Results 111 to 120 of about 10,108 (225)
Interpretable decision-tree induction in a big data parallel framework
International Journal of Applied Mathematics and Computer Science, 2017 When running data-mining algorithms on big data platforms, a parallel, distributed framework, such asMAPREDUCE, may be used. However, in a parallel framework, each individual model fits the data allocated to its own computing node without necessarily ...
Weinberg Abraham Itzhak, Last Mark
doaj +1 more source
QoS-guaranteed resource provisioning for cloud-based MapReduce in dynamical environments
, 2018 How to guarantee Quality of Service (QoS) with the minimum resource cost has become a new problem in cloud-based computation-intensive MapReduce computations. However, the new problem is challenging as the cloud-based MapReduce environment is dynamically
Yu-Chu Tian +5 more
core +1 more source
, 2019 We investigate the problem of MapReduce and coded MapReduce. MapReduce is a programming model for the website search engine. It has three phases: Map, Shuffle, and Reduce.
Li, Xiaoran
core
A Parallel ETL Tool Based on an Improved Chain-MapReduce Framework
Dianxin kexue, 2013 The related work in parallel ETL and common methods to deal with multiple MapReduce jobs were introduced. Then an improved chain-MapReduce framework was presented, based on this framework,a parallel ETL tool was designed.
Bin Wu, Xinguang Liu
doaj
MapReduce cloud computing system applied to predict scheduling mechanism
, 2014 碩士在眾多雲端技術中，MapReduce是Google在雲端技術上所提供出來使用在許多高運算、高儲存量的資料上的一個處理機制。MapReduce所提供的Map和Reduce兩個function，可以讓使用者輕易的將待處理的大量資料自動的完成。因此再藉由Hadoop依據 MapReduce這個架構將概念變成實際的產物，就可以方便使用者來使用。 目前Hadoop的應用絕大部分都還是用在複雜度較低且運算密度較高的程序上如搜尋(Sort)、資料統計等等。 在先前的文獻研究中 ...
鍾弘哲; Chung, Hung-Che
core
A MapReduce iteration framework in local parallel and message synchronization
, 2013 With the development of large-scale distributed computing, Stand-alone operating environment to meet the demand of the time and space overhead of massive data based on.
Zhang HL(张华良) +2 more
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Data stream treatment using sliding windows with MapReduce
Journal of Computer Science and Technology, 2016 Knowledge Discovery in Databases (KDD) techniques present limitations when the volume of data to process is very large. Any KDD algorithm needs to do several iterations on the complete set of data in order to carry out its work.
María José Basgall +2 more
doaj
Scaling simple, compact and extended compact genetic algorithms using MapReduce
, 2010 Data-intensive computing has emerged as a key player for processing large volumes of data exploiting massive parallelism. Data-intensive computing frameworks have shown that terabytes and petabytes of data can be routinely processed.
Verma, Abhishek
core
BIGhybrid - A Toolkit for Simulating MapReduce on Hybrid Infrastructures
, 2014 Cloud computing has increasingly been used as a platform for running large business and data processing applications. Although clouds have become highly popular, when it comes to data processing, the cost of usage is not negligible.
Santos dos Anjos, Julio Cesar +2 more
core +2 more sources
QoS-guaranteed resource provisioning for cloud-based MapReduce
, 2016 This PhD project has investigated how to guarantee the quality of MapReduce services in cloud computing while minimizing the operational cost of the MapReduce services through dynamic resource provisioning.
Xu, Xiaoyong
core