This part of the work is dedicated to methods and tools for the discovery of latent knowledge from aggregated Web data. The work includes investigations in the inherent dynamics of Web sources and dependencies among them. Our research aims to develop algorithms and software to systematically aggregating, querying, mining and analyzing statistical partners, cross data dependencies, and temporal variabilities in order to reveal latent knowledge In Web sources. Following the development of the others areas, the key contributions will be:
Web-scale methods for data aggregation, querying and pattern mining, comprehensive temporal cross-data analytics, latent knowledge discovery along the time dimension. Results of this work package contribute to the applications test cases. Improvements archived by this work will be measurable in the quantity of agreeable data end the quality of latent knowledge from aggregated Web sources.