Abstract
Ranking-aware queries have been gaining much attention recently in many applications such as search engines and data streams. They are, however, not only restricted to such applications but are also very useful in OLAP applications. In this paper, we introduce aggregation ranking queries in OLAP data cubes motivated by an online advertisement tracking data warehouse application. These queries aggregate information over a specified range and then return the ranked order of the aggregated values. They differ from range aggregate queries in that range aggregate queries are mainly concerned with an aggregate operator such as SUM and MIN/MAX over the selected ranges of all dimensions in the data cubes. Existing techniques for range aggregate queries are not able to process aggregation ranking queries efficiently. Hence, in this paper we propose new algorithms to handle this problem. The essence of the proposed algorithms is based on both ranking and cumulative information to progressively rank aggregation results. Furthermore we empirically evaluate our techniques and the experimental results show that the query cost is improved significantly.
This research is supported by the NSF grants under IIS-23022, CNF-0423336, and EIA-00-80134.
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Li, HG., Yu, H., Agrawal, D., El Abbadi, A. (2005). Progressive Ranking of Range Aggregates. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_18
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DOI: https://doi.org/10.1007/11546849_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
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