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WANG Min, PENG Cheng-chen, LI Rong-rong, PENG Yu-wei. Data correlation-based partition approach for distributed deputy database[J]. Journal of East China Normal University (Natural Sciences), 2016, (5): 45-55. doi: 10.3969/j.issn.1000-5641.2016.05.006
Citation:
WANG Min, PENG Cheng-chen, LI Rong-rong, PENG Yu-wei. Data correlation-based partition approach for distributed deputy database[J]. Journal of East China Normal University (Natural Sciences), 2016, (5): 45-55. doi: 10.3969/j.issn.1000-5641.2016.05.006
WANG Min, PENG Cheng-chen, LI Rong-rong, PENG Yu-wei. Data correlation-based partition approach for distributed deputy database[J]. Journal of East China Normal University (Natural Sciences), 2016, (5): 45-55. doi: 10.3969/j.issn.1000-5641.2016.05.006
Citation:
WANG Min, PENG Cheng-chen, LI Rong-rong, PENG Yu-wei. Data correlation-based partition approach for distributed deputy database[J]. Journal of East China Normal University (Natural Sciences), 2016, (5): 45-55. doi: 10.3969/j.issn.1000-5641.2016.05.006
Object deputy database (ODDB) is an advanced database system with strong ability of complex information processing. With the rapid development of data, distributed storage becomes more and more important to ODDB. However, there exist correlations between objects in ODDB, which makes the traditional data portioning method of distributed storage unsuitable. To solve this problem, we propose a data correlation-based partition approach for ODDB. Firstly, we cluster correlated objects according to the deputy tree, and each object cluster is considered as a heap file in storage. Secondly, on the basis of schema feature and semantic feature, we divide object clusters into k subsets using k-means, each subset is stored on one of the storage nodes. Finally, we compare our method with random distributed storage, the results show that our approach is obviously better in query efficiency.
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