DataCloud:An Efficient Massive Data Mining and Analysis Framework on Large Clusters

机译：DataCloud：大型集群上的高效海量数据挖掘和分析框架

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摘要

with the development of cloud computing technologies,big data processing is becoming more and more important.How to mine and analyze massive data is facing a very big challenge.In this paper,we proposed an efficient massive data mining and analysis framework DataCloud on large clusters.The most important part of DataCloud is the Rabbit.It is a kind of massive data mining and analysis processing plan framework on the large clusters like the Pig and Hive.We make a detail analysis about the Rabbit plan.

机译：随着云计算技术的发展，大数据处理变得越来越重要。如何挖掘和分析海量数据面临着很大的挑战。本文提出了一种在大型集群上高效的海量数据挖掘与分析框架DataCloud。 DataCloud最重要的部分是Rabbit，它是对Pig和Hive等大型集群的海量数据挖掘和分析处理计划框架，我们对Rabbit计划进行详细分析。

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来源
《2012 Ninth Web Information Systems and Applications Conference》|2012年|198-203|共6页
会议地点 Haikou(CN)
作者
Guigang Zhang; Chao Li; Yong Zhang; Chunxiao Xing1;
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作者单位

Research Institute of Information Technology, Tsinghua National Laboratory for Information Science and Technology,Tsinghua University, Beijing 100084, China;

Tsinghua National Laboratory for Information Science and Technology, Tsinghua National Laboratory for InformationScience and Technology, Tsinghua University, Beijing 100084, China;

Department of Computer Science and Technology, Tsinghua University, Beijing 100084,China;

Research Institute of Information Technology, Tsinghua National Laboratory for Information Science and Technology,Tsinghua University, Beijing 100084, China;

Tsinghua National Laboratory for Information Science and Technology, Tsinghua National Laboratory for InformationScience and Technology, Tsinghua University, Beijing 100084, China;

Department of Computer Science and Technology, Tsinghua University, Beijing 100084,China;

Research Institute of Information Technology, Tsinghua National Laboratory for Information Science and Technology,Tsinghua University, Beijing 100084, China;

Tsinghua National Laboratory for Information Science and Technology, Tsinghua National Laboratory for InformationScience and Technology, Tsinghua University, Beijing 100084, China;

Department of Computer Science and Technology, Tsinghua University, Beijing 100084,China;

Research Institute of Information Technology, Tsinghua National Laboratory for Information Science and Technology,Tsinghua University, Beijing 100084, China;

Tsinghua National Laboratory for Information Science and Technology, Tsinghua National Laboratory for InformationScience and Technology, Tsinghua University, Beijing 100084, China;

Department of Computer Science and Technology, Tsinghua University, Beijing 100084,China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类计算机网络;
关键词
DataCloud; Massive data management; Rabbit; Cloud computing;

机译：DataCloud;海量数据管理;兔子;云计算;

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1. An efficient data processing framework for mining the massive trajectory of moving objects [J] . Zhou Yuanchun, Zhang Yang, Ge Yong, Computers，environment and urban systems . 2017,第ptaB期

机译：一个有效的数据处理框架，可用于挖掘运动对象的大量轨迹
2. Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats [J] . Richard Tran Mills, Forrest M. Hoffman, Jitendra Kumar, Procedia Computer Science . 2011,第1期

机译：基于聚类分析的海量数据集地理时空数据挖掘方法，用于森林威胁识别
3. Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes [J] . Villez K, Ruiz M, Sin G, Water Science and Technology . 2008,第10期

机译：结合多路主成分分析（MPCA）和聚类，可对SBR过程的历史数据集进行有效的数据挖掘
4. DataCloud: An Efficient Massive Data Mining and Analysis Framework on Large Clusters [C] . Zhang Guigang, Li Chao, Zhang Yong, 2012 Ninth Web Information Systems and Applications Conference. . 2012

机译：DataCloud：大型集群上的高效海量数据挖掘和分析框架
5. Mining massive moving object datasets from RFID flow analysis to traffic mining [D] . Gonzalez, Hector 2008

机译：从RFID流量分析到流量挖掘，挖掘海量移动物体数据集
6. Efficient Mining of Discriminative Co-clusters from Gene Expression Data [O] . Omar Odibat, Chandan K. Reddy -1

机译：从基因表达数据有效挖掘区分性共群
7. Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats [O] . Prof Satoshi Matsuoka, Richard Tran Millsa, Forrest M. Hoffmana, 2015

机译：基于聚类分析的海量数据集地理时空数据挖掘方法用于森林威胁识别
8. Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats. [R] . Mills, R. T., Hoffman, F. M., Kumar, J., 2011

机译：基于聚类分析的海量数据集地理时空数据挖掘方法用于森林威胁识别。

DataCloud:An Efficient Massive Data Mining and Analysis Framework on Large Clusters

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