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An Advanced MapReduce: Cloud MapReduce, Enhancements and Applications
IEEE Transactions on Network and Service Management, 2014Recently, Cloud Computing is attracting great attention due to its provision of configurable computing resources. MapReduce (MR) is a popular framework for data-intensive distributed computing of batch jobs. MapReduce suffers from the following drawbacks: 1. It is sequential in its processing of Map and Reduce Phases 2.
Devendra Dahiphale +7 more
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Proceedings of the second international workshop on MapReduce and its applications, 2011
The MapReduce model of computation and systems that realize the model have simplified large-scale data processing. Recently, Google introduced other models of computation and systems to simplify data processing for a broader class of important computations.
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The MapReduce model of computation and systems that realize the model have simplified large-scale data processing. Recently, Google introduced other models of computation and systems to simplify data processing for a broader class of important computations.
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2018
Diabetes Mellitus has turned into a noteworthy general wellbeing issue in India. Most recent measurements on diabetes uncover that 63 million individuals in India are experiencing diabetes, and this figure is probably going to go up to 80 million by 2025.
Utkarsh Srivastava, null Ramanathan L.
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Diabetes Mellitus has turned into a noteworthy general wellbeing issue in India. Most recent measurements on diabetes uncover that 63 million individuals in India are experiencing diabetes, and this figure is probably going to go up to 80 million by 2025.
Utkarsh Srivastava, null Ramanathan L.
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Simplifying MapReduce Data Processing
2011 Fourth IEEE International Conference on Utility and Cloud Computing, 2011MapReduce is a programming model developed by Google for processing and generating large data sets in distributed environments. Many real-world tasks can be implemented by two functions, map and reduce. MapReduce plays a key role in Cloud Computing, since it decreases the complexity of the distributed programming and is easy to be developed on large ...
Chih Shan Liao +2 more
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h-MapReduce: A Framework for Workload Balancing in MapReduce
2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), 2013The big data analytics community has accepted MapReduce as a programming model for processing massive data on distributed systems such as a Hadoop cluster. MapReduce has been evolving to improve its performance. We identified skewed workload among workers in the MapReduce ecosystem.
V. S. Martha +2 more
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Tagged MapReduce: Efficiently Computing Multi-analytics Using MapReduce
2011MapReduce is a programming paradigm for effective processing of large datasets in distributed environments, using the map and reduce functions. The map process creates (key, value) pairs, while the reduce phase aggregates same-key values. In other words, a MapReduce application defines and reduces one set of values for each key, which means that the ...
Andreas Williams +2 more
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2013
From automatically translating documents to analyzing electoral voting patterns; from computing personalized movie recommendations to predicting flu epidemics: all of these tasks are possible due to the success and proliferation of the MapReduce parallel programming paradigm.
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From automatically translating documents to analyzing electoral voting patterns; from computing personalized movie recommendations to predicting flu epidemics: all of these tasks are possible due to the success and proliferation of the MapReduce parallel programming paradigm.
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Incremental MapReduce Computations
2006Distributed processing of large data sets is an area that received much attention from researchers and practitioners over the last few years. In this context, several proposals exist that leverage the observation that data sets evolve over time, and as such there is often a substantial overlap between the input to consecutive runs of a data processing ...
Bhatotia, Pramod +3 more
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