NDLI: MRST—An Efficient Monitoring Technology of Summarization on Stream Data

Content Provider	Springer Nature Link
Author	Fan, Xiao Bo Xie, Ting Ting Li, Cui Ping Chen, Hong
Copyright Year	2007
Abstract	Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree (MRST) which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.
Starting Page	190
Ending Page	196
Page Count	7
File Format	PDF
ISSN	10009000
Journal	Journal of Computer Science and Technology
Volume Number	22
Issue Number	2
e-ISSN	18604749
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2007-04-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Haar wavelet sliding window stream data Computer Science Software Engineering Theory of Computation Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Information Systems Applications (incl.Internet)
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Computer Science Applications Software Hardware and Architecture

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MRST—An Efficient Monitoring Technology of Summarization on Stream Data

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