2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
Suan Lee , Department of Computer Science, Kangwon National University, Chuncheon, Kangwon, Korea
Jinho Kim , Department of Computer Science, Kangwon National University, Chuncheon, Kangwon, Korea
This paper presents the performance evaluation of MRDataCube which we have previously proposed as an efficient algorithm for data cube computation with data reduction using MapReduce framework. We performed a large number of analyses and experiments to evaluate the MRDataCube algorithm in the MapReduce framework. In this paper, we compared it to simple MR-based data cube computation algorithms, e.g., MRNaive, MR2D as well as algorithms converted into MR paradigms from conventional ROLAP (relational OLAP) data cube algorithms, e.g., MRGBLP and MRPipeSort. From the experimental results, we observe that the MRDataCube algorithm outperforms the other algorithms in comparison tests by increasing the number of tuples and/or dimensions.
Algorithm design and analysis, Aggregates, Performance evaluation, Distributed databases, Parallel processing, Computers, Computer science
S. Lee and J. Kim, "Performance evaluation of MRDataCube for data cube computation algorithm using MapReduce," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 325-328.