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Cleanits-MEDetect: Multiple Errors Detection for Time Series Data in Cleanits

  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data quality problems are seriously prevalent in time series data, and the data suffer from types of errors including single-point errors, continuous errors, and contextual errors. Since it is challenging to achieve high accuracy and efficiency in error detection tasks for time series data, we develop error detection system MEDetect in Cleanits, a data cleaning tool for multi-dimensional industrial time series. We propose an integration detection model for multiple errors, which holds the hierarchical variational automatic encoder as the main structure, and we propose a dimensionality reduction method for k-shape based cluster- ing algorithm, which reduces the time costs of the detection process. MEDetect is designed to allow customized error detection, and users can choose detection and repairing algorithms on their demands.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 28th International Conference, DASFAA 2023, Proceedings
EditorsXin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao, Hongzhi Yin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages674-678
Number of pages5
ISBN (Print)9783031306778
DOIs
StatePublished - 2023
Externally publishedYes
Event28th International Conference on Database Systems for Advanced Applications, DASFAA 2023 - Tianjin, China
Duration: 17 Apr 202320 Apr 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13946 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on Database Systems for Advanced Applications, DASFAA 2023
Country/TerritoryChina
CityTianjin
Period17/04/2320/04/23

Keywords

  • Error detection
  • Hierarchical structure
  • Time series data quality

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