Location and Processing Aware Datacube Caching

Array databases are used to manage and query large N-dimensional arrays, such as sensor data, simulation models and imagery, as well as various time-series. Modern database systems and database applications make extensive use of caching techniques to improve performance. Research on array databases on the other hand has not explored the potential benefits of caching in query processing on big arrays. In this work we propose a design for a content-aware cache for array databases which allows to reuse results of previously evaluated queries. Besides identical query matching, our method also takes into account spatially overlapping queries and queries with common subexpressions. We evaluate performance of the query cache implementation by varying data and query parameters and show that it decreases query execution time by up to 93%, with a potential for even higher savings with increasing query complexity.