Abstract
Data cube computation is a well-known expensive operation and has been studied extensively. It is often not feasible to compute a complete data cube due to the huge storage requirement. Recently proposed quotient cube addressed this fundamental issue through a partitioning method that groups cube cells into equivalent partitions. The effectiveness and efficiency of the quotient cube for cube compression and computation have been proved. However, as changes are made to the data sources, to maintain such a quotient cube is non-trivial since the equivalent classes in it must be split or merged. In this paper, incremental algorithms are designed to update existing quotient cube efficiently based on Galois lattice. Performance study shows that these algorithms are efficient and scalable for large databases.
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Supported by the National Natural Science Foundation of China under Grant No.60273017, the National High-Tech Research and Development Program of China under Grant No.2002AA4Z3420, the National Grand Fundamental Research 973 Program of China under Grant No.2001CCA03003, and the Important Scientific and Technical Research Project of Educational Sector under Grant. No.02036.
Cui-Ping Li received her B.S. and M.S. degrees in computer science from Xi'an Jiaotong University in 1994 and 1997 respectively. She is now a Ph.D. candidate of Institute of Computing Technology, the Chinese Academy of Sciences. Her current research interests include database systems, data warehouse, and data mining.
Kum-Hoe Tung received his B.Sc. and M.Sc. degrees in computer science from the National University of Singapore in 1997 and 1998 respectively. In 2001, he received the Ph.D. degree in computer science from Simon Fraser University (SFU). He is currently an assistant professor in the Department of Computer Science, National University of Singapore. His research interests involve various aspects of databases and data mining including buffer management, frequent pattern discovery, spatial clustering, outlier detection, and classification analysis.
Shan Wang received her B.S. degree from Pecking University in 1968 and her M.S. degree from the Renmin University of China in 1981. She is now the dean and a professor of the Information School, Renmin University of China. She is also a research fellow and a Ph.D. supervisor of the Institute of Computing Technology, the Chinese Academy of Sciences. Her research interests include database systems and knowledge engineering, mobile data management, and data warehousing.
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Li, CP., Tung, KH. & Wang, S. Incremental maintenance of quotient cube based on galois lattice. J. Comput. Sci. & Technol. 19, 302–308 (2004). https://doi.org/10.1007/BF02944900
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DOI: https://doi.org/10.1007/BF02944900