The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Maintenance of data cubes and summary tables in a warehouse
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An alternative storage organization for ROLAP aggregate views based on cubetrees
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Progressive approximate aggregate queries with a multi-resolution tree structure
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Analysis of pre-computed partition top method for range top-k queries in OLAP data cubes
Proceedings of the eleventh international conference on Information and knowledge management
Modeling Multidimensional Databases
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Selective Materialization: An Efficient Method for Spatial Data Cube Construction
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Pre-aggregation in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
Extracting k most important groups from data efficiently
Data & Knowledge Engineering
Processing Aggregate Queries on Spatial OLAP Data
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Efficient Online Aggregates in Dense-Region-Based Data Cube Representations
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Efficient online aggregates in dense-region-based data cube representations
Transactions on large-scale data- and knowledge-centered systems II
Efficient online aggregates in dense-region-based data cube representations
Transactions on large-scale data- and knowledge-centered systems II
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A top-k OLAP query groups measures with respect to some abstraction level of interesting dimensions and selects the k 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-k 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-k 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-k groups. The efficiency of our approach is demonstrated by experimentation.