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 News
Colloquia
Integrating OLAP and Ranking: The Ranking-Cube Methodology
Dong Xin
Computer Sciences department
University of Illinois at Urbana-Champaign
Wednesday, March 21, 2007
Recent years have witnessed an enormous growth of data in business,
industry, and Web applications. Database search often returns a large
collection of results, which poses challenges to both efficient query
processing and effective digest of the query results. To address this
problem, ranked search has been introduced to database systems. We
study the problem of On-Line Analytical Processing (OLAP) of ranked
queries, where ranked queries are conducted in the arbitrary subset of
data defined by multi-dimensional selections. While pre-computation and
multi-dimensional aggregation is the standard solution for OLAP,
materializing dynamic ranking results is unrealistic because the ranking
criteria are not known until the query time. To overcome such
difficulty, we first develop a new ranking cube method that performs
semi off-line materialization and semi online computation, and then
extend it to high-dimensional data. Our performance studies show that
Ranking-Cube is orders of magnitude faster than previous approaches. In
this talk, I will also provide a brief overview of my other research
work in data mining.
Administrative support: Jacky Carley (x8291)
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