Relational DBMSs (RDBMSs) remain the most popular tool for processing structured data in data intensive domains. However, most of stand-alone data mining packages process flat files outside a RDBMS. In-database data mining avoids export-import data/results bottleneck as opposed to use stand-alone mining packages and keeps all the benefits provided by a RDBMS. The paper presents an approach to data mining inside a RDBMS based on a parallel implementation of user-defined functions (UDFs). Such an approach is implemented for PostgreSQL and modern Intel MIC (Many Integrated Core) architecture. The UDF performs a single mining task on data from the specified table and produces a resulting table. The UDF is organized as a wrapper of an appropriate mining algorithm, which is implemented in C language and is parallelized by the OpenMP technology and thread-level parallelism. The heavy-weight parts of the algorithm are additionally parallelized by intrinsic functions for MIC platforms to reach the optimal loop vectorization manually. The library of such UDFs supports a cache of precomputed mining structures to reduce costs of further computations. In the experiments, the proposed approach shows good scalability and overtakes R data mining package.
CITATION STYLE
Rechkalov, T., & Zymbler, M. (2018). Integrating DBMS and parallel data mining algorithms for modern many-core processors. In Communications in Computer and Information Science (Vol. 822, pp. 230–245). Springer Verlag. https://doi.org/10.1007/978-3-319-96553-6_17
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