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Home > Archive>Volume 20, Issue zk, 2009 >165-175
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Distributed Aggregate Functions Enabled Parallel Main-Memory OLAP Query Processing Technique
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    Abstract:

    A multi-node parallel main-memory OLAP system is proposed in this paper which is considered by the character of OLAP queries and the performance of main-memory database system. In this system, multi-dimensional OLAP queries with aggregate functions are distributed to each computing node to get aggregate results and final result can be available by merging all the aggregate results from multiple computing nodes. Comparing with other solutions, this system uses horizontal distribution policy to distribute massive data in multi-node with only consideration of memory capacity of computation node. According to feature of distributed aggregate function, system can improve parallel processing capacity by lazy results merging which can reduce the volume of message between nodes, the overall performance of parallel query processing can be improved. This system is easy to deploy, it is also practical with good scalability and performance for the requirements of enterprise massive data processing.

    Reference
    [1] Chen Y, Dehne F, Eavis T. Parallel ROLAP data cube construction on shared-nothing multiprocessors. Distributed and Parallel Databases, 2004,15(3):219?236.
    [2] Dehne F, Eavis T, Rau-Chaplin A. The cgmCUBE project: Optimizing parallel data cube generation for ROLAP. Distributed and Parallel Databases, 2006,19(1):29?62.
    [3] Goil S, Choudhary A. Parallel data cube construction for high performance on-line analytical processing. In: Proc. of the 4th Int’l Conf. on High-Performance Computing. Washington: IEEE Computer Society, 1997. 18?21.
    [4] Goil S, Choudhary A. A parallel scalable infrastructure for OLAP and data mining. In: Proc. of the Int’l Data Engineering and Applications Symp. (IDEAS’99). 1999. 178?186.
    [5] Muto S, Kitsuregawa M. A dynamic load balancing strategy for parallel datacube computation. In: Proc. of the 2nd ACM Int’l Workshop on Data Warehousing and OLAP. New York: ACM, 1999. 67?72.
    [6] Lu HJ, Huang XH, Li ZX. Computing data cubes using massively parallel processors. In: Proc. of the 7th Parallel Computing Workshop (PCW’97). 1997.
    [7] Mumick IS, Quass D, Mumick BS. Maintenance of data cubes and summary tables in a warehouse. In: Peckham J, ed. Proc. of the ACM-SIGMOD Conf. on Management of Data. New York: ACM Press, 1997. 100?111.
    [8] Lee KY, Kim M. Efficient incremental maintenance of data cubes. In: Dayal U, Whang KY, eds. Proc. of the 32nd Int’l Conf. on Very Large Data Bases. VLDB Endowment, 2006. 823?833.
    [9] Zhang YS, Xiao YQ, Wang ZW, Ji XD, Huang YK, Wang S. ScaMMDB: Facing challenge of mass data processing with MMDB. In: Chen L, et al., eds. Proc. of the 1st Int’l Workshop on Real-Time Business Intelligence at APWeb/WAIM. 2009. 1?12.
    [10] Chen YJ, Cole RL, McKenna WJ, Perfilov S, Sinha A, Szedenits E. Partial join order optimization in the ParAccel analytic database. In: ?etintemel U, Zdonik SB, et al., eds. Proc. of the SIGMOD 2009. 2009. 905?908.
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张延松,张 宇,黄 伟,王 珊,陈 红.分布式聚集函数支持的内存OLAP并行查询处理技术.软件学报,2009,20(zk):165-175

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History
  • Received:May 01,2009
  • Revised:July 20,2009
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