Study on QC-Tree with MapReduce and Hbase in Hadoop

Author

Juan Zhang ; Jiongmin Zhang

Author_Institution

Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China

fYear

2013

fDate

21-23 June 2013

Firstpage

980

Lastpage

983

Abstract

Cloud computing provides the possibility of a solution to the problems caused by the massive amounts of data. As an open source cloud computing platform, Hadoop has been widely used in the commercial. MapReduce model is one of the important parts of Hadoop, and it can support parallel computing and schedule tasks automatically. Because of these, it can improve the efficiency of the configuration while programmers only have to do very little work on it. Combining the advantages of the QC-Tree and Hadoop platform, we finished building QC-Tree in Hadoop and use the Hbase to solve the problem on memory. At the same time, we give the algorithm called HBS to improve query efficiency of the QC-Tree which is stored in Hbase.

Keywords

cloud computing; distributed databases; parallel processing; public domain software; query processing; scheduling; HBS; Hadoop; Hbase; MapReduce model; QC-tree; open source cloud computing platform; parallel computing; query efficiency; task scheuling; Algorithm design and analysis; Buildings; Cloud computing; Computational modeling; Context; Databases; Semantics; HBS; Hadoop; Hbase; MapReduce; QC-Tree;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on

Conference_Location

Shiyang

Type

conf

DOI

10.1109/ICCIS.2013.263

Filename

6643179