Integrating OLAP and Ranking
The Ranking-Cube Methodology
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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 standa...
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 develop a new ranking cube method that performs semi off-line materialization and semi online computation. We further extend the ranking cube in three dimensions. First, how to answer queries in high-dimensional data. Second, how to answer queries which involves joins over multiple relations. Third, how to answer general preference queries (e.g., skyline queries).
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