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|Title:||Evaluation of top-k OLAP queries using aggregate R-trees||Authors:||Mamoulis, N.
|Issue Date:||2005||Citation:||Mamoulis, N.,Bakiras, S.,Kalnis, P. (2005). Evaluation of top-k OLAP queries using aggregate R-trees. Lecture Notes in Computer Science 3633 : 236-253. ScholarBank@NUS Repository.||Abstract:||A top-κ OLAP query groups measures with respect to some abstraction level of interesting dimensions and selects the κ groups with the highest aggregate value. An example of such a query is "find the 10 combinations of product-type and month with the largest sum of sales". Such queries may also be applied in a spatial database context, where objects are augmented with some measures that must be aggregated according to a spatial division. For instance, consider a map of objects (e.g., restaurants), where each object carries some non-spatial measure (e.g., the number of customers served during the last month). Given a partitioning of the space into regions (e.g., by a regular grid), the goal is to find the regions with the highest number of served customers. A straightforward method to evaluate a top-κ OLAP query is to compute the aggregate value for each group and then select the groups with the highest aggregates. In this paper, we study the integration of the top-κ operator with the aggregate query processing module. For this, we make use of spatial indexes, augmented with aggregate information, like the aggregate R-tree. We device a branch-and-bound algorithm that accesses a minimal number of tree nodes in order to compute the top-κ groups. The efficiency of our approach is demonstrated by experimentation. © Springer-Verlag Berlin Heidelberg 2005.||Source Title:||Lecture Notes in Computer Science||URI:||http://scholarbank.nus.edu.sg/handle/10635/40466||ISSN:||03029743|
|Appears in Collections:||Staff Publications|
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