메뉴 건너뛰기




Volumn 7, Issue 13, 2014, Pages 1441-1451

Summingbird: A framework for integrating batch and online MapReduce computations

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; PROBLEM ORIENTED LANGUAGES; SOCIAL NETWORKING (ONLINE);

EID: 84905858499     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/2733004.2733016     Document Type: Conference Paper
Times cited : (88)

References (40)
  • 4
    • 0014814325 scopus 로고
    • Space/time trade-offs in hash coding with allowable errors
    • B. Bloom. Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7):422-426, 1970.
    • (1970) Communications of the ACM , vol.13 , Issue.7 , pp. 422-426
    • Bloom, B.1
  • 5
    • 84904136037 scopus 로고    scopus 로고
    • Large-scale machine learning with stochastic gradient descent
    • L. Bottou. Large-scale machine learning with stochastic gradient descent. COMPSTAT, 2010.
    • (2010) COMPSTAT
    • Bottou, L.1
  • 6
    • 0031346696 scopus 로고    scopus 로고
    • On the resemblance and containment of documents
    • A. Z. Broder. On the resemblance and containment of documents. SEQUENCES, 1997.
    • (1997) SEQUENCES
    • Broder, A.Z.1
  • 9
    • 14844367057 scopus 로고    scopus 로고
    • An improved data stream summary: The count-min sketch and its applications
    • G. Cormode and S. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. Journal of Algorithms, 55(1):58-75, 2005.
    • (2005) Journal of Algorithms , vol.55 , Issue.1 , pp. 58-75
    • Cormode, G.1    Muthukrishnan, S.2
  • 11
    • 55649105542 scopus 로고    scopus 로고
    • SPADE: The System S declarative stream processing engine
    • B. Gedik, H. Andrade, K.-L. Wu, P. Yu, and M. Doo. SPADE: The System S declarative stream processing engine. SIGMOD, 2008.
    • (2008) SIGMOD
    • Gedik, B.1    Andrade, H.2    Wu, K.-L.3    Yu, P.4    Doo, M.5
  • 14
    • 84893318699 scopus 로고    scopus 로고
    • Hourglass: A library for incremental processing on Hadoop
    • M. Hayes and S. Shah. Hourglass: A library for incremental processing on Hadoop. Big Data, 2013.
    • (2013) Big Data
    • Hayes, M.1    Shah, S.2
  • 15
    • 84887748650 scopus 로고    scopus 로고
    • HyperLogLog in practice: Algorithmic engineering of a state of the art cardinality estimation algorithm
    • S. Heule, M. Nunkesser, and A. Hall. HyperLogLog in practice: Algorithmic engineering of a state of the art cardinality estimation algorithm. EDBT, 2013.
    • (2013) EDBT
    • Heule, S.1    Nunkesser, M.2    Hall, A.3
  • 17
    • 84897489756 scopus 로고    scopus 로고
    • Algebraic classifiers: A generic approach to fast cross-validation, online training, and parallel training
    • M. Izbicki. Algebraic classifiers: A generic approach to fast cross-validation, online training, and parallel training. ICML, 2013.
    • (2013) ICML
    • Izbicki, M.1
  • 18
    • 84873171258 scopus 로고    scopus 로고
    • Kafka: A distributed messaging system for log processing
    • J. Kreps, N. Narkhede, and J. Rao. Kafka: A distributed messaging system for log processing. NetDB, 2011.
    • (2011) NetDB
    • Kreps, J.1    Narkhede, N.2    Rao, J.3
  • 20
    • 77952270709 scopus 로고    scopus 로고
    • DEDUCE: At the intersection of MapReduce and stream processing
    • V. Kumar, H. Andrade, B. Gedik, and K.-L. Wu. DEDUCE: At the intersection of MapReduce and stream processing. EDBT, 2010.
    • (2010) EDBT
    • Kumar, V.1    Andrade, H.2    Gedik, B.3    Wu, K.-L.4
  • 22
    • 84984039280 scopus 로고
    • Hints for computer system design
    • B. Lampson. Hints for computer system design. SOSP, 1983.
    • (1983) SOSP
    • Lampson, B.1
  • 23
    • 84873206169 scopus 로고    scopus 로고
    • The unified logging infrastructure for data analytics at Twitter
    • G. Lee, J. Lin, C. Liu, A. Lorek, and D. Ryaboy. The unified logging infrastructure for data analytics at Twitter. VLDB, 2012.
    • (2012) VLDB
    • Lee, G.1    Lin, J.2    Liu, C.3    Lorek, A.4    Ryaboy, D.5
  • 24
    • 84862684679 scopus 로고    scopus 로고
    • Large-scale machine learning at Twitter
    • J. Lin and A. Kolcz. Large-scale machine learning at Twitter. SIGMOD, 2012.
    • (2012) SIGMOD
    • Lin, J.1    Kolcz, A.2
  • 25
    • 84893267530 scopus 로고    scopus 로고
    • Scaling big data mining infrastructure: The Twitter experience
    • J. Lin and D. Ryaboy. Scaling big data mining infrastructure: The Twitter experience. SIGKDD Explorations, 14(2):6-19, 2012.
    • (2012) SIGKDD Explorations , vol.14 , Issue.2 , pp. 6-19
    • Lin, J.1    Ryaboy, D.2
  • 27
    • 79953647392 scopus 로고    scopus 로고
    • A co-relational model of data for large shared data banks
    • E. Meijer and G. Bierman. A co-relational model of data for large shared data banks. Communications of the ACM, 54(4):49-58, 2011.
    • (2011) Communications of the ACM , vol.54 , Issue.4 , pp. 49-58
    • Meijer, E.1    Bierman, G.2
  • 28
    • 84880559221 scopus 로고    scopus 로고
    • Fast data in the era of big data: Twitter's real-time related query suggestion architecture
    • G. Mishne, J. Dalton, Z. Li, A. Sharma, and J. Lin. Fast data in the era of big data: Twitter's real-time related query suggestion architecture. SIGMOD, 2013.
    • (2013) SIGMOD
    • Mishne, G.1    Dalton, J.2    Li, Z.3    Sharma, A.4    Lin, J.5
  • 29
    • 84990965478 scopus 로고    scopus 로고
    • Information network or social network? The structure of the Twitter follow graph
    • S. A. Myers, A. Sharma, P. Gupta, and J. Lin. Information network or social network? The structure of the Twitter follow graph. WWW Companion, 2014.
    • (2014) WWW Companion
    • Myers, S.A.1    Sharma, A.2    Gupta, P.3    Lin, J.4
  • 30
    • 84879520704 scopus 로고    scopus 로고
    • Incremental stream processing using computational conflict-free replicated data types
    • D. Navalho, S. Duarte, N. Preguiça, and M. Shapiro. Incremental stream processing using computational conflict-free replicated data types. CloudDP, 2013.
    • (2013) CloudDP
    • Navalho, D.1    Duarte, S.2    Preguiça, N.3    Shapiro, M.4
  • 36
    • 85002655794 scopus 로고    scopus 로고
    • Stream-monitoring with BlockMon: Convergence of network measurements and data analytics platforms
    • D. Simoncelli, M. Dusi, F. Gringoli, and S. Niccolini. Stream-monitoring with BlockMon: Convergence of network measurements and data analytics platforms. ACM SIGCOMM Computer Communication Review, 43(2):30-35, 2013.
    • (2013) ACM SIGCOMM Computer Communication Review , vol.43 , Issue.2 , pp. 30-35
    • Simoncelli, D.1    Dusi, M.2    Gringoli, F.3    Niccolini, S.4
  • 38
    • 70350591395 scopus 로고    scopus 로고
    • DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language
    • Y. Yu, M. Isard, D. Fetterly, M. Budiu, Ú. Erlingsson, P. K. Gunda, and J. Currey. DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language. OSDI, 2008.
    • (2008) OSDI
    • Yu, Y.1    Isard, M.2    Fetterly, D.3    Budiu, M.4    Erlingsson, Ú.5    Gunda, P.K.6    Currey, J.7
  • 40
    • 84962598306 scopus 로고    scopus 로고
    • Discretized streams: An efficient and fault-tolerant model for stream processing on large clusters
    • M. Zaharia, T. Das, H. Li, S. Shenker, and I. Stoica. Discretized streams: An efficient and fault-tolerant model for stream processing on large clusters. HotCloud, 2012.
    • (2012) HotCloud
    • Zaharia, M.1    Das, T.2    Li, H.3    Shenker, S.4    Stoica, I.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.