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WIDM 2003

Efficacious Data Cube Exploration by Semantic Summarization and Compression


Laks V. S. Lakshmanan, Jian Pei, and Yan Zhao

  View Paper (PDF)  

Return to Advanced Models and Languages/Architectures for Data Analysis (Session D3)


Abstract

Data cube is the core operator in data ware- housing and OLAP. Its efficient computa- tion, maintenance, and utilization for query answering and advanced analysis have been the subjects of numerous studies. However, for many applications, the huge size of the data cube limits its applicability as a means for semantic exploration by the user. Recently, we have developed a systematic approach to achieve efficacious data cube construction and exploration by semantic summarization and compression. Our ap- proach is pivoted on a notion of quotient cube that groups together structurally re- lated data cube cells with common (ag- gregate) measure values into equivalence classes. The equivalence relation used to partition the cube lattice preserves the roll- up/drill-down semantics of the data cube, in that the same kind of explorations can be conducted in the quotient cube as in the original cube, between classes instead of be- tween cells. We have also developed com- pact data structures for representing a quo- tient cube and efficient algorithms for an- swering queries using a quotient cube for its incremental maintenance against updates. We have implemented SOCQET, a proto- type data warehousing system making use of our results on quotient cube. In this demo, we will demonstrate (1) the critical techniques of building a quotient cube; (2) use of a quotient cube to answer various queries and to support advanced OLAP; (3) an empirical study on the effectiveness and efficiency of quotient cube-based data ware- houses and OLAP; (4) a user interface for visual and interactive OLAP; and (5) SOC- QET, a research prototype data warehous- ing system integrating all the techniques. The demo reflects our latest research results and may stimulate some interesting future studies.


©2004 Association for Computing Machinery