Results 21 to 30 of about 10,108 (225)
Mammoth : gearing Hadoop towards memory-intensive MapReduce applications [PDF]
, 2015 The MapReduce platform has been widely used for large-scale data processing and analysis recently. It works well if the hardware of a cluster is well configured.
Hai Jin +15 more
core +1 more source
2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015 MapReduce is a commonly used framework for executing data-intensive jobs on distributed server clusters. We introduce a variant implementation of MapReduce, namely "Coded MapReduce", to substantially reduce the inter-server communication load for the shuffling phase of MapReduce, and thus accelerating its execution.
Songze Li +2 more
openaire +2 more sources
Recognizing Indonesian Acronym and Expansion Pairs with Supervised Learning and MapReduce
Information, 2020 During the previous decades, intelligent identification of acronym and expansion pairs from a large corpus has garnered considerable research attention, particularly in the fields of text mining, entity extraction, and information retrieval.
Taufik Fuadi Abidin +4 more
doaj +1 more source
REST-MapReduce: An Integrated Interface but Differentiated Service
Journal of Applied Mathematics, 2014 With the fast deployment of cloud computing, MapReduce architectures are becoming the major technologies for mobile cloud computing. The concept of MapReduce was first introduced as a novel programming model and implementation for a large set of ...
Jong-Hyuk Park +3 more
doaj +1 more source
Proving Equivalence Between Imperative and MapReduce Implementations Using Program Transformations [PDF]
Electronic Proceedings in Theoretical Computer Science, 2018 Distributed programs are often formulated in popular functional frameworks like MapReduce, Spark and Thrill, but writing efficient algorithms for such frameworks is usually a non-trivial task.
Bernhard Beckert +5 more
doaj +1 more source
On using MapReduce to scale algorithms for Big Data analytics: a case study
Journal of Big Data, 2019 Introduction Many data analytics algorithms are originally designed for in-memory data. Parallel and distributed computing is a natural first remedy to scale these algorithms to “Big algorithms” for large-scale data.
Phongphun Kijsanayothin +2 more
doaj +1 more source
Cloudgene: A graphical execution platform for MapReduce programs on private and public clouds
BMC Bioinformatics, 2012 Background The MapReduce framework enables a scalable processing and analyzing of large datasets by distributing the computational load on connected computer nodes, referred to as a cluster.
Schönherr Sebastian +5 more
doaj +1 more source
ACM Transactions on Architecture and Code Optimization, 2013 The prevalence of chip multiprocessors opens opportunities of running data-parallel applications originally in clusters on a single machine with many cores. MapReduce, a simple and elegant programming model to program large-scale clusters, has recently been shown a promising alternative to harness the multicore platform.
Rong Chen 0001, Haibo Chen 0001
openaire +1 more source
Practical scalable image analysis and indexing using Hadoop [PDF]
, 2012 The ability to handle very large amounts of image data is important for image analysis, indexing and retrieval applications. Sadly, in the literature, scalability aspects are often ignored or glanced over, especially with respect to the intricacies of ...
Hare, Jonathon S. +5 more
core +1 more source
Building cubes with MapReduce [PDF]
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP, 2011 In the last years, the problems of using generic storage techniques for very specific applications has been detected and outlined. Thus, some alternatives to relational DBMSs (e.g., BigTable) are blooming. On the other hand, cloud computing is already a reality that helps to save money by eliminating the hardware as well as software fixed costs and ...
Alberto Abelló +2 more
openaire +1 more source