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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Juan Zhang Jiongmin Zhang |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China (Juan Zhang; Jiongmin Zhang) |
| 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. |
| Sponsorship | Hubei Univ. Automot. Technol. |
| Starting Page | 980 |
| Ending Page | 983 |
| File Size | 588605 |
| Page Count | 4 |
| File Format | |
| ISBN | 9780769550046 |
| DOI | 10.1109/ICCIS.2013.263 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Context Algorithm design and analysis Cloud computing Databases Computational modeling Buildings Semantics QC-Tree Hadoop HBS Hbase MapReduce |
| Content Type | Text |
| Resource Type | Article |
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