C4.5: programs for machine learning
C4.5: programs for machine learning
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
The grid
The functional guts of the Kleisli query system
ICFP '00 Proceedings of the fifth ACM SIGPLAN international conference on Functional programming
Don't Scrap It, Wrap It! A Wrapper Architecture for Legacy Data Sources
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Grid-Based Knowledge Discovery Services for High Throughput Informatics
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Kleisli, a functional query system
Journal of Functional Programming
Designing grid services for distributed knowledge discovery
Web Intelligence and Agent Systems
Architecture design of grid GIS and its applications on image processing based on LAN
Information Sciences—Informatics and Computer Science: An International Journal
KDE bioscience: platform for bioinformatics analysis workflows
Journal of Biomedical Informatics
Distributed multi-join query processing in data grids
Information Sciences: an International Journal
Preference-Function Algorithm: a novel approach for selection of the users' preferred websites
International Journal of Business Intelligence and Data Mining
International Journal of Data Mining and Bioinformatics
A non-linear index to evaluate a journal's scientific impact
Information Sciences: an International Journal
Adapting the weka data mining toolkit to a grid based environment
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Semantically rich materialisation rules for integrating heterogeneous databases
BNCOD'05 Proceedings of the 22nd British National conference on Databases: enterprise, Skills and Innovation
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Many scientific experiments produce large amounts of data using high-throughput devices. Knowledge Discovery systems are used in order to analyse this type of data. However, generic laboratory systems do not provide any contextual information about the system that is being studied. In these situations, Knowledge Discovery can be aided and validated by the use of Information integration tools. In this paper, we introduce InfoGrid, a data integration middleware engine, designed to operate under a Grid framework. It focuses on providing information access services and offers all users a query system which is able to retain the familiarity with their specific scientific applications while being diverse, flexible and open at the same time. The assumption here is that defining a common language for all queries is not desirable.Using this design, we show how the InfoGrid architecture can be used to provide contextual features for a data table to be used for analysis (i.e. the Annotation Problem). We also show how it can be used to find relevant background knowledge for a user (i.e. the Information Comprehension Problem). Both of these issues are very often encountered in Knowledge Discovery tasks, which we illustrate by means of a real-world example.