Advances in the design of the BANG file
3rd International Conference, FODO 1989 on Foundations of Data Organization and Algorithms
A well-behaved file structure for the storage of spatial objects
SSD '90 Proceedings of the first symposium on Design and implementation of large spatial databases
A general solution of the n-dimensional B-tree problem
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
On the analysis of indexing schemes
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A lower bound theorem for indexing schemes and its application to multidimensional range queries
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Tight bounds for 2-dimensional indexing schemes
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
On multi-scale display of geometric objects
Data & Knowledge Engineering
Design and Implementation of Multi-scale Databases
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Multiresolution amalgamation: dynamic spatial data cube generation
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Positions, regions, and clusters: strata of granularity in location modelling
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Semantic caching for multiresolution spatial query processing in mobile environments
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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It is frequently the case that spatial queries require a result set of objects whose scale - however this may be more precisely defined - is the same as that of the query window. In this paper we present an approach which considerably improves query performance in such cases. By adding a scale dimension to the schema we make the index structure explicitly "aware" of the scale of a spatial object. The additional dimension causes the index structure to cluster objects not only by geographic location but also by scale. By matching scales of the query window and the objects, the query then automatically considers only "relevant" objects. Thus, for example, a query window encompassing an entire world map of political boundaries might return only national borders. Note that "scale" is not necessarily synonymous with "size". This approach improves performance by both narrowing the initial selection criteria and by eliminating the need for subsequent filtering of the query result. In our performance measurements on databases with up to 40 million spatial objects, the introduction of a scale dimension decreased I/O by up to 4 orders of magnitude. The performance gain largely depends on the object scale distribution.We investigate a broad set of parameters that affect performance and show that many typical applications could benefit considerably from this technique. Its scalability is demonstrated by showing that the benefit increases with the size of the query and/or of the database. The technique is simple to apply and can be used with any multidimensional index structure that can index spatial extents and can be efficiently generalized to three or more dimensions. In our tests we have used the BANG index structure.