Abstract
Hierarchical clustering has been proved an effective means for physically organizing large fact tables since it reduces significantly the I/O cost during ad hoc OLAP query evaluation. In this paper, we propose a novel multidimensional file structure for organizing the most detailed data of a cube, the CUBE File. The CUBE File achieves hierarchical clustering of the data, enabling fast access via hierarchical restrictions. Moreover, it imposes a low storage cost and adapts perfectly to the extensive sparseness of the data space achieving a high compression rate. Our results show that the CUBE File outperforms the most effective method proposed up to now for hierarchically clustering the cube, resulting in 7-9 times less I/Os on average for all workloads tested. Thus, it achieves a higher degree of hierarchical clustering. Moreover, the CUBE File imposes a 2-3 times lower storage cost.
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References
Bayer, R.: The universal B-Tree for multi-dimensional Indexing: General Concepts. In: Masuda, T., Tsukamoto, M., Masunaga, Y. (eds.) WWCA 1997. LNCS, vol. 1274, Springer, Heidelberg (1997)
Chan, C.Y., Ioannidis, Y.E.: Bitmap Index Design and Evaluation. In: SIGMOD 1998 (1998)
Deshpande, P., Ramasamy, K., Shukla, A., Naughton, J.F.: Caching Multidimensional Queries Using Chunks. In: SIGMOD 1998 (1998)
Gray, J., Bosworth, A., Layman, A., Pirahesh, H.: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and SubTotal. In: ICDE 1996 (1996)
Karayannidis, N.: Storage Structures, Query Processing and Implementation of On-Line Analytical Processing Systems, Ph.D. Thesis, National Technical University of Athens (2003), Available at: http://www.dblab.ece.ntua.gr/~nikos/thesis/PhD_thesis_en.pdf
Karayannidis, N., Sellis, T.: SISYPHUS: The Implementation of a Chunk-Based Storage Manager for OLAP Data Cubes. Data and Knowledge Engineering 45(2), 155–188 (2003)
Karayannidis, N., et al.: Processing Star-Queries on Hierarchically-Clustered Fact-Tables. In: VLDB 2002 (2002)
Lakshmanan, L.V.S., Pei, J., Han, J.: Quotient Cube: How to Summarize the Semantics of a Data Cube. In: VLDB 2002 (2002)
Markl, V., Ramsak, F., Bayern, R.: Improving OLAP Performance by Multidimensional Hierarchical Clustering. In: IDEAS 1999 (1999)
O’Neil, P.E., Graefe, G.: Multi-Table Joins Through Bitmapped Join Indices. SIGMOD Record 24(3), 8–11 (1995)
Nievergelt, J., Hinterberger, H., Sevcik, K.C.: The Grid File: An Adaptable, Symmetric Multikey File Structure. TODS 9(1), 38–71 (1984)
O’Neil, P.E., Quass, D.: Improved Query Performance with Variant Indexes. In: SIGMOD 1997 (1997)
Pieringer, R., et al.: Combining Hierarchy Encoding and Pre-Grouping: Intelligent Grouping in Star Join Processing. In: ICDE 2003 (2003)
Ramsak, F., et al.: Integrating the UB-Tree into a Database System Kernel. In: VLDB 2000 (2000)
Sarawagi, S.: Indexing OLAP Data. Data Engineering Bulletin 20(1), 36–43 (1997)
Sismanis, Y., Deligiannakis, A., Roussopoulos, N., Kotidis, Y.: Dwarf: shrinking the PetaCube. In: SIGMOD 2002 (2002)
Sarawagi, S., Stonebraker, M.: Efficient Organization of Large Multidimensional Arrays. In: ICDE 1994 (1994)
The Transbase Hypercube® relational database system, http://www.transaction.de
Tsois, A., Sellis, T.: The Generalized Pre-Grouping Transformation: Aggregate- Query Optimization in the Presence of Dependencies. In: VLDB 2003 (2003)
Weber, R., Schek, H.-J., Blott, S.: A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces. In: VLDB 1998, pp. 194–205 (1998)
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Karayannidis, N., Sellis, T., Kouvaras, Y. (2004). CUBE File: A File Structure for Hierarchically Clustered OLAP Cubes. In: Bertino, E., et al. Advances in Database Technology - EDBT 2004. EDBT 2004. Lecture Notes in Computer Science, vol 2992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24741-8_36
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DOI: https://doi.org/10.1007/978-3-540-24741-8_36
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