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https://dl.acm.org/ft_gateway.cfm?id=1807295&ftid=805278&dwn=1
8 rows · Online aggregation and continuous query support in MapReduce. Full Text: PDF ... also supports continuous queries, which enable MapReduce programs to be written for applications such as event monitoring and stream processing. HOP retains the fault tolerance properties of Hadoop, and can run unmodified user-defined MapReduce programs ...Cited by: 203
https://www.researchgate.net/publication/221213893_Online_aggregation_and_continuous_query_support_in_MapReduce
Online aggregation and continuous query support in MapReduce. ... MapReduce framework that supports online aggregation, which allows users to see "early returns" from a job as it is being computed ...
http://www.neilconway.org/docs/sigmod2010_hop_demo.pdf
Online Aggregation and Continuous Query support in MapReduce Tyson Condie, Neil Conway, Peter Alvaro, Joseph M. Hellerstein UC Berkeley John Gerth, Justin Talbot Stanford University Khaled Elmeleegy, Russell Sears Yahoo! Research ABSTRACT MapReduce is a popular framework for data-intensive distributed computing of batch jobs.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.232.6892
online aggregation continuous query support batch job unmodified user-defined mapreduce program popular framework modified version completion time hadoop mapreduce framework data-intensive distributed computing early return system utilization modified mapreduce architecture hadoop online prototype fault tolerance property mapreduce task event ...
https://dl.acm.org/citation.cfm?id=1855732
Tyson Condie , Neil Conway , Peter Alvaro , Joseph M. Hellerstein , John Gerth , Justin Talbot , Khaled Elmeleegy , Russell Sears, Online aggregation and continuous query support in MapReduce, Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, June 06-10, 2010, Indianapolis, Indiana, USACited by: 1023
https://core.ac.uk/display/21907933
This extends the MapReduce programming model beyond batch processing, and can reduce completion times and improve system utilization for batch jobs as well. We demonstrate a modified version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see “early returns ” from a job as it is being computed.
http://issues.apache.org/jira/browse/MAPREDUCE-1211
This extends the MapReduce programming model beyond batch processing, and can reduce completion times and improve system utilization for batch jobs as well. We have built a modified version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see "early returns" from a job as it is being computed.
http://www.vldb.org/pvldb/vol4/p1135-pansare.pdf
Online Aggregation for Large MapReduce Jobs Niketan Pansare1, Vinayak Borkar2, Chris Jermaine1, Tyson Condie3 1Rice University, 2UC Irvine, 3Yahoo! Research [email protected], [email protected], [email protected], tcondie@yahooinc.com ABSTRACT In online aggregation, a database system processes a user’s aggre-gation query in an online fashion.
https://static.usenix.org/event/nsdi10/tech/full_papers/condie.pdf
of the Hadoop MapReduce framework that supports on-line aggregation, which allows users to see “early returns” from a job as it is being computed. Our Hadoop Online Prototype (HOP) also supports continuous queries, which enable MapReduce programs to be written for applica-tions such as event monitoring and stream processing.
https://dirtysalt.github.io/html/mapreduce-online.html
This ex- tends the MapReduce programming model beyond batch processing, and can reduce completion times and im- prove system utilization for batch jobs as well. We present a modified version of the Hadoop MapReduce framework that supports online aggregation, which al- lows users to see “early returns” from a job as it is being computed.
https://dl.acm.org/citation.cfm?id=1807295
Online aggregation and continuous query support in MapReduce. Full Text: PDF ... also supports continuous queries, which enable MapReduce programs to be written for applications such as event monitoring and stream processing. HOP retains the fault tolerance properties of Hadoop, and can run unmodified user-defined MapReduce programs ...Cited by: 206
http://www.neilconway.org/docs/sigmod2010_hop_demo.pdf
Online Aggregation and Continuous Query support in MapReduce Tyson Condie, Neil Conway, Peter Alvaro, Joseph M. Hellerstein UC Berkeley John Gerth, Justin Talbot Stanford University Khaled Elmeleegy, Russell Sears Yahoo! Research ABSTRACT MapReduce is a popular framework for data-intensive distributed computing of batch jobs.
https://www.researchgate.net/publication/221213893_Online_aggregation_and_continuous_query_support_in_MapReduce
Online aggregation and continuous query support in MapReduce. ... MapReduce framework that supports online aggregation, which allows users to see "early returns" from a job as it is being computed ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.232.6892
online aggregation continuous query support batch job unmodified user-defined mapreduce program popular framework modified version completion time hadoop mapreduce framework data-intensive distributed computing early return system utilization modified mapreduce architecture hadoop online prototype fault tolerance property mapreduce task event ...
http://issues.apache.org/jira/browse/MAPREDUCE-1211
This extends the MapReduce programming model beyond batch processing, and can reduce completion times and improve system utilization for batch jobs as well. We have built a modified version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see "early returns" from a job as it is being computed.
https://core.ac.uk/display/21907933
This extends the MapReduce programming model beyond batch processing, and can reduce completion times and improve system utilization for batch jobs as well. We demonstrate a modified version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see “early returns ” from a job as it is being computed.
https://dl.acm.org/citation.cfm?id=1855732
Tyson Condie , Neil Conway , Peter Alvaro , Joseph M. Hellerstein , John Gerth , Justin Talbot , Khaled Elmeleegy , Russell Sears, Online aggregation and continuous query support in MapReduce, Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, June 06-10, 2010, Indianapolis, Indiana, USACited by: 1032
https://dirtysalt.github.io/html/mapreduce-online.html
This ex- tends the MapReduce programming model beyond batch processing, and can reduce completion times and im- prove system utilization for batch jobs as well. We present a modified version of the Hadoop MapReduce framework that supports online aggregation, which al- lows users to see “early returns” from a job as it is being computed.
https://link.springer.com/article/10.1007/s10619-014-7141-2
Jan 30, 2014 · To eliminate such additional execution cost and improve the overall performance, we present online aggregation with two-level sharing strategy in cloud (OATS) based on MapReduce framework in this paper to effectively support online aggregation for large scale concurrent query processing in skewed data distribution.Cited by: 10
https://static.usenix.org/event/nsdi10/tech/full_papers/condie.pdf
of the Hadoop MapReduce framework that supports on-line aggregation, which allows users to see “early returns” from a job as it is being computed. Our Hadoop Online Prototype (HOP) also supports continuous queries, which enable MapReduce programs to be written for applica-tions such as event monitoring and stream processing.
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