메뉴 건너뛰기




Volumn 5, Issue 10, 2012, Pages 1028-1039

Early accurate results for advanced analytics on mapreduce

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL APPLICATIONS; APPROXIMATE RESULTS; ARBITRARY FUNCTIONS; DEGREE OF ACCURACY; INCREMENTAL COMPUTATION; MAPREDUCE FRAMEWORKS; MASSIVE DATA SETS; RESOURCE CONSTRAINT;

EID: 84873118945     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/2336664.2336675     Document Type: Article
Times cited : (92)

References (31)
  • 1
    • 84873199185 scopus 로고    scopus 로고
    • Earl release website
    • Earl release website: http://yellowstone.cs.ucla.edu/wis/
  • 2
    • 84873121706 scopus 로고    scopus 로고
    • Hadoop: Open-source implementation of mapreduce
    • Hadoop: Open-source implementation of mapreduce: http://hadoop.apache.org.
  • 3
    • 79957872898 scopus 로고    scopus 로고
    • Hyracks: A exible and extensible foundation for data-intensive computing
    • V. Borkar, M. Carey, R. Grover, N. Onose, and R. Vernica. Hyracks: A exible and extensible foundation for data-intensive computing. In ICDE, pages 1151 -1162, 2011.
    • (2011) ICDE , pp. 1151-1162
    • Borkar, V.1    Carey, M.2    Grover, R.3    Onose, N.4    Vernica, R.5
  • 4
    • 79956351190 scopus 로고    scopus 로고
    • HaLoop: Effcient iterative data processing on large clusters
    • Y. Bu, B. Howe, M. Balazinska, and M. D. Ernst. HaLoop: Effcient iterative data processing on large clusters. In VLDB, pages 285-296, 2010.
    • (2010) VLDB , pp. 285-296
    • Bu, Y.1    Howe, B.2    Balazinska, M.3    Ernst, M.D.4
  • 5
    • 3142697062 scopus 로고    scopus 로고
    • Effective use of block-level sampling in statistics estimation
    • S. Chaudhuri, G. Das, and U. Srivastava. Effective use of block-level sampling in statistics estimation. In SIGMOD, pages 287-298, 2004.
    • (2004) SIGMOD , pp. 287-298
    • Chaudhuri, S.1    Das, G.2    Srivastava, U.3
  • 6
    • 0242625264 scopus 로고    scopus 로고
    • A new two-phase sampling based algorithm for discovering association rules
    • B. Chen, P. Haas, and P. Scheuermann. A new two-phase sampling based algorithm for discovering association rules. In SIGKDD, pages 462-468, 2002.
    • (2002) SIGKDD , pp. 462-468
    • Chen, B.1    Haas, P.2    Scheuermann, P.3
  • 7
    • 84873176683 scopus 로고    scopus 로고
    • Mapreduce online. Technical Report UCB/EECS-2009-136, EECS Department, University of California, Berkeley
    • T. Condie, N. Conway, P. Alvaro, J. M. Hellerstein, K. Elmeleegy, and R. Sears. Mapreduce online. Technical Report UCB/EECS-2009-136, EECS Department, University of California, Berkeley, 2009.
    • (2009)
    • Condie, T.1    Conway, N.2    Alvaro, P.3    Hellerstein, J.M.4    Elmeleegy, K.5    Sears, R.6
  • 9
    • 84873193663 scopus 로고    scopus 로고
    • Bootstrap tests: How many bootstraps? Working Papers 1036, Queen's University, Department of Economics
    • R. Davidson and J. G. MacKinnon. Bootstrap tests: How many bootstraps? Working Papers 1036, Queen's University, Department of Economics, 2001.
    • (2001)
    • Davidson, R.1    MacKinnon, J.G.2
  • 11
    • 0002344794 scopus 로고
    • Bootstrap methods: Another look at the jackknife
    • B. Efron. Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7(1):1-26, 1979.
    • (1979) Annals of Statistics , vol.7 , Issue.1 , pp. 1-26
    • Efron, B.1
  • 14
    • 84864210199 scopus 로고    scopus 로고
    • Extending map-reduce for effcient predicate-based sampling
    • R. Grover and M. Carey. Extending map-reduce for effcient predicate-based sampling. In ICDE, 2012.
    • (2012) ICDE
    • Grover, R.1    Carey, M.2
  • 18
    • 66249121016 scopus 로고    scopus 로고
    • Double block bootstrap conffdence intervals for dependent data
    • S. M. S. Lee and P. Y. Lai. Double block bootstrap conffdence intervals for dependent data. Biometrika, 96(2):427-443, 2009.
    • (2009) Biometrika , vol.96 , Issue.2 , pp. 427-443
    • Lee, S.M.S.1    Lai, P.Y.2
  • 19
    • 79959939881 scopus 로고    scopus 로고
    • A platform for scalable one-pass analytics using mapreduce
    • B. Li, E. Mazur, Y. Diao, A. McGregor, and P. J. Shenoy. A platform for scalable one-pass analytics using mapreduce. In SIGMOD, pages 985-996, 2011.
    • (2011) SIGMOD , pp. 985-996
    • Li, B.1    Mazur, E.2    Diao, Y.3    McGregor, A.4    Shenoy, P.J.5
  • 20
    • 85032328251 scopus 로고
    • Random sampling from database ffles: A survey
    • F. Olken and D. Rotem. Random sampling from database ffles: A survey. In SSDBM, pages 92-111, 1990.
    • (1990) SSDBM , pp. 92-111
    • Olken, F.1    Rotem, D.2
  • 21
    • 55349148888 scopus 로고    scopus 로고
    • Pig latin: a not-so-foreign language for data processing
    • C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig latin: a not-so-foreign language for data processing. In SIGMOD, pages 1099-1110, 2008.
    • (2008) SIGMOD , pp. 1099-1110
    • Olston, C.1    Reed, B.2    Srivastava, U.3    Kumar, R.4    Tomkins, A.5
  • 22
    • 84863769684 scopus 로고    scopus 로고
    • Online aggregation for large mapreduce jobs
    • N. Pansare, V. R. Borkar, C. Jermaine, and T. Condie. Online aggregation for large mapreduce jobs. PVLDB, 4(11):1135-1145, 2011.
    • (2011) PVLDB , vol.4 , Issue.11 , pp. 1135-1145
    • Pansare, N.1    Borkar, V.R.2    Jermaine, C.3    Condie, T.4
  • 23
    • 30344452311 scopus 로고    scopus 로고
    • Interpreting the data: Parallel analysis with sawzall
    • R. Pike, S. Dorward, R. Griesemer, and S. Quinlan. Interpreting the data: Parallel analysis with sawzall. Sci. Program., 13(4):277-298, 2005.
    • (2005) Sci. Program. , vol.13 , Issue.4 , pp. 277-298
    • Pike, R.1    Dorward, S.2    Griesemer, R.3    Quinlan, S.4
  • 24
    • 29844444248 scopus 로고    scopus 로고
    • Relational conffdence bounds are easy with the bootstrap
    • A. Pol and C. Jermaine. Relational conffdence bounds are easy with the bootstrap. In SIGMOD, pages 587-598, 2005.
    • (2005) SIGMOD , pp. 587-598
    • Pol, A.1    Jermaine, C.2
  • 25
    • 60849112285 scopus 로고    scopus 로고
    • Automatic block-length selection for the dependent bootstrap
    • D. N. Politis and H. White. Automatic block-length selection for the dependent bootstrap. Econometric Reviews, 28(4):372-375, 2009.
    • (2009) Econometric Reviews , vol.28 , Issue.4 , pp. 372-375
    • Politis, D.N.1    White, H.2
  • 26
    • 41149110050 scopus 로고    scopus 로고
    • Disk failures in the real world: what does an mttf of 1,000 000 hours mean to you?
    • B. Schroeder and G. A. Gibson. Disk failures in the real world: what does an mttf of 1,000,000 hours mean to you? In USENIX FAST, pages 1-12, 2007.
    • (2007) USENIX FAST , pp. 1-12
    • Schroeder, B.1    Gibson, G.A.2
  • 27
  • 28
    • 84873101231 scopus 로고    scopus 로고
    • A bootstrap approach to making sample-size calculations for resource surveys
    • S. E. Syrjala. A bootstrap approach to making sample-size calculations for resource surveys. In SSC, pages 53-60, 2001.
    • (2001) SSC , pp. 53-60
    • Syrjala, S.E.1
  • 29
    • 85039670960 scopus 로고    scopus 로고
    • Bootstrapping, jackkniffng and cross validation, reusing your data. Utah University Lecture
    • A. Thomas. Bootstrapping, jackkniffng and cross validation, reusing your data. Utah University Lecture, 2000.
    • (2000)
    • Thomas, A.1
  • 31
    • 71749094178 scopus 로고    scopus 로고
    • Parallel k-means clustering based on mapreduce
    • W. Zhao, H. Ma, and Q. He. Parallel k-means clustering based on mapreduce. In CloudCom, pages 674-679, 2009.
    • (2009) In CloudCom , pp. 674-679
    • Zhao, W.1    Ma, H.2    He, Q.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.