Microsoft TerraServer: a spatial data warehouse
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
DBRS: a density-based spatial clustering method with random sampling
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
On efficient storing and processing of long aggregate lists
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
A novel method to find appropriate for ε for DBSCAN
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Shared execution strategy for neighbor-based pattern mining requests over streaming windows
ACM Transactions on Database Systems (TODS)
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Spatial information processing is an active research field in database technology. Spatial databases store information about the position of individual objects in space [6]. Our current research is focused on providing an efficient caching structure for a telemetric data warehouse. We perform spatial objects clustering when creating levels of the structure. For this purpose we employ a density-based clustering algorithm. The algorithm requires an user-defined parameter Eps. As we cannot get the Eps from user for every level of the structure we propose a heuristic approach for calculating the Eps parameter. Automatic Eps Calculation (AEC) algorithm analyzes pairs of points defining two quantities: distance between the points and density of the stripe between the points. In this paper we describe in detail the algorithm operation and interpretation of the results. The AEC algorithm was implemented in one centralized and two distributed versions. Included test results present the algorithm correctness and efficiency against various datasets.