NDLI: Continuous similarity join on data streams

Content Provider	IEEE Xplore Digital Library
Author	Jia Cui Weiping Wang Dan Meng Zhenyan Liu
Copyright Year	2014
Description	Author affiliation: Center of Comput. Applic. Res., Inst. of Comput. Technol., Beijing, China (Jia Cui; Zhenyan Liu) \|\| Nat. Eng. Lab. of Inf.-Security Technol., Inst. of Inf. Eng., Beijing, China (Weiping Wang; Dan Meng)
Abstract	Similarity join plays an important role in many applications, such as data cleaning and integration, to address the poor data quality problem. Most of the existing studies focused on performing similarity join on static datasets but few studies realized running it on dynamic data streams. With the development of network technology, the data accessing paradigm has transferred from disk-oriented mode to online data streams, which makes performing similarity join in continuous query on data streams become a novel query processing paradigm. Different from static dataset, data stream is unbounded, continuous and unpredictable. The significant differences pose serious challenges, such as real-time query performance. To this end, we study the problem of continuous similarity join on data streams in this paper, which is based on edit distance metric and filter-and-verify framework with sliding-window semantics. Two subcases of this problem are studied, including self similarity join on a single data stream and similarity join on two streams. We introduced the basic window based sliding window model to facilitate the update of sliding window and its index. More details of our method, including signature extraction schemes, filtering and verification algorithms, re-evaluation strategies are discussed respectively. Finally, extensive experimental results show that our method works efficiently on real data streams.
Sponsorship	IEEE Comput. Soc.
Starting Page	552
Ending Page	559
File Size	884957
Page Count	8
File Format	PDF
ISBN	9781479976157
DOI	10.1109/PADSW.2014.7097853
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-16
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering algorithms Filtering Query processing Indexing Heuristic algorithms Real-time systems edit distance similarity join data stream sliding window
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams ∗

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