研究云计算与大数据分析处理领域建议看的学术论文列表

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[13] Wang GZ, Salles MV, Sowell B, Wang X, Cao T, Demers A, Gehrke J, White W. Behavioral simulations in MapReduce. PVLDB, 2010,3(1-2):952.963.
[14] Gunarathne T, Wu TL, Qiu J, Fox G. Cloud computing paradigms for pleasingly parallel biomedical applications. In: Hariri S, Keahey K, eds. Proc. of the HPDC. Chicago: ACM Press, 2010. 460−469.
[15] Delmerico JA, Byrnesy NA, Brunoz AE, Jonesz MD, Galloz SM, Chaudhary V. Comparing the performance of clusters, hadoop, and active disks on microarray correlation computations. In: Yang YY, Parashar M, Muralidhar R, Prasanna VK, eds. Proc. of the HiPC. Kochi: IEEE Press, 2009. 378−387.
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[17] Wegener D, Mock M, Adranale D, Wrobel S. Toolkit-Based high-performance data mining of large data on MapReduce clusters. In: Saygin Y, Yu JX, Kargupta H, Wang W, Ranka S, Yu PS, Wu XD, eds. Proc. of the ICDM Workshop. Washington: IEEE Computer Society, 2009. 296−301.
[18] Kovoor G, Singer J, Lujan M. Building a Java Map-Reduce framework for multi-core architectures. In: Ayguade E, Gioiosa R, Stenstrom P, Unsal O, eds. Proc. of the HiPEAC. Pisa: HiPEAC Endowment, 2010. 87−98.
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[20] Becerra Y, Beltran V, Carrera D, Gonzalez M, Torres J, Ayguade E. Speeding up distributed MapReduce applications using hardware accelerators. In: Barolli L, Feng WC, eds. Proc. of the ICPP. Vienna: IEEE Computer Society, 2009. 42−49.
[21] Ranger C, Raghuraman R, Penmetsa A, Bradski G, Kozyrakis C. Evaluating MapReduce for multi-core and multiprocessor systems. In: Dally WJ, ed. Proc. of the HPCA. Phoenix: IEEE Computer Society, 2007. 13−24.
[22] Ma WJ, Agrawal G. A translation system for enabling data mining applications on GPUs. In: Zhou P, ed. Proc. of the Supercomputing (SC). New York: ACM Press, 2009. 400−409.
[23] He BS, Fang WB, Govindaraju NK, Luo Q, Wang TY. Mars: A MapReduce framework on graphics processors. In: Moshovos A, Tarditi D, Olukotun K, eds. Proc. of the PACT. Ontario: ACM Press, 2008. 260−269.
[24] Stuart JA, Chen CK, Ma KL, Owens JD. Multi-GPU volume rendering using MapReduce. In: Hariri S, Keahey K, eds. Proc. of the MapReduce Workshop (HPDC 2010). New York: ACM Press, 2010. 841−848.
[25] Hong CT, Chen DH, Chen WG, Zheng WM, Lin HB. MapCG: Writing parallel program portable between CPU and GPU. In: Salapura V, Gschwind M, Knoop J, eds. Proc. of the PACT. Vienna: ACM Press, 2010. 217−226.
[26] Jiang W, Ravi VT, Agrawal G. A Map-Reduce system with an alternate API for multi-core environments. In: Chiba T, ed. Proc. of the CCGRID. Melbourne: IEEE Press, 2010. 84−93.
[27] Liao HJ, Han JZ, Fang JY. Multi-Dimensional index on hadoop distributed file system. In: Xu ZW, ed. Proc. of the Networking, Architecture, and Storage (NAS). Macau: IEEE Computer Society, 2010. 240−249.
[28] Zou YQ, Liu J, Wang SC, Zha L, Xu ZW. CCIndex: A complemental clustering index on distributed ordered tables for multi- dimensional range queries. In: Ding C, Shao ZY, Zheng R, eds. Proc. of the NPC. Zhengzhou: Springer-Verlag, 2010. 247−261.
[29] Zhang SB, Han JZ, Liu ZY, Wang K, Feng SZ. Accelerating MapReduce with distributed memory cache. In: Huang XX, ed. Proc. of the ICPADS. Shenzhen: IEEE Press, 2009. 472−478.
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[31] Chen ST. Cheetah: A high performance, custom data warehouse on top of MapReduce. PVLDB, 2010,3(1-2):1459−1468.
[32] Iu MY, Zwaenepoel W. HadoopToSQL: A MapReduce query optimizer. In: Morin C, Muller G, eds. Proc. of the EuroSys. Paris: ACM Press, 2010. 251−264.
[33] Blanas S, Patel JM, Ercegovac V, Rao J, Shekita EJ, Tian YY. A comparison of join algorithms for log processing in MapReduce. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 975−986.
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[37] Hoefler T, Lumsdaine A, Dongarra J. Towards efficient MapReduce using MPI. In: Oster P, ed. Proc. of the EuroPVM/MPI. Berlin: Springer-Verlag, 2009. 240.249.
[38] Nykiel T, Potamias M, Mishra C, Kollios G, Koudas N. MRShare: Sharing across multiple queries in MapReduce. PVLDB, 2010, 3(1-2):494.505.
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[44] Papagiannis A, Nikolopoulos DS. Rearchitecting MapReduce for heterogeneous multicore processors with explicitly managed memories. In: Qin F, Barolli L, Cho SY, eds. Proc. of the ICPP. San Diego: IEEE Press, 2010. 121.130.
[45] Jiang DW, Ooi BC, Shi L, Wu S. The performance of MapReduce: An in-depth study. PVLDB, 2010,3(1-2):472.483.
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[52] Isard M, Budiu M, Yu Y, Birrell A, Fetterly D. Dryad: Distributed data-parallel programs from sequential building blocks. ACM SIGOPS Operating Systems Review, 2007,41(3):59.72.
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[55] Condie T, Conway N, Alvaro P, Hellerstein JM, Gerth J, Talbot J, Elmeleegy K, Sears R. Online aggregation and continuous query support in MapReduce. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 1115.1118.
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[59] Abouzeid A, Bajda-Pawlikowski K, Abadi D, Silberschatz A, Rasin A. HadoopDB: An architectural hybrid of MapReduce and DBMS technologes for analytical workloads. PVLDB, 2009,2(1):922.933.
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[62] Stonebraker M, Abadi D, DeWitt DJ, Maden S, Paulson E, Pavlo A, Rasin A. MapReduce and parallel DBMSs: Friends or foes? Communications of the ACM, 2010,53(1):64.71.
[63] Dean J, Ghemawat S. MapReduce: A flexible data processing tool. Communications of ACM, 2010,53(1):72.77.
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[67] Kang U, Tsourakakis CE, Faloutsos C. PEGASUS: A peta-scale graph mining system—Implementation and observations. In: Wang W, Kargupta H, Ranka S, Yu PS, Wu XD, eds. Proc. of the ICDM. Miami: IEEE Computer Society, 2009. 229.238.
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[76] Lang W, Patel JM. Energy management for MapReduce clusters. PVLDB, 2010,3(1-2):129.139.
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[79] Groot S. Jumbo: Beyond MapReduce for workload balancing. In: Mylopoulos J, Zhou LZ, Zhou XF, eds. Proc. of the PhD Workshop (VLDB 2010). Singapore: VLDB Endowment, 2010. 7.12.
[80] Chatziantoniou D, Tzortzakakis E. ASSET queries: A declarative alternative to MapReduce. SIGMOD Record, 2009,38(2):35.41.
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