Cordeiro / Shishkov / Ranchordas Software and Data Technolgoies

Third International Conference, ICSOFT 2008, Porto, Portugal, July 22-24, 2008
1. Auflage 2009
ISBN: 978-3-642-05201-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 47, 242 Seiten

Reihe: Communications in Computer and Information Science

This book contains the best papers of the third International Conference on Software and Data Technologies, ICSOFT 2008, held in Porto, Portugal in July 2008. The purpose of ICSOFT 2008 was to bring together researchers, engineers and practitioners interested in information technology and software development. The conference tracks were: software engineering, information systems and data management, programming languages, distributed and parallel systems, and knowledge engineering.

Autoren/Hrsg.

Cordeiro, José

Shishkov, Boris

Ranchordas, Alpeshkumar

Helfert, Markus

Inhaltsverzeichnis

1;Preface;5
2;Organization;6
3;Table of Contents;10
4;Invited Speakers;10
4.1;User Defined Geo-referenced Information;12
4.1.1;Introduction;12
4.1.2;Hovering Information;13
4.1.2.1;Overview of the Hovering Information Concept;14
4.1.2.2;Analysis of the Hovering Information Concept;15
4.1.3;End-to-End QoS-Predictions’ Information Service;16
4.1.3.1;Basic Assumptions;17
4.1.3.2;Overview of the Concept and Service;17
4.1.4;Conclusions;18
4.1.5;References;19
5;Part I: Programming Languages;10
5.1;Extending the SSCLI to Support Dynamic Inheritance;20
5.1.1;Introduction;20
5.1.2;Background;22
5.1.2.1;Type Systems of Dynamically Typed Languages;22
5.1.2.2;Runtime Performance of Dynamically Typed Languages;22
5.1.2.3;Structural Reflection;23
5.1.2.4;$\Large{\textrevscr}{\small\textsc{Rotor}}$;23
5.1.2.5;The Object Model of $\Large{\textrevscr}{\small\textsc{Rotor}}$;24
5.1.2.6;Dynamic Inheritance;25
5.1.3;Design;25
5.1.3.1;Class-Based Model;26
5.1.3.2;Prototype-BasedModel;28
5.1.4;Implementation;29
5.1.4.1;The $setSuper$ Primitive Interface;29
5.1.4.2;Instance Manipulation;30
5.1.4.3;Class Manipulation;30
5.1.5;Current State of the Implementation;31
5.1.6;Conclusions;31
5.1.7;References;32
5.2;Scala Roles: Reusable Object Collaborations in a Library;34
5.2.1;Introduction;34
5.2.2;Objects and Roles;36
5.2.3;Compound Objects with Dynamic Proxies;37
5.2.3.1;Object Identity;39
5.2.3.2;Collaborations with Nested Types;40
5.2.4;Scala Roles in Action;41
5.2.4.1;Persons and Their Roles;42
5.2.4.2;Composite Design Pattern;43
5.2.5;Related Work;45
5.2.6;Conclusions;45
5.2.7;References;46
6;Part II: Software Engineering;10
6.1;Common Criteria Based Security Scenario Verification;48
6.1.1;Introduction;48
6.1.2;Scenario Language;49
6.1.2.1;Outline;49
6.1.2.2;Scenario Example;50
6.1.3;Verification of Scenarios;51
6.1.3.1;Rule;51
6.1.3.2;Evaluation;53
6.1.3.3;Rule Based on Common Criteria;54
6.1.4;Discussion;56
6.1.5;Related Works;56
6.1.6;Conclusions;57
6.1.7;References;57
6.2;A Software Infrastructure for User–Guided Quality–of–Service Tradeoffs;59
6.2.1;Introduction;59
6.2.2;Architectural Baseline;61
6.2.3;Quality–of–Service Tradeoffs;63
6.2.3.1;Evaluation of Usability;65
6.2.4;Coordinating Resource Usage;67
6.2.4.1;Evaluation;68
6.2.5;Adaptive Applications;69
6.2.5.1;Evaluation;69
6.2.6;Related Work;70
6.2.7;Conclusions;71
6.2.8;References;71
6.3;On the Multiplicity Semantics of the Extend Relationship in Use Case Models;73
6.3.1;Introduction;73
6.3.2;The Evolution of the Extend Relationship;74
6.3.3;The Interpretation of the Extension Point Concept;77
6.3.4;Discussion;81
6.3.5;Related Work;83
6.3.6;Conclusions and Future Work;84
6.3.7;References;85
6.4;Secure Mobile Phone Access to Remote Personal Computers: A Case Study;87
6.4.1;Introduction;87
6.4.2;Background;88
6.4.3;Security Issues;89
6.4.4;Proposed Security Model;90
6.4.5;Improving the Design;91
6.4.6;Implementation;93
6.4.6.1;Discussion;94
6.4.7;User Study;95
6.4.8;Related Work;97
6.4.9;Conclusions;98
6.4.10;References;99
7;Part III: Distributed and Parallel Systems;10
7.1;Understanding and Evaluating Replication in Service Oriented Multi-tier Architectures;102
7.1.1;Introduction;102
7.1.2;Replication in SOA;103
7.1.2.1;Basic Architecture;103
7.1.2.2;Replication;104
7.1.3;Replication Strategies;105
7.1.3.1;Categorization;105
7.1.3.2;Example Algorithm;108
7.1.4;Simulation;109
7.1.4.1;Simulation Architecture;109
7.1.4.2;Execution;111
7.1.4.3;Parameters;112
7.1.5;Selected Results;113
7.1.6;Related Work;114
7.1.7;Conclusions;114
7.1.8;References;115
7.2;Applying Optimal Stopping for Optimizing Queries to External Semantic Web Resources;116
7.2.1;Introduction;116
7.2.2;Performance and Scalability;117
7.2.3;The Search Test Stop Model;119
7.2.3.1;Preconditions;120
7.2.3.2;The Discrete Search Test Stop Model;120
7.2.4;Method;121
7.2.4.1;Cost Functions;121
7.2.4.2;Utility Distributions;121
7.2.4.3;Application;122
7.2.5;Evaluation;124
7.2.5.1;Performance;125
7.2.5.2;Web Services;126
7.2.6;Outlook and Conclusions;128
7.2.7;References;128
7.3;An Efficient Pipelined Parallel Join Algorithm on Heterogeneous Distributed Architectures;130
7.3.1;Introduction;130
7.3.2;Limitations of Parallel Execution Strategies in Multi-join Queries;131
7.3.2.1;Sequential Parallel Execution;132
7.3.2.2;Parallel Synchronous Execution;132
7.3.2.3;Segmented Right-Deep Execution;133
7.3.2.4;Full Parallel Execution;134
7.3.3;Parallelism in Multi-join Queries Using PDFA-Join Algorithm;134
7.3.3.1;Detailed Algorithm;135
7.3.3.2;Discussion;141
7.3.4;Conclusions;143
7.3.5;References;143
8;Part IV: Information Systems and Data Management;11
8.1;Declarative Business Process Modelling and the Generation of ERP Systems;145
8.1.1;Introduction;145
8.1.2;An Introduction to REA;147
8.1.3;An Object-Z Model;148
8.1.3.1;The REA Meta-model;148
8.1.3.2;The Runtime Model;150
8.1.4;A Domain Specific Language;151
8.1.4.1;Language Implementation;153
8.1.5;Automated Generation of Applications;154
8.1.6;Summary;156
8.1.7;References;157
8.2;Single Vector Large Data Cardinality Structure to Handle Compressed Database in a Distributed Environment;158
8.2.1;Introduction;158
8.2.2;Related Work;159
8.2.2.1;Existing HIBASE Compression Technique;160
8.2.3;Proposed Single Vector Large Data Cardinality (SVLDCS) Structure;161
8.2.4;Searching Technique;164
8.2.4.1;Explanation of the Searching Technique of (SVLDCS) Structure;165
8.2.4.2;Searching Time Analysis of SVLDC Structure;166
8.2.5;The Analysis of Storage Capacity of SVLDC Structure;166
8.2.5.1;Analytical Analysis of Storage Capacity Using Different Methods;167
8.2.6;Conclusions;169
8.2.7;References;170
8.3;Relaxed Approaches for Correct DB-Replication with SI Replicas;172
8.3.1;Introduction;172
8.3.2;Multiversion Histories;174
8.3.3;Generalized Snapshot Isolation;175
8.3.4;The Deferred Update Technique;177
8.3.5;1–Copy–GSI Schedules;180
8.3.6;Relaxing Assumptions;181
8.3.7;Conclusions;184
8.3.8;References;185
8.4;Measuring the Usability of Augmented Reality e-Learning Systems: A User–Centered Evaluation Approach;186
8.4.1;Introduction;186
8.4.2;Related Work;187
8.4.3;Evaluation Method and Procedure;189
8.4.3.1;Equipment;189
8.4.3.2;Participants and Tasks;190
8.4.3.3;Method and Procedure;190
8.4.4;Evaluation Results and Comparison;191
8.4.4.1;Answers to the Questionnaire;191
8.4.4.2;Most Mentioned Positive and Negative Aspects;192
8.4.4.3;Measures of Effectiveness and Efficiency;194
8.4.5;Conclusions and Future Work;195
8.4.6;References;196
8.5;Supporting the Process Assessment through a Flexible Software Environment;198
8.5.1;Introduction;198
8.5.2;State of the Art;199
8.5.3;EvalTOOL;201
8.5.3.1;Process Model Management;203
8.5.3.2;Application and Evaluation Model;203
8.5.4;EvalTOOL Application Example;205
8.5.5;EvalTOOL as Support to COMPETISOFT;207
8.5.6;Conclusions and Future Work;209
8.5.7;References;210
9;Part V: Knowledge Engineering;11
9.1;Increasing Data Set Incompleteness May Improve Rule Set Quality;211
9.1.1;Introduction;211
9.1.2;Blocks of Attribute-Value Pairs;213
9.1.3;Definability;215
9.1.4;Lower and Upper Approximations;215
9.1.5;LERS and LEM2;218
9.1.5.1;LEM2;218
9.1.5.2;LERS Classification System;219
9.1.6;Experiments;220
9.1.7;Conclusions;225
9.1.8;References;226
9.2;Anomaly Detection Using Behavioral Approaches;228
9.2.1;Introduction;228
9.2.2;Behavioral Approaches for Intrusion Detection;229
9.2.2.1;Why Standard Classification Rules Are Ineffective for Detecting Novel Attacks;230
9.2.3;Enhancing Bayesian Classification for Anomaly Detection;231
9.2.3.1;Using Zero Probabilities as Abnormal Evidence;232
9.2.3.2;Using Likelihood of Rare Attacks as Abnormal Evidence;233
9.2.4;Enhancing Decision Trees for Anomaly Detection;234
9.2.4.1;Decision Tree Classifiers;234
9.2.4.2;Decision Tree Adaptations for Anomaly Detection;234
9.2.5;Experimental Studies;237
9.2.5.1;Training and Testing Data Sets;237
9.2.5.2;Experiments on Standard/Enhanced Bayesian Classification Rule;238
9.2.6;Conclusions;239
9.2.7;References;240
10;Author Index;242