Data Stream Evolution Diagnosis Using Recursive Wavelet Density Estimators

Author:

Treviño Edgar S. García1,Hameed Muhammad Zaid1,Barria Javier A1

Affiliation:

1. Imperial College London, London, United Kingdom

Abstract

Data streams are a new class of data that is becoming pervasively important in a wide range of applications, ranging from sensor networks, environmental monitoring to finance. In this article, we propose a novel framework for the online diagnosis of evolution of multidimensional streaming data that incorporates Recursive Wavelet Density Estimators into the context of Velocity Density Estimation. In the proposed framework changes in streaming data are characterized by the use of local and global evolution coefficients . In addition, we propose for the analysis of changes in the correlation structure of the data a recursive implementation of the Pearson correlation coefficient using exponential discounting. Two visualization tools, namely temporal and spatial velocity profiles, are extended in the context of the proposed framework. These are the three main advantages of the proposed method over previous approaches: (1) the memory storage required is minimal and independent of any window size; (2) it has a significantly lower computational complexity; and (3) it makes possible the fast diagnosis of data evolution at all dimensions and at relevant combinations of dimensions with only one pass of the data. With the help of the four examples, we show the framework’s relevance in a change detection context and its potential capability for real world applications.

Funder

Imperial College London President's PhD Scholarship

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference31 articles.

1. On change diagnosis in evolving data streams

2. Charu C. Aggarwal. 2007. An introduction to data streams. In Data Streams (The Kluwer International Series on Advances in Database Systems) Charu C. Aggarwal and Ahmed K. Elmagarmid (Eds.). Vol. 31 Springer 1--8. Charu C. Aggarwal. 2007. An introduction to data streams. In Data Streams (The Kluwer International Series on Advances in Database Systems) Charu C. Aggarwal and Ahmed K. Elmagarmid (Eds.). Vol. 31 Springer 1--8.

3. On string classification in data streams

4. Density-Based Clustering over an Evolving Data Stream with Noise

5. Kyle A. Caudle and Edward Wegman. 2009. Nonparametric density estimation of streaming data using orthogonal series. Computational Statistics 8 Data Analysis 53 12 (2009) 3980--3986. 10.1016/j.csda.2009.06.014 Kyle A. Caudle and Edward Wegman. 2009. Nonparametric density estimation of streaming data using orthogonal series. Computational Statistics 8 Data Analysis 53 12 (2009) 3980--3986. 10.1016/j.csda.2009.06.014

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3