### 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.

Original language | English |
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Pages (from-to) | 236-253 |

Number of pages | 18 |

Journal | Lecture Notes in Computer Science |

Volume | 3633 |

Publication status | Published - 18 Oct 2005 |

Event | 9th International Symposium on Spatial and Temporal Databases, SSTD 2005 - Angra dos Reis, Brazil Duration: 22 Aug 2005 → 24 Aug 2005 |

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### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Lecture Notes in Computer Science*,

*3633*, 236-253.