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By Abdelkader Hameurlain, Josef Küng, Roland Wagner, Ladjel Bellatreche, Mukesh Mohania

The LNCS magazine Transactions on Large-Scale information- and Knowledge-Centered platforms makes a speciality of facts administration, wisdom discovery and data processing, that are center and scorching themes in computing device technology. because the Nineties, the net has turn into the most driver in the back of software improvement in all domain names. a rise within the call for for source sharing throughout diverse websites hooked up via networks has ended in an evolution of information- and knowledge-management platforms from centralized structures to decentralized platforms permitting large-scale disbursed purposes delivering excessive scalability. present decentralized structures nonetheless specialize in facts and information as their major source. Feasibility of those structures is based primarily on P2P (peer-to-peer) ideas and the help of agent platforms with scaling and decentralized keep watch over. Synergy among grids, P2P platforms and agent applied sciences is the major to info- and knowledge-centered structures in large-scale environments. This, the seventeenth factor of Transactions on Large-Scale facts- and Knowledge-Centered platforms, comprises prolonged and revised types of 5 papers, chosen from the 24 complete and eight brief papers provided on the fifteenth foreign convention on information Warehousing and data Discovery, DaWaK 2013, held in Prague, The Czech Republic, in August 2013. Of the 5 papers, conceal facts warehousing facets regarding question processing optimization in complex structures, particularly Map decrease and parallel databases, and 3 conceal wisdom discovery, in particular the causal community inference challenge, dimensionality relief, and the quality-of-pattern-mining task.

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Map tasks need to transfer data to reduce tasks, but reduce tasks do not run until all the map tasks are completed. Therefore, some trade-offs are necessary to find out when and where reduce tasks run. The inefficient storage problem is more independent relatively to the first two conditions. In order to resolve these problems, we introduce the following four techniques. An Uncoupled Data Process and Transfer Model for MapReduce Node1 Node2 Node3 Map Map Map Dynamic MemBuffer Dynamic MemBuffer Dynamic MemBuffer compress compress compress Mem Mem Mem Index Data Index Data Index P1 P2 I1 I2 P1 P2 I1 I2 P1 P2 I1 I2 Map Data Disk Disk Data-P1 Index-P1 M1 M2 M3 I1 I2 I3 Data-P2 Index-P2 M1 M2 M3 I1 I2 I3 Node 2 Node 1 Reduce Mem Mem Disk DataP1 M1 M2 M3 IndexP1 I1 I2 I3 29 Reduce Compress Disk DataP1 M1 M2 M3 Reduce IndexP1 I1 I2 I3 Compress Fig.

4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. Apache hadoop. org/ Bzip2 compression. org/ Gridmix. html Lempel-ziv-oberhumer(lzo) compression. com/opensource/ lzo Quicklz. com/ Snappy. : A study of integrated prefetching and caching strategies. SIGMETRICS Perform. Eval. Rev. : Bigtable: A distributed storage system for structured data. ACM Trans. Comput. Syst. : To compress or not to compress - compute vs. IO tradeoffs for MapReduce energy efficiency. In: Proceedings of the First ACM SIGCOMM Workshop on Green Networking, Green Networking 2010, pp.

Platforms which can process and store large data are receiving more and more attention, such as MapReduce [12], Dryad [16], Sector/Sphere [15], and BigTable [8]. The MapReduce programming model proposed by Google has become the mainstream data-centric platform for large data processing because of its scalability and simplicity. Apache Hadoop [1], an open-source implementation of MapReduce, is widely used. The MapReduce model is a software architecture for parallel computing on large data sets with commercial hardware.

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