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SUMMARY
With new forms of information production, the increase in available data grows at an ever increasing exponential rate. As a result, multi-source data has become increasingly popular in a research context and has had a strong impact on database development and compilation. As data quantities derived from internet-connect objects grows more massive, spatio-temporal data has become more important. As a result, problems arise in terms of the space-time semantics of the data models and need to integrate specific techniques for data-processing.
In this context, the Data Integration and Mining team is interested in the problems of multi-source data access and data mining of data from the web or from temporal (RSS, sensors) or spatiotemporal (moving objects) flows. The team’s research interests range from logical models, languages and algorithms in databases and data mining, to physical data access optimizations.
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SOLUTIONS
- Developing an algebraic model for imperfect and multi-granualar data and knowledge integration, incorporating space-time semantics
- Constructing a data model and query language is expressive and contains continuous spatio-temporal qualities
- Creating specialized access methods, algorithms, query evaluation and optimised architecture designs which are robust with respect to flows and capable of identifying new problems, adapting to operators and
- Developing approaches and algorithms for discovering temporal and spatiotemporal patterns, evaluation and optimization of data processing
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LINK: http://www.prism.uvsq.fr/index.php?option=com_content&view=article&id=64&Itemid=164
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