Efficacious data cube exploration by semantic summarization and compression

Data cube is the core operator in data warehousing and OLAP. Its efficient computation, 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 approach is pivoted on a notion of quotient cube that groups together structurally related data cube cells with common (aggregate) measure values into equivalence classes. The equivalence relation used to partition the cube lattice preserves the rollup/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 between cells. We have also developed compact data structures for representing a quotient cube and efficient algorithms for answering queries using a quotient cube for its incremental maintenance against updates. We have implemented SOCQET, a prototype 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 warehouses and OLAP; (4) a user interface for visual and interactive OLAP; and (5) SOC-QET, a research prototype data warehousing system integrating all the techniques. The demo reflects our latest research results and may stimulate some interesting future studies.

Duke Scholars

Author Jian Pei Computer Science