Implementing vertical splitting for large scale multidimensional datasets and its evaluations

  • Authors:

  • Takayuki Tsuchida;Tatsuo Tsuji;Ken Higuchi

  • Affiliations:

  • Graduate School of Engineering, University of Fukui, Fukui, Japan;Graduate School of Engineering, University of Fukui, Fukui, Japan;Graduate School of Engineering, University of Fukui, Fukui, Japan

  • Venue:
  • DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
  • Year:
  • 2011

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Abstract

History-offset encoding we are proposing is a scheme for encoding multidimensional datasets. In general, significant problems in implementing multidimensional databases include the saturation of address space for addressing multidimensional data. One of the solutions against this problem is splitting the dimension attributes of the multidimensional data into more than one group; i.e., vertical splitting. We have implemented the vertical splitting scheme for large scale multidimensional datasets based on the history-offset encoding. In this paper, we describe implementation of the constructed prototype system and experimentally evaluate and compare the system with other systems. These systems include PostgreSQL, which is a relational DBMS conventionally implemented, and UB tree, which is organized in a similar kind of multidimensional approach with our history-offset encoding. The evaluation results prove that our vertical splitting scheme can reduce retrieval I/O cost, while expanding the required logical address space to store large scale multidimensional datasets. Our method far outperforms PostgreSQL and is fairly better than UB tree in retrieval time. The splitting causes increase of storage cost but the cost is not so large compared with those of them.