Abstract
R & D Organizations handling many Research and Development projects produce a very large amount of Scientific and Technical data. The analysis and interpretation of these data is crucial for the proper understanding of Scientific / Technical phenomena and discovery of new concepts. Data warehousing using multidimensional view and on-line analytical processing (OLAP) have become very popular in both business and science in recent years and are essential elements of decision support, analysis and interpretation of data. Data warehouses for scientific purposes pose several great challenges to existing data warehouse technology. This paper provides an overview of scientific data warehousing and OLAP technologies, with an emphasis on their data warehousing requirements. The methods that we used include the efficient computation of data cubes by integration of MOLAP and ROLAP techniques, the integration of data cube methods with dimension relevance analysis and data dispersion analysis for concept description and data cube based multi-level association, classification, prediction and clustering techniques.
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Sreenivasarao, V., Pallamreddy, V.S. (2011). Advanced Data Warehousing Techniques for Analysis, Interpretation and Decision Support of Scientific Data. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Advances in Computing and Information Technology. ACITY 2011. Communications in Computer and Information Science, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22555-0_18
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DOI: https://doi.org/10.1007/978-3-642-22555-0_18
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