| 数据流历史数据的存储与聚集查询处理算法 |
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| 引用本文： | 张冬冬,李建中,王伟平,郭龙江. 数据流历史数据的存储与聚集查询处理算法[J]. 软件学报, 2005, 16(12): 2089-2098 |
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| 作者姓名： | 张冬冬 李建中 王伟平 郭龙江 |
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| 作者单位： | 哈尔滨工业大学,计算机科学与技术学院,黑龙江,哈尔滨,150001;哈尔滨工业大学,计算机科学与技术学院,黑龙江,哈尔滨,150001;黑龙江大学,计算机科学与技术学院,黑龙江,哈尔滨,150080 |
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| 基金项目： | Supported bv the National Natural Science Foundation of China under Grant No.60273082(国家自然科学基金);the National High-Tech Research and Development Plan of China under Grant No.2002AA444110(国家高技术研究发展计划(863));the National Grand Fundamental Research 973 Program of China under Grant No.G1999032704(国家重点基础研究发展规划(973));the Natural Science Foundation of Heilongjiang Province of China under Grant No.zjg03-05(黑龙江省自然科学基金) |
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| 摘 要： | 目前数据流的研究成果主要集中在分析处理存储于内存中的最近一段时间内的数据流数据,忽略了对数据流历史数据的分析处理与存储管理.提出了一种数据流历史数据的存储管理及聚集查询处理方法,通过对历史数据实施多层递阶抽样存储,并在内存中建立存储数据流历史数据聚集值的HDS-Tree索引,实现对无限数据流历史数据的存储管理,有效地支持各种聚集查询同时,还给出了基于HDS-Tree的聚集查询算法的时间复杂性分析和查询误差分析.理论分析与实验结果表明,该方法可以有效地用于数据流历史数据的存储与分析.
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| 关 键 词： | 数据流 历史数据 聚集算法 HDS-Tree |
| 文章编号： | 1000-9825/2005/16(12)2089 |
| 收稿时间： | 2004-04-24 |
| 修稿时间： | 2005-04-01 |
Algorithms for Storing and Aggregating Historical Streaming Data |
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| ZHANG Dong-Dong,LI Jian-Zhong,WANG Wei-Ping and GUO Long-Jiang. Algorithms for Storing and Aggregating Historical Streaming Data[J]. Journal of Software, 2005, 16(12): 2089-2098 |
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| Authors: | ZHANG Dong-Dong LI Jian-Zhong WANG Wei-Ping GUO Long-Jiang |
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| Affiliation: | 1.School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China; 2.School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China |
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| Abstract: | The current research work over data streams is mainly focused on dealing with the arrival of recent data in memory, neglecting the analysis and management of historical streaming data. An approach is proposed to store and query historical streaming data by using multi-layer recursive sampling method and HDS-Tree structure, which indexes the aggregation of historical streaming data and supports all kinds of aggregation queries over historical streaming data. The time-complexity and the error of aggregation algorithms are also analyzed based on HDS-Tree. The analytical and experimental results show that the approach can be effectively used to store and analyze the historical streaming data. |
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| Keywords: | data streams historical data aggregation algorithm HDS-Tree |
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