Abstract
Data is typically incorporated in a data warehouse in increasing order of time. Furthermore, the MOLAP data cube tends to be sparse because of the large cardinality of the time dimension. We propose an approach to improve the efficiency of range aggregate queries on MOLAP data cubes in a temporal data warehouse by factoring out the time-related dimensions. These time-related dimensions are handled separately to take advantage of the monotonic trend over time. The proposed technique captures local data trends with respect to time by partitioning data points into blocks, and then uses a perfect binary block tree as an index structure to achieve logarithmic time complexity for both incremental updates and data retrievals. Experimental results establish the scalability and efficiency of the proposed approach on various datasets.
This work has been supported by the NSF under grant numbers CNF-04-23336, IIS-02-23022 and EIA-00-80134.
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Feng, Y., Li, HG., Agrawal, D., El Abbadi, A. (2005). Exploiting Temporal Correlation in Temporal Data Warehouses. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_61
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DOI: https://doi.org/10.1007/11408079_61
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