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
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Many real-life applications use various kinds of clustering algorithms. Very popular and interesting are applications dealing with spatial data, like on-line map services or traffic tracking systems. A very important branch of spatial systems is telemetry. Our current research is focused on providing an efficient caching structure that will accelerate spatial queries evaluation and improve the ways of storing and processing aggregates. We use a density-based clustering algorithm to create the structure levels. The used clustering algorithm is fast and efficient but it requires a user-defined Eps parameter. As we cannot get the Eps parameter from the user for every level of the structure, we propose an Automatic Eps Calculation (AEC) algorithm which, based on the points distribution characteristics, is able to estimate the Eps parameter value. The algorithm is not limited to the telemetry-specific data and can be applied to any set of points located in a two-dimensional space. We describe in detail the algorithm operation, test results and possible algorithm improvements.