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2017 International Conference on High Performance Computing & Simulation (HPCS), 2017
Integrity constraints (ICs) such as Functional Dependencies (FDs) or Inclusion Dependencies (INDs) are commonly used in databases to check if input relations obey to certain pre-defined quality metrics. While Data-Intensive Scalable Computing (DISC) platforms such as MapReduce commonly accept as input (semi-structured) data not in relational format ...
Interlandi, Matteo +3 more
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Integrity constraints (ICs) such as Functional Dependencies (FDs) or Inclusion Dependencies (INDs) are commonly used in databases to check if input relations obey to certain pre-defined quality metrics. While Data-Intensive Scalable Computing (DISC) platforms such as MapReduce commonly accept as input (semi-structured) data not in relational format ...
Interlandi, Matteo +3 more
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Proceedings of the 22nd International Conference on World Wide Web, 2013
JSMapReduce is an implementation of MapReduce which exploits the computing power available in the computers of the users of a web platform by giving tasks to the JavaScript engines of their web browsers. This article describes the implementation of JSMapReduce exploiting HTML 5 features, the heuristics it uses for distributing tasks to workers, and ...
Philipp Langhans +2 more
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JSMapReduce is an implementation of MapReduce which exploits the computing power available in the computers of the users of a web platform by giving tasks to the JavaScript engines of their web browsers. This article describes the implementation of JSMapReduce exploiting HTML 5 features, the heuristics it uses for distributing tasks to workers, and ...
Philipp Langhans +2 more
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Proceedings of the VLDB Endowment, 2009
A user-defined function (UDF) is a powerful database feature that allows users to customize database functionality. Though useful, present UDFs have numerous limitations, including install-time specification of input and output schema and poor ability to parallelize execution.
Eric Friedman +2 more
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A user-defined function (UDF) is a powerful database feature that allows users to customize database functionality. Though useful, present UDFs have numerous limitations, including install-time specification of input and output schema and poor ability to parallelize execution.
Eric Friedman +2 more
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Proceedings of third international workshop on MapReduce and its Applications Date, 2012
The volume and complexity of data that must be analyzed in scientific applications is increasing exponentially. Often, this data is distributed, thus efficient processing of large distributed datasets is required, whilst ideally not introducing fundamentally new programming models or methods.
Pradeep Kumar Mantha +2 more
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The volume and complexity of data that must be analyzed in scientific applications is increasing exponentially. Often, this data is distributed, thus efficient processing of large distributed datasets is required, whilst ideally not introducing fundamentally new programming models or methods.
Pradeep Kumar Mantha +2 more
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Proceedings of the 2nd IKDD Conference on Data Sciences, 2015
We begin with a sketch of how MapReduce works and how MapReduce algorithms differ from general parallel algorithms. While algorithm analysis usually centers on the serial or parallel running time of the algorithms that solve a given problem, in the MapReduce world, the critical issue is a tradeoff between interprocessor communication and the parallel ...
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We begin with a sketch of how MapReduce works and how MapReduce algorithms differ from general parallel algorithms. While algorithm analysis usually centers on the serial or parallel running time of the algorithms that solve a given problem, in the MapReduce world, the critical issue is a tradeoff between interprocessor communication and the parallel ...
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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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