We present a novel method of query execution in similarity based databases which adopts techniques commonly used in traditional programming language compilers. Our method is based on decomposition of relational algebra operators into a small set of simple operations which are subject of further optimizations. It shows up that with a small set of optimizations rules our system itself is able to infer efficient algorithms for data processing. Furthermore, operations we propose are compatible with the map/reduce approach to data processing, and thus, allows for implicitly parallel or distributed data processing.
CITATION STYLE
Krajča, P. (2015). Optimized and parallel query processing in similarity-based databases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9321, pp. 167–179). Springer Verlag. https://doi.org/10.1007/978-3-319-23240-9_14
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