Dealing with granularity of time in temporal databases
CAiSE '91 Proceedings of the third international conference on Advanced information systems engineering
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Shifts in Detail through Temporal Zooming
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Scalable Algorithms for Large Temporal Aggregation
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Journal of Intelligent Information Systems
Modeling Fundamental Geo-Raster Operations with Array Algebra
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
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A frequent operation in e-Science is downscaling of some data item or part thereof, such as obtaining a 1 GB overview from a 10 TB dataset. Scaling is expensive as it normally requires a full scan of the area. Speeding up such operations, therefore, is performance critical. A common optimization technique used for map imagery is to materialize selected downscaled versions. However, there is no support for 3D, such as x/y/t timeseries or x/y/z geophysics data. To overcome this, we propose a preaggregation technique for multi-dimensional gridded ("raster") data. Preaggregates are selected based on a given query workload while considering disk space constraints. Upon evaluation, queries use the next best preaggregate and perform the remaining scaling. We present the preaggregate selection algorithm and argue its efficiency based on a performance analysis covering 2-D and 3-D use cases. Further, we show how our approach outperforms the well-known 2-D image pyramids widely used in Web mapping.