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A CPM-Based Change Detection Test for Big Data

  • Giada Tacconelli
  • Manuel RoveriEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 529)

Abstract

Big data analytics nowadays represent one of the most relevant and promising research activities in the field of Big Data. Tools and solutions designed for such purpose are meant to analyse very large sets ot data to extract relevant/valuable information. In this path, this paper addresses the problem of sequentially analysing big streams of data inspecting for changes. This problem that has been extensively studied for scalar or multivariate datastreams, has been mostly left unattended in the Big Data scenario. More specifically, the aim of this paper is to introduce a change detection test able to detect changes in datastreams characterized by very-large dimensions (up to 1000). The proposed test, based on a change-point method, is non parameteric (in the sense that it does not require any apriori information about the system under inspection or the possible changes) and is designed to detect changes in the mean vector of the datastreams. The effectiveness and the efficiency of the proposed change detection test has been tested on both synthetic and real datasets.

Keywords

Real Dataset Detection Ability False Positive Detection Detection Delay Synthetic Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Politecnico di MilanoMilanoItaly

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