Paper Info
Title
MRST: an efficient monitoring technology of summarization on stream data
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.
Year
DOI
Venue
2007
10.1007/s11390-007-9025-7
J. Comput. Sci. Technol.
Keywords
Field
DocType
real-world data,current algorithm,open challenging problem,limited resource,stream data,multi-resolution summarization tree,infinite data stream,incoming data,efficient monitoring technology,synthetic data,data stream,multi-resolution summarization data structure,data structure,range query,sliding window
Data structure,Data mining,Automatic summarization,Data stream mining,Data stream clustering,Sliding window protocol,Computer science,Data stream,Range query (data structures),Synthetic data
Journal
Volume
Issue
ISSN
22
2
1860-4749
Citations 
PageRank 
References 
0
0.34
22
Authors
4
Name
Order
Citations
PageRank
Xiaobo Fan184898.89
Ting-Ting Xie220.73
Cuiping Li349243.56
Hong Chen49923.20