Talia / Bilas Knowledge and Data Management in GRIDs

1. Auflage 2007
ISBN: 978-0-387-37831-2
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 254 Seiten

Current research activities are leveraging the Grid to create generic- and domain-specific solutions and services for data management and knowledge discovery. Knowledge and Data Management in Grids is the third volume of the CoreGRID series; it gathers contributions by researchers and scientists working on storage, data, and knowledge management in Grid and Peer-to-Peer systems. This volume presents the latest Grid solutions and research results in key areas such as distributed storage management, Grid databases, Semantic Grid and Grid-aware data mining. Written for a professional audience of researchers and practitioners in industry, it is suitable for graduate-level students in computer science.

Autoren/Hrsg.

Talia, Domenico

Bilas, Angelos

Inhaltsverzeichnis

1;Contents;6
2;Foreword;8
3;Preface;10
4;Contributing Authors;14
5;I GRID DATA MANAGEMENT;19
5.1;ACCESSING DATA IN GRIDS USING OGSA-DAI;21
5.1.1;1. Introduction;22
5.1.2;2. Web Services and Grids;23
5.1.3;3. Architectural Requirements for Data Middleware;24
5.1.4;4. An Overview of OGSA-DAI;25
5.1.5;5. Activities and Perform Documents;28
5.1.6;6. How OGSA-DAI is being Used;30
5.1.7;7. Related Work;31
5.1.8;8. Importance of Standards;32
5.1.9;9. Conclusions;33
5.1.10;Acknowledgments;34
5.1.11;References;34
5.2;SERVICE CHOREOGRAPHY FOR DATA INTEGRATION ON THE GRID;37
5.2.1;1. Introduction;38
5.2.2;2. Background;39
5.2.3;3. Architecture and Service Interactions;41
5.2.4;4. The XMAP Integration Framework;43
5.2.5;5. Combining query processing and reformulation services;44
5.2.6;6. Conclusions;49
5.2.7;Acknowledgments;50
5.2.8;References;50
5.3;ACCESSING WEB DATABASES USING OGSA-DAI IN BDWORLD*;53
5.3.1;1. Introduction;54
5.3.2;2. Generic Bioinformatics Data Access and Integration Requirements;56
5.3.3;3. BDWorld Data Integration Issues;58
5.3.4;4. The BioDA Exemplar;60
5.3.5;5. Conclusion;65
5.3.6;Acknowledgments;66
5.3.7;References;66
5.4;FAILURE RECOVERY ALTERNATIVES IN GRID- BASED DISTRIBUTED QUERY PROCESSING: A CASE STUDY;69
5.4.1;1. Introduction;70
5.4.2;2. Related Work;70
5.4.3;3. Recovery Options;72
5.4.4;4. Implementation;74
5.4.5;5. Experimental Results;77
5.4.6;6. Conclusions;79
5.4.7;Acknowledgments;80
5.4.8;References;80
6;II GRID DATA STORAGE;83
6.1;CONDUCTOR: SUPPORT FOR AUTONOMOUS CONFIGURATION OF STORAGE SYSTEMS;85
6.1.1;1. Introduction;86
6.1.2;2. Related work;88
6.1.3;3. System Overview;89
6.1.4;4. Initial Configuration Mode;91
6.1.5;5. Evaluation;96
6.1.6;6. Conclusions and future work;98
6.1.7;Acknowledgments;99
6.1.8;References;99
6.2;VIOLIN: A FRAMEWORK FOR EXTENSIBLE BLOCK-LEVEL STORAGE;101
6.2.1;1. Introduction;102
6.2.2;2. System Architecture;103
6.2.3;3. Advanced Virtualization Scenarios;111
6.2.4;4. Related Work;114
6.2.5;5. Conclusions;115
6.2.6;Acknowledgments;115
6.2.7;References;115
6.3;CLUSTERIX DATA MANAGEMENT SYSTEM (CDMS) - ARCHITECTURE AND USE CASES *;117
6.3.1;1. Introduction;118
6.3.2;2. Data Management System;118
6.3.3;3. System Architecture;124
6.3.4;4. System Interface;126
6.3.5;5. Integration of End-User Applications with CDMS;127
6.3.6;6. Integration with GRMS;127
6.3.7;7. Related work;131
6.3.8;8. Conclusions;132
6.3.9;References;132
7;III SEMANTIC GRID;135
7.1;ARCHITECTURAL PATTERNS FOR THE SEMANTIC GRID *;137
7.1.1;1. Introduction;138
7.1.2;2. Semantic Grid concepts;139
7.1.3;3. The Grid scheduling use case;141
7.1.4;4. Service interaction patterns for the Semantic Grid;145
7.1.5;5. Discussion;148
7.1.6;6. Future Directions;150
7.1.7;References;151
7.2;A METADATA MODEL FOR THE DISCOVERY AND EXPLOITATION OF SCIENTIFIC STUDIES;153
7.2.1;1. Introduction;154
7.2.2;2. A Science Data Portal;155
7.2.3;3. The Metadata Structure;157
7.2.4;4. Metadata Conformance;162
7.2.5;5. An Example;162
7.2.6;6. Conclusions and Future Development;165
7.2.7;Acknowledgments;166
7.2.8;References;166
7.3;IDEAS FOR THE PROVISION OF ONTOLOGY ACCESS IN GRID ENVIRONMENTS;169
7.3.1;1. Introduction;170
7.3.2;2. Lessons Learnt from the Semantic Web;170
7.3.3;3. Possibilities for Providing Ontology Access in the Grid;171
7.3.4;4. WS-DAIOnt: a Proposal of an Ontology Access Mechanism in the Grid;183
7.3.5;5. Conclusions;185
7.3.6;Acknowledgments;185
7.3.7;References;185
7.4;SEMANTIC SUPPORT FOR META-SCHEDULING IN GRIDS;187
7.4.1;1. Introduction;188
7.4.2;2. Requirements for the Scheduling Domain Knowledge Model;191
7.4.3;3. A Semantic Model for Grid Scheduling;192
7.4.4;4. Environment for Semantic Exploitation;196
7.4.5;5. Future Perspectives;199
7.4.6;Acknowledgments;199
7.4.7;References;200
7.5;SEMANTIC GRID RESOURCE DISCOVERY IN ATLAS*;203
7.5.1;1. Introduction;204
7.5.2;2. Related Work;205
7.5.3;3. The P2P System Atlas;206
7.5.4;4. Atlas in Operation: Service Discovery in OntoKit;213
7.5.5;5. Conclusions;215
7.5.6;References;215
8;IV DISTRIBUTED DATA MINING;219
8.1;WSRF-BASED SERVICES FOR DISTRIBUTED DATA MINING;221
8.1.1;1. Introduction;222
8.1.2;2. SOA and the WS-Resource Framework;223
8.1.3;3. WSRF-based Data Mining Services;224
8.1.4;4. Application Modeling and Representation;230
8.1.5;5. WSRF Service Execution Performance;235
8.1.6;6. Related work;236
8.1.7;7. Conclusions;237
8.1.8;Acknowledgments;237
8.1.9;References;237
8.2;MINING FREQUENT CLOSED ITEMSETS FROM DISTRIBUTED REPOSITORIES;239
8.2.1;1. Introduction.;240
8.2.2;2. Frequent and Closed Itemsets;241
8.2.3;3. Distributed Frequent Itemsets;243
8.2.4;4. Distributed Frequent Closed Itemsets;245
8.2.5;5. Conclusion;250
8.2.6;References;250
8.3;DISTRIBUTED DATA MINING AND KNOWLEDGE MANAGEMENT WITH NETWORKS OF SENSOR ARRAYS;253
8.3.1;1. Introduction;254
8.3.2;2. Industrial context;255
8.3.3;3. Data mining in TELEMAC;256
8.3.4;4. The Grids context;260
8.3.5;5. Grids based approach to TELEMAC;263
8.3.6;Acknowledgments;267
8.3.7;References;267
9;Index;269

Leseproben

1. Introduction (p. 20)

The Grid, as an emerging infrastructure for the discovery, access and use of distributed computational resources [15], offers new opportunities and raises new challenges in data management. Many aspects differentiate the Grid from a traditional distributed environment, such aspects include the large scale, dynamic, autonomous, and distributed nature of data sources.

A Grid can include related data resources maintained in different syntaxes, managed by different software systems, and accessible through different protocols and interfaces. Due to this diversity in data resources, one of the most demanding issue in managing data on Grids is reconciliation of data heterogeneity [8].

Therefore, in order to provide facilities for addressing requests over multiple heterogeneous data sources, it is necessary to provide data integration models and mechanisms.

Data integration is one of the most persistent problems that the database and information management community has to deal with. Although significant progress has been made in several aspects of data integration, the increase in availability of web-based data sources has led to new challenges. More specif- ically, efficient techniques have been developed and approaches have been devised to schema mediation languages, query answering algorithms, optimisation strategies, query execution policies, industrial development, and so on [17].

However, effective techniques for the generation and handling of semantic mappings are still in their infancy. The need for semantic correlation of data sources is particularly felt in Grid settings. Moreoever, in a Grid, a centralized structure for coordinating all the nodes may not be practical because it can be- come a bottleneck and, more importantly, it cannot accommodate the dynamic and distributed nature of Grid resources.

Data access and integration services have been attracting significant interest from the Grid community. Data Grids that rely on the coordinated sharing of and interaction across multiple autonomous database management systems play a key role in many industrial and scientific initiatives. To this end, middleware services have been developed.

Two notable examples are the OGSA Data Access and Integration (OGSA-DAI) [6] and the OGSA Distributed Query Processor (OGSA-DQP)' [5,4] projects. These projects have moved toward a servide-oriented architecture quite early in their lifecycle. OGSA-DAI exposes database management systems (including Oracle, MySQL, SQLServer, DB2, and so on) in a uniform way, whereas OGSA-DQP provides distributed query processing functionalities on top of OGSA-DAI. As such, OGSA-DQP can combine and integrate data from multiple data sources. To enhance performance, it employs parallel query execution techniques, nevertheless it relies on the user for the semantic interpretation of the data and does not address any schema integration requirements.

To date, only few projects (e.g., [ l l , 91) actually meet the schemaintegration requirements that are necessary for establishing semantic connections among heterogeneous data sources. To address this limitation, the use of the XMAP framework for integrating heterogeneous data sources distributed over a Grid has been proposed [12] .