By Alfredo Cuzzocrea
Clever suggestions for Warehousing and Mining Sensor community facts provides basic and theoretical concerns referring to information administration. protecting a wide diversity of issues on warehousing and mining sensor networks, this complex identify offers major strategies to these in database, info warehousing, and information mining learn groups.
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Mining time-changing data streams. In Proceedings of the Seventh ACM SIGKDD international Conference on Knowledge Discovery and Data Mining (pp. 97-106). , & Yalamanchi, A. (2008). Using Oracle Extensibility Framework for Supporting Temporal and Spatio-Temporal Applications. In Proceedings of the fifteenth International Symposium on Temporal Representation and Reasoning (pp. 15-18). , & Hobbs, L. (2008). Comparing materialized views and analytic workspaces in Oracle Database 11g. oracle. , & Greenwald, R.
On demand classification of data streams. In Proceedings of the Tenth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining (pp. 503-508). , & Widom, J. (2002). Models and issues in data stream systems. In Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. , & Widom, J. (2001). Continuous queries over data streams. SIGMOD Record, 30(3), 109–120. 603884 15 Integrated Intelligence Brand, M. (2006). Fast low-rank modifications of the thin singular value decomposition.
Basically, this process consists in flattening a multidimensional data cube model onto a twodimensional OLAP view model, whose dimensions, called flattening dimensions, are selected from the original dimension set in dependence on specific application requirements. The OLAP dimension flattening process is finally performed via systematically merging original hierarchies defined on dimensions of the multidimensional data stream model. The final flattening dimensions are thus equipped with specialized hierarchies generated in dependence on application-oriented requirements.