MRST: an efficient monitoring technology of summarization on stream data

  • Authors:

  • Xiao-Bo Fan;Ting-Ting Xie;Cui-Ping Li;Hong Chen

  • Affiliations:

  • School of Information, Renmin University of China, Beijing, China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing, China;School of Information, Renmin University of China, Beijing, China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing, China;School of Information, Renmin University of China, Beijing, China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing, China;School of Information, Renmin University of China, Beijing, China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Beijing, China

  • Venue:
  • Journal of Computer Science and Technology
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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.