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COMBAT:A New bitmap index Coding Algorithm for Big Data

Tsinghua Science and Technology 2016年第2期21卷 136-145页

作者： Yinjun Wu Zhen Chen Yuhao Wen Wenxun Zheng Junwei Cao Department of Automation and Tsinghua National Laboratory for Information Science and Technology(TNList) Tsinghua UniversityBeijing 100084China. iCenter of Tsinghua University Beijing 100084China Department of Computer Science Duke UniversityNC 27708USA. Research Institute of Information Technology and Tsinghua National Laboratory for Information Science and Technology(TNList) Tsinghua UniversityBeijing 100084China.

bitmap indexing has been widely used in various applications due to its speed in bitwise operations. However, it can consume large amounts of memory. To solve this problem, various bitmap coding algorithms have been proposed. In this paper, we present COMbining Binary And Ternary encoding （COMBAT）, a new bitmap index coding algorithm. Typical algorithms derived from Word Aligned Hybrid （WAH） are COMPressed Adaptive index （COMPAX） and Compressed ＂n＂ Composable Integer Set （CONCISE）, which can combine either two or three continuous words after WAH encoding. COMBAT combines both mechanisms and results in more compact bitmap indexes. Moreover, querying time of COMBAT can be faster than that of COMPAX and CONCISE, since bitmap indexes are smaller and it would take less time to load them into memory. To prove the advantages of COMBAT, we extend a theoretical analysis model proposed by our group, which is composed of the analysis of various possible bitmap indexes. Some experimental results based on real data are also provided, which show COMBAT＇s storage and speed superiority. Our results demonstrate the advantages of COMBAT and codeword statistics are provided to solidify the proof.

关键词： bitmap index big data COMBAT CONCISE COMPAX index encoding performance evaluation

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PLQ:An Efficient Approach to Processing Pattern-Based Log Queries

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Journal of Computer Science & Technology 2022年第5期37卷 1239-1254页

作者： Jia Chen Peng Wang Fan Qiao Shi-Qing Du Wei Wang School of Computer Science Fudan UniversityShanghai 200082China

As software systems grow more and more complex,extensive techniques have been proposed to analyze the log data to obtain the insight of the system ***,during log data analysis,tedious manual efforts are paid to search interesting or informative log patterns from a huge volume of log data,named pattern-based *** existing log management tools and DMBS systems can also support pattern-based queries,they suffer from a low *** deal with this problem,we propose a novel approach,named PLQ(Pattern-based Log Query).First,PLQ organizes logs into disjoint chunks and builds chunk-wise bitmap indexes for log types and attribute ***,based on bitmap indexes,PLQ finds candidate logs with a set of efficient bit-wise ***,PLQ fetches candidate logs and validates them according to the queried *** experiments are conducted on real-life *** to experimental results,compared with existing log management systems,PLQ is more efficient in querying log patterns and has a higher pruning rate for filtering irrelevant ***,in PLQ,since the ratio of the index size to the data size does not exceed 2.5%for log datasets of different sizes,PLQ has a high scalability.

关键词： pattern query log analysis bitmap index log pattern

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利用位图技术挖掘关联规则的高效算法

利用位图技术挖掘关联规则的高效算法

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第十八届全国数据库学术会议

作者：祁文文谭红星李声威河南大学数学系河南大学计算机学院中科院软件所对象技术中心

1概述企业在利用计算机管理业务的过程中,会积累大量的历史数据。这些数据中常常隐含有未知而有价值的信息,如用户的行为模式或规律等。将这些信息或知识从规模庞大的历史数据中提取出来的过程称为知识发现(Knowledge Discovery in Data... 详细信息

1概述企业在利用计算机管理业务的过程中,会积累大量的历史数据。这些数据中常常隐含有未知而有价值的信息,如用户的行为模式或规律等。将这些信息或知识从规模庞大的历史数据中提取出来的过程称为知识发现(Knowledge Discovery in Databases)或

关键词： Data Mining Association Rules bitmap index

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