Community Research and Development Information Service - CORDIS


Final Report - LEMPEM (Web graph: learning models for prediction and evolution monitoring)

Project ID: 224909
Funded under: FP7-PEOPLE


As web data are voluminous and distributed, we have argued that dimensionality reduction techniques should be employed to ensure efficient processing of the data. Data and web mining tasks results are usually improved by reducing the dimensionality of data.

According to the results achieved by the project the main conclusion arrived at is that dimensionality reduction can credibly decrease the complexity and partially increase the quality of data mining in large and distributed data. The obtained results show the suitability and viability of their approach for knowledge discovery in distributed environments as the web inherently is.

The potential of the project's results is promising as the researcher has developed collaborations with the web search and telecom industry (i.e. Lucent Alcatel, Exalead, Google research). The industrial partners are interested in the research results produced in the area of localised and real time web personalisation. Also, the project results in the area of web services and data management have been exploited in a local industrial research and development competitive proposal that resulted into a project in the area of web service brokers for car reservations.

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