Results 181 to 190 of about 8,758 (210)
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Tuple MapReduce: Beyond Classic MapReduce
2012 IEEE 12th International Conference on Data Mining, 2012This paper proposes Tuple Map Reduce, a new foundational model extending Map Reduce with the notion of tuples. Tuple Map Reduce allows to bridge the gap between the low-level constructs provided by Map Reduce and higher-level needs required by programmers, such as compound records, sorting or joins.
Pedro Ferrera +4 more
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Communications of the ACM, 2008
MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically ...
Jeffrey Dean, Sanjay Ghemawat
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MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically ...
Jeffrey Dean, Sanjay Ghemawat
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Communications of the ACM, 2010
MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs.
Jeffrey Dean, Sanjay Ghemawat
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MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs.
Jeffrey Dean, Sanjay Ghemawat
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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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Cross-MapReduce: Data transfer reduction in geo-distributed MapReduce
Future Generation Computer Systems, 2021Abstract The MapReduce model is widely used to store and process big data in a distributed manner. MapReduce was originally developed for a single tightly coupled cluster of computers. Approaches such as Hierarchical and Geo-Hadoop are designed to address geo-distributed MapReduce processing.
Saeed Mirpour Marzuni +3 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 ...
openaire +1 more source