rough Kohonen Neural Network for Overlapping Data Detection;192
18.1;Introduction;192
18.2;Self Organizing Map;193
18.3;Rough Set Incremental Clustering;195
18.3.1;Incremental Clustering;195
18.3.2;Rough Set Incremental Clustering;196
18.4;Rough Set Clustering of the Self Organizing Map;198
18.5;Experimentation and Results;201
18.6;Conclusion and Future Work;204
18.7;References;204
19;Design and Implementation of a PC-Cluster Based Video-On-Demand Server;206
19.1;Introduction;206
19.2;Related Works;207
19.3;System Architecture and Video Strategy;209
19.4;Experimental Results;214
19.5;Conclusions;217
19.6;References;217
20;Using WordNet in Conceptual Query Expansion;219
20.1;Introduction;219
20.2;WordNet and WordNet::SenseRelate;220
20.2.1;Introduction to WordNet;220
20.2.2;About WordNet::SenseRelate Modules;221
20.3;Conceptual Query Expansion;222
20.3.1;Concept Recovery Process;222
20.3.2;Query Expansion Process;223
20.4;Experiment;225
20.5;Conclusion;226
20.6;References;226
21;Pre-processing and Step-Size Adaptation for Performance Improvement in ADM;228
21.1;Introduction;228
21.2;The Existing Step-Size Adaptations;230
21.2.1;SONG Algorithm;230
21.2.2;Modified ABATE Algorithm;231
21.3;The Proposed Step-Size Adaptation;232
21.4;Performance Comparison;234
21.4.1;SNR Calculation;234
21.5;Pre-processing;234
21.5.1;Performance Comparison with Pre-processor;237
21.6;Conclusion;238
21.7;References;239
22;Clustering Web Transactions Using Fuzzy Rough k Means;240
22.1;Introduction;240
22.2;Reviewal of Fuzzy Variable and Rough Variable;241
22.2.1;Fuzzy Variable;241
22.2.2;Rough Variable;242
22.3;Clustering Web Access Patterns by Rough k-Means Method in Fuzzy Environment;242
22.3.1;Characterizing User Access Patterns as Fuzzy User Access Patterns;243
22.3.2;Algorithm for Clustering Fuzzy Web Access Patterns Based on Rough k-Means;244
22.4;An Example;246
22.5;Conclusion;248
22.6;References;249
23;Author Index;250