研究大规模数据处理的文献列表
[1] Zhou AY. Data intensive computing-challenges of data management techniques. Communications of CCF, 2009,5(7):50.53 (in Chinese with English abstract).
[2] Cohen J, Dolan B, Dunlap M, Hellerstein JM, Welton C. MAD skills: New analysis practices for big data. PVLDB, 2009,2(2): 1481.1492.
[3] Schroeder B, Gibson GA. Understanding failures in petascale computers. Journal of Physics: Conf. Series, 2007,78(1):1.11.
[4] Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. In: Brewer E, Chen P, eds. Proc. of the OSDI. California: USENIX Association, 2004. 137.150.
[5] Pavlo A, Paulson E, Rasin A, Abadi DJ, Dewitt DJ, Madden S, Stonebraker M. A comparison of approaches to large-scale data analysis. In: Cetintemel U, Zdonik SB, Kossmann D, Tatbul N, eds. Proc. of the SIGMOD. Rhode Island: ACM Press, 2009. 165.178.
[6] Chu CT, Kim SK, Lin YA, Yu YY, Bradski G, Ng AY, Olukotun K. Map-Reduce for machine learning on multicore. In: Scholkopf B, Platt JC, Hoffman T, eds. Proc. of the NIPS. Vancouver: MIT Press, 2006. 281.288.
[7] Wang CK, Wang JM, Lin XM, Wang W, Wang HX, Li HS, Tian WP, Xu J, Li R. MapDupReducer: Detecting near duplicates over massive datasets. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 1119.1122.
[8] Liu C, Guo F, Faloutsos C. BBM: Bayesian browsing model from petabyte-scale data. In: Elder JF IV, Fogelman-Soulié F, Flach PA, Zaki MJ, eds. Proc. of the KDD. Paris: ACM Press, 2009. 537.546.
[9] Panda B, Herbach JS, Basu S, Bayardo RJ. PLANET: Massively parallel learning of tree ensembles with MapReduce. PVLDB, 2009,2(2):1426.1437.
[10] Lin J, Schatz M. Design patterns for efficient graph algorithms in MapReduce. In: Rao B, Krishnapuram B, Tomkins A, Yang Q, eds. Proc. of the KDD. Washington: ACM Press, 2010. 78.85.
[11] Zhang CJ, Ma Q, Wang XL, Zhou AY. Distributed SLCA-based XML keyword search by Map-Reduce. In: Yoshikawa M, Meng XF, Yumoto T, Ma Q, Sun LF, Watanabe C, eds. Proc. of the DASFAA. Tsukuba: Springer-Verlag, 2010. 386.397.
[12] Stupar A, Michel S, Schenkel R. RankReduce—Processing K-nearest neighbor queries on top of MapReduce. In: Crestani F, Marchand-Maillet S, Chen HH, Efthimiadis EN, Savoy J, eds. Proc. of the SIGIR. Geneva: ACM Press, 2010. 13.18.
[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.
[16] Das S, Sismanis Y, Beyer KS, Gemulla R, Haas PJ, McPherson J. Ricardo: Integrating R and hadoop. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 987−998.
[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.
[19] De Kruijf M, Sankaralingam K. MapReduce for the cell broadband engine architecture. IBM Journal of Research and Development, 2009,53(5):1−12.
[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.
[30] Dittrich J, Quian′e-Ruiz JA, Jindal A, Kargin Y, Setty V, Schad J. Hadoop++: Making a yellow elephant run like a cheetah (without it even noticing). PVLDB, 2010,3(1-2):518−529.
[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.
[34] Zhou MQ, Zhang R, Zeng DD, Qian WN, Zhou AY. Join optimization in the MapReduce environment for column-wise data store. In: Fang YF, Huang ZX, eds. Proc. of the SKG. Ningbo: EEE Computer Society, 2010. 97−104.
[35] Afrati FN, Ullman JD. Optimizing joins in a Map-Reduce environment. In: Manolescu I, Spaccapietra S, Teubner J, Kitsuregawa M, Léger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 99−110.
[36] Sandholm T, Lai K. MapReduce optimization using regulated dynamic prioritization. In: Douceur JR, Greenberg AG, Bonald T, Nieh J, eds. Proc. of the SIGMETRICS. Seattle: ACM Press, 2009. 299.310.
[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.
[39] Kambatla K, Rapolu N, Jagannathan S, Grama A. Asynchronous algorithms in MapReduce. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 245.254.
[40] Polo J, Carrera D, Becerra Y, Torres J, Ayguade E, Steinder M, Whalley I. Performance-Driven task co-scheduling for MapReduce environments. In: Tonouchi T, Kim MS, eds. Proc. of the IEEE Network Operations and Management Symp. (NOMS). Osaka: IEEE Press, 2010. 373.380.
[41] Zaharia M, Konwinski A, Joseph AD, Katz R, Stoica I. Improving MapReduce performance in heterogeneous environments. In: Draves R, van Renesse R, eds. Proc. of the ODSI. Berkeley: USENIX Association, 2008. 29.42.
[42] Xie J, Yin S, Ruan XJ, Ding ZY, Tian Y, Majors J, Manzanares A, Qin X. Improving MapReduce performance through data placement in heterogeneous hadoop clusters. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshop on Heterogeneity in Computing (IPDPS 2010). Atlanta: IEEE Press, 2010. 1.9.
[43] Polo J, Carrera D, Becerra Y, Beltran V, Torres J, Ayguade E. Performance management of accelerated MapReduce workloads in heterogeneous clusters. In: Qin F, Barolli L, Cho SY, eds. Proc. of the ICPP. San Diego: IEEE Press, 2010. 653.662.
[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.
[46] Berthold J, Dieterle M, Loogen R. Implementing parallel Google Map-Reduce in Eden. In: Sips HJ, Epema DHJ, Lin HX, eds. Proc. of the Euro-Par. Delft: Springer-Verlag, 2009. 990.1002.
[47] Verma A, Zea N, Cho B, Gupta I, Campbell RH. Breaking the MapReduce stage barrier. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 235.244.
[48] Yang HC, Dasdan A, Hsiao RL, Parker DS. Map-Reduce-Merge simplified relational data processing on large clusters. In: Chan CY, Ooi BC, Zhou AY, eds. Proc. of the SIGMOD. Beijing: ACM Press, 2007. 1029.1040.
[49] Seo SW, Jang I, Woo KC, Kim I, Kim JS, Maeng S. HPMR: Prefetching and pre-shuffling in shared MapReduce computation environment. In: Rana O, Tang FL, Kosar T, eds. Proc. of the CLUSTER. New Orleans: IEEE Press, 2009. 1.8.
[50] Babu S. Towards automatic optimization of MapReduce programs. In: Kansal A, ed. Proc. of the ACM Symp. on Cloud Computing (SoCC). New York: ACM Press, 2010. 137.142.
[51] Olston C, Reed B, Srivastava U, Kumar R, Tomkins A. Pig Latin: A not-so-foreign language for data processing. In: Wang JTL, ed. Proc. of the SIGMOD. Vancouver: ACM Press, 2008. 1099.1110.
[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.
[53] Isard M, Yu Y. Distributed data-parallel computing using a high-level programming language. In: Cetintemel U, Zdonik SB, Kossmann D, Tatbul N, eds. Proc. of the SIGMOD. Rhode Island: ACM Press, 2009. 987.994.
[54] Chaiken R, Jenkins B, Larson P, Ramsey B, Shakib D, Weaver S, Zhou JR. SCOPE: Easy and efficient parallel processing of massive data sets. PVLDB, 2008,1(2):1265.1276.
[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.
[56] Thusoo A, Sarma JS, Jain N, Shao Z, Chakka P, Anthony S, Liu H, Wyckoff P, Murthy R. Hive a warehousing solution over a MapReduce framework. PVLDB, 2009,2(2):938.941.
[57] Ghoting A, Pednault E. Hadoop-ML: An infrastructure for the rapid implementation of parallel reusable analytics. In: Culotta A, ed. Proc. of the Large-Scale Machine Learning: Parallelism and Massive Datasets Workshop (NIPS 2009). Vancouver: MIT Press, 2009. 6.
[58] Yang C, Yen C, Tan C, Madden S. Osprey: Implementing MapReduce-style fault tolerance in a shared-nothing distributed database. In: Li FF, Moro MM, Ghandeharizadeh S, Haritsa JR, Weikum G, Carey MJ, Casati F, Chang EY, Manolescu I, Mehrotra S, Dayal U, Tsotras VJ, eds. Proc. of the ICDE. Long Beach: IEEE Press, 2010. 657.668.
[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.
[60] Abouzied A, Bajda-Pawlikowski K, Huang JW, Abadi DJ, Silberschatz A. HadoopDB in action: Building real world applications. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 1111.1114.
[61] Friedman E, Pawlowski P, Cieslewicz J. SQL/MapReduce: A practical approach to self describing, polymorphic, and parallelizable user defined functions. PVLDB, 2009,2(2):1402.1413.
[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.
[64] Xu Y, Kostamaa P, Gao LK. Integrating hadoop and parallel DBMS. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 969.974.
[65] Thusoo A, Shao Z, Anthony S, Borthakur D, Jain N, Sarma JS, Murthy R, Liu H. Data warehousing and analytics infrastructure at facebook. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 1013.1020.
[66] Mcnabb AW, Monson CK, Seppi KD. MRPSO: MapReduce particle swarm optimization. In: Ryan C, Keijzer M, eds. Proc. of the GECCO. Atlanta: ACM Press, 2007. 177.185.
[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.
[68] Kang S, Bader DA. Large scale complex network analysis using the hybrid combination of a MapReduce cluster and a highly multithreaded system. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshops and Phd Forum (IPDPS 2010). Atlanta: IEEE Presss, 2010. 11.19.
[69] Logothetis D, Yocum K. AdHoc data processing in the cloud. PVLDB, 2008,1(1):1472.1475.
[70] Olston C, Bortnikov E, Elmeleegy K, Junqueira F, Reed B. Interactive analysis of WebScale data. In: DeWitt D, ed. Proc. of the CIDR. Asilomar: Online www.crdrdb.org, 2009.
[71] Bose JH, Andrzejak A, Hogqvist M. Beyond online aggregation: Parallel and incremental data mining with online Map-Reduce. In: Tanaka K, Zhou XF, Zhang M, Jatowt A, eds. Proc. of the Workshop on Massive Data Analytics on the Cloud (WWW 2010). Raleigh: ACM Press, 2010. 3.
[72] Kumar V, Andrade H, Gedik B, Wu KL. DEDUCE: At the intersection of MapReduce and stream processing. In: Manolescu I, Spaccapietra S, Teubner J, Kitsuregawa M, Léger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 657.662.
[73] Abramson D, Dinh MN, Kurniawan D, Moench B, DeRose L. Data centric highly parallel debugging. In: Hariri S, Keahey K, eds. Proc. of the HPDC. Chicago: ACM Press, 2010. 119.129.
[74] Morton K, Friesen A, Balazinska M, Grossman D. Estimating the progress of MapReduce pipelines. In: Li FF, Moro MM, Ghandeharizadeh S, et al., eds. Proc. of the ICDE. Long Beach: IEEE Press, 2010. 681.684.
[75] Morton K, Balazinska M, Grossman D. ParaTimer: A progress indicator for MapReduce DAGs. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 507.518.
[76] Lang W, Patel JM. Energy management for MapReduce clusters. PVLDB, 2010,3(1-2):129.139.
[77] Wieder A, Bhatotia P, Post A, Rodrigues R. Brief announcement: Modeling MapReduce for optimal execution in the cloud. In: Richa AW, Guerraoui R, eds. Proc. of the PODC. Zurich: ACM Press, 2010. 408.409.
[78] Zheng Q. Improving MapReduce fault tolerance in the cloud. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshops and Phd Forum (IPDPS 2010). Atlanta: IEEE Presss, 2010. 1.6.
[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.
[81] Bu YY, Howe B, Balazinska M, Ernst MD. HaLoop: Efficient iterative data processing on large clusters. PVLDB, 2010,3(1-2): 285−296.
[82] Wang HJ, Qin XP, Zhang YS, Wang S, Wang ZW. LinearDB: A relational approach to make data warehouse scale like MapReduce. In: Yu JX, Kim MH, Unland R, eds. Proc. of the DASFAA. Hong Kong: Springer-Verlag, 2011. 306−320.
[2] Cohen J, Dolan B, Dunlap M, Hellerstein JM, Welton C. MAD skills: New analysis practices for big data. PVLDB, 2009,2(2): 1481.1492.
[3] Schroeder B, Gibson GA. Understanding failures in petascale computers. Journal of Physics: Conf. Series, 2007,78(1):1.11.
[4] Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. In: Brewer E, Chen P, eds. Proc. of the OSDI. California: USENIX Association, 2004. 137.150.
[5] Pavlo A, Paulson E, Rasin A, Abadi DJ, Dewitt DJ, Madden S, Stonebraker M. A comparison of approaches to large-scale data analysis. In: Cetintemel U, Zdonik SB, Kossmann D, Tatbul N, eds. Proc. of the SIGMOD. Rhode Island: ACM Press, 2009. 165.178.
[6] Chu CT, Kim SK, Lin YA, Yu YY, Bradski G, Ng AY, Olukotun K. Map-Reduce for machine learning on multicore. In: Scholkopf B, Platt JC, Hoffman T, eds. Proc. of the NIPS. Vancouver: MIT Press, 2006. 281.288.
[7] Wang CK, Wang JM, Lin XM, Wang W, Wang HX, Li HS, Tian WP, Xu J, Li R. MapDupReducer: Detecting near duplicates over massive datasets. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 1119.1122.
[8] Liu C, Guo F, Faloutsos C. BBM: Bayesian browsing model from petabyte-scale data. In: Elder JF IV, Fogelman-Soulié F, Flach PA, Zaki MJ, eds. Proc. of the KDD. Paris: ACM Press, 2009. 537.546.
[9] Panda B, Herbach JS, Basu S, Bayardo RJ. PLANET: Massively parallel learning of tree ensembles with MapReduce. PVLDB, 2009,2(2):1426.1437.
[10] Lin J, Schatz M. Design patterns for efficient graph algorithms in MapReduce. In: Rao B, Krishnapuram B, Tomkins A, Yang Q, eds. Proc. of the KDD. Washington: ACM Press, 2010. 78.85.
[11] Zhang CJ, Ma Q, Wang XL, Zhou AY. Distributed SLCA-based XML keyword search by Map-Reduce. In: Yoshikawa M, Meng XF, Yumoto T, Ma Q, Sun LF, Watanabe C, eds. Proc. of the DASFAA. Tsukuba: Springer-Verlag, 2010. 386.397.
[12] Stupar A, Michel S, Schenkel R. RankReduce—Processing K-nearest neighbor queries on top of MapReduce. In: Crestani F, Marchand-Maillet S, Chen HH, Efthimiadis EN, Savoy J, eds. Proc. of the SIGIR. Geneva: ACM Press, 2010. 13.18.
[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.
[16] Das S, Sismanis Y, Beyer KS, Gemulla R, Haas PJ, McPherson J. Ricardo: Integrating R and hadoop. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 987−998.
[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.
[19] De Kruijf M, Sankaralingam K. MapReduce for the cell broadband engine architecture. IBM Journal of Research and Development, 2009,53(5):1−12.
[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.
[30] Dittrich J, Quian′e-Ruiz JA, Jindal A, Kargin Y, Setty V, Schad J. Hadoop++: Making a yellow elephant run like a cheetah (without it even noticing). PVLDB, 2010,3(1-2):518−529.
[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.
[34] Zhou MQ, Zhang R, Zeng DD, Qian WN, Zhou AY. Join optimization in the MapReduce environment for column-wise data store. In: Fang YF, Huang ZX, eds. Proc. of the SKG. Ningbo: EEE Computer Society, 2010. 97−104.
[35] Afrati FN, Ullman JD. Optimizing joins in a Map-Reduce environment. In: Manolescu I, Spaccapietra S, Teubner J, Kitsuregawa M, Léger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 99−110.
[36] Sandholm T, Lai K. MapReduce optimization using regulated dynamic prioritization. In: Douceur JR, Greenberg AG, Bonald T, Nieh J, eds. Proc. of the SIGMETRICS. Seattle: ACM Press, 2009. 299.310.
[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.
[39] Kambatla K, Rapolu N, Jagannathan S, Grama A. Asynchronous algorithms in MapReduce. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 245.254.
[40] Polo J, Carrera D, Becerra Y, Torres J, Ayguade E, Steinder M, Whalley I. Performance-Driven task co-scheduling for MapReduce environments. In: Tonouchi T, Kim MS, eds. Proc. of the IEEE Network Operations and Management Symp. (NOMS). Osaka: IEEE Press, 2010. 373.380.
[41] Zaharia M, Konwinski A, Joseph AD, Katz R, Stoica I. Improving MapReduce performance in heterogeneous environments. In: Draves R, van Renesse R, eds. Proc. of the ODSI. Berkeley: USENIX Association, 2008. 29.42.
[42] Xie J, Yin S, Ruan XJ, Ding ZY, Tian Y, Majors J, Manzanares A, Qin X. Improving MapReduce performance through data placement in heterogeneous hadoop clusters. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshop on Heterogeneity in Computing (IPDPS 2010). Atlanta: IEEE Press, 2010. 1.9.
[43] Polo J, Carrera D, Becerra Y, Beltran V, Torres J, Ayguade E. Performance management of accelerated MapReduce workloads in heterogeneous clusters. In: Qin F, Barolli L, Cho SY, eds. Proc. of the ICPP. San Diego: IEEE Press, 2010. 653.662.
[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.
[46] Berthold J, Dieterle M, Loogen R. Implementing parallel Google Map-Reduce in Eden. In: Sips HJ, Epema DHJ, Lin HX, eds. Proc. of the Euro-Par. Delft: Springer-Verlag, 2009. 990.1002.
[47] Verma A, Zea N, Cho B, Gupta I, Campbell RH. Breaking the MapReduce stage barrier. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 235.244.
[48] Yang HC, Dasdan A, Hsiao RL, Parker DS. Map-Reduce-Merge simplified relational data processing on large clusters. In: Chan CY, Ooi BC, Zhou AY, eds. Proc. of the SIGMOD. Beijing: ACM Press, 2007. 1029.1040.
[49] Seo SW, Jang I, Woo KC, Kim I, Kim JS, Maeng S. HPMR: Prefetching and pre-shuffling in shared MapReduce computation environment. In: Rana O, Tang FL, Kosar T, eds. Proc. of the CLUSTER. New Orleans: IEEE Press, 2009. 1.8.
[50] Babu S. Towards automatic optimization of MapReduce programs. In: Kansal A, ed. Proc. of the ACM Symp. on Cloud Computing (SoCC). New York: ACM Press, 2010. 137.142.
[51] Olston C, Reed B, Srivastava U, Kumar R, Tomkins A. Pig Latin: A not-so-foreign language for data processing. In: Wang JTL, ed. Proc. of the SIGMOD. Vancouver: ACM Press, 2008. 1099.1110.
[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.
[53] Isard M, Yu Y. Distributed data-parallel computing using a high-level programming language. In: Cetintemel U, Zdonik SB, Kossmann D, Tatbul N, eds. Proc. of the SIGMOD. Rhode Island: ACM Press, 2009. 987.994.
[54] Chaiken R, Jenkins B, Larson P, Ramsey B, Shakib D, Weaver S, Zhou JR. SCOPE: Easy and efficient parallel processing of massive data sets. PVLDB, 2008,1(2):1265.1276.
[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.
[56] Thusoo A, Sarma JS, Jain N, Shao Z, Chakka P, Anthony S, Liu H, Wyckoff P, Murthy R. Hive a warehousing solution over a MapReduce framework. PVLDB, 2009,2(2):938.941.
[57] Ghoting A, Pednault E. Hadoop-ML: An infrastructure for the rapid implementation of parallel reusable analytics. In: Culotta A, ed. Proc. of the Large-Scale Machine Learning: Parallelism and Massive Datasets Workshop (NIPS 2009). Vancouver: MIT Press, 2009. 6.
[58] Yang C, Yen C, Tan C, Madden S. Osprey: Implementing MapReduce-style fault tolerance in a shared-nothing distributed database. In: Li FF, Moro MM, Ghandeharizadeh S, Haritsa JR, Weikum G, Carey MJ, Casati F, Chang EY, Manolescu I, Mehrotra S, Dayal U, Tsotras VJ, eds. Proc. of the ICDE. Long Beach: IEEE Press, 2010. 657.668.
[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.
[60] Abouzied A, Bajda-Pawlikowski K, Huang JW, Abadi DJ, Silberschatz A. HadoopDB in action: Building real world applications. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 1111.1114.
[61] Friedman E, Pawlowski P, Cieslewicz J. SQL/MapReduce: A practical approach to self describing, polymorphic, and parallelizable user defined functions. PVLDB, 2009,2(2):1402.1413.
[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.
[64] Xu Y, Kostamaa P, Gao LK. Integrating hadoop and parallel DBMS. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 969.974.
[65] Thusoo A, Shao Z, Anthony S, Borthakur D, Jain N, Sarma JS, Murthy R, Liu H. Data warehousing and analytics infrastructure at facebook. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 1013.1020.
[66] Mcnabb AW, Monson CK, Seppi KD. MRPSO: MapReduce particle swarm optimization. In: Ryan C, Keijzer M, eds. Proc. of the GECCO. Atlanta: ACM Press, 2007. 177.185.
[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.
[68] Kang S, Bader DA. Large scale complex network analysis using the hybrid combination of a MapReduce cluster and a highly multithreaded system. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshops and Phd Forum (IPDPS 2010). Atlanta: IEEE Presss, 2010. 11.19.
[69] Logothetis D, Yocum K. AdHoc data processing in the cloud. PVLDB, 2008,1(1):1472.1475.
[70] Olston C, Bortnikov E, Elmeleegy K, Junqueira F, Reed B. Interactive analysis of WebScale data. In: DeWitt D, ed. Proc. of the CIDR. Asilomar: Online www.crdrdb.org, 2009.
[71] Bose JH, Andrzejak A, Hogqvist M. Beyond online aggregation: Parallel and incremental data mining with online Map-Reduce. In: Tanaka K, Zhou XF, Zhang M, Jatowt A, eds. Proc. of the Workshop on Massive Data Analytics on the Cloud (WWW 2010). Raleigh: ACM Press, 2010. 3.
[72] Kumar V, Andrade H, Gedik B, Wu KL. DEDUCE: At the intersection of MapReduce and stream processing. In: Manolescu I, Spaccapietra S, Teubner J, Kitsuregawa M, Léger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 657.662.
[73] Abramson D, Dinh MN, Kurniawan D, Moench B, DeRose L. Data centric highly parallel debugging. In: Hariri S, Keahey K, eds. Proc. of the HPDC. Chicago: ACM Press, 2010. 119.129.
[74] Morton K, Friesen A, Balazinska M, Grossman D. Estimating the progress of MapReduce pipelines. In: Li FF, Moro MM, Ghandeharizadeh S, et al., eds. Proc. of the ICDE. Long Beach: IEEE Press, 2010. 681.684.
[75] Morton K, Balazinska M, Grossman D. ParaTimer: A progress indicator for MapReduce DAGs. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 507.518.
[76] Lang W, Patel JM. Energy management for MapReduce clusters. PVLDB, 2010,3(1-2):129.139.
[77] Wieder A, Bhatotia P, Post A, Rodrigues R. Brief announcement: Modeling MapReduce for optimal execution in the cloud. In: Richa AW, Guerraoui R, eds. Proc. of the PODC. Zurich: ACM Press, 2010. 408.409.
[78] Zheng Q. Improving MapReduce fault tolerance in the cloud. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshops and Phd Forum (IPDPS 2010). Atlanta: IEEE Presss, 2010. 1.6.
[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.
[81] Bu YY, Howe B, Balazinska M, Ernst MD. HaLoop: Efficient iterative data processing on large clusters. PVLDB, 2010,3(1-2): 285−296.
[82] Wang HJ, Qin XP, Zhang YS, Wang S, Wang ZW. LinearDB: A relational approach to make data warehouse scale like MapReduce. In: Yu JX, Kim MH, Unland R, eds. Proc. of the DASFAA. Hong Kong: Springer-Verlag, 2011. 306−320.