.png){kind=icon}
Return
Distributed Data Management Using MapReduce
.png){kind=icon}
OA
AI
DOI：10.1145/2503009.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
MapReduce is a framework for processing and managing large-scale datasets in a distributed cluster, which has been used for applications such as generating search indexes, document clustering, access log analysis, and various other forms of data analytics. MapReduce adopts a flexible computation model with a simple interface consisting of map and reduce functions whose implementations can be customized by application developers. Since its introduction, a substantial amount of research effort has been directed toward making it more usable and efficient for supporting database-centric operations. In this article, we aim to provide a comprehensive review of a wide range of proposals and systems that focusing fundamentally on the support of distributed data management and processing using the MapReduce framework.
Keywords:
Algorithms
Performance
MapReduce
scalability
Hadoop
Journal
IF:
28
Papers:
2.5K
Citations:
3.5W
Organization
View more organizations
Cited Papers
Classifying hand sensorimotor functions of the chronic kidney disease patients using novel manual tactile test and pinch-holding-up activity.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSaveShareSaveShareSaveShareSaveIdentification of two immortalized cell lines, ECV304 and bEnd3, for in vitro permeability studies of blood-brain barrierPLOS ONEOAAI
PLOS ONEIF0
OAAIShareSaveShareSaveMetal carbonyls—II Carbon monoxide and chloride exchange with some cyclopentadienyl metal carbonyls
PREAI
ShareSaveShareSave View more