research-article

Data Series Similarity Using Correlation-Aware Measures

Authors:

Katsiaryna Mirylenka,

Michele Dallachiesa,

Themis PalpanasAuthors Info & Claims

SSDBM '17: Proceedings of the 29th International Conference on Scientific and Statistical Database Management

Article No.: 11, Pages 1 - 12

https://doi.org/10.1145/3085504.3085515

Published: 27 June 2017 Publication History

Abstract

The increased availability of unprecedented amounts of sequential data (generated by Internet-of-Things, as well as scientific applications) has led in the past few years to a renewed interest and attention to the field of data series processing and analysis. Data series collections are processed and analyzed using a large variety of techniques, most of which are based on the computation of some distance function. In this study, we revisit this basic operation of data series distance calculation. We observe that the popular distance measures are oblivious to the correlations inherent in neighboring values in a data series. Therefore, we evaluate the plausibility and benefit of incorporating into the distance function measures of correlation, which enable us to capture the associations among neighboring values in the sequence. We propose four such measures, inspired by statistical and probabilistic approaches, which can effectively model these correlations. We analytically and experimentally demonstrate the benefits of the new measures using the 1NN classification task, and discuss the lessons learned. Finally, we propose future research directions for enabling the proposed measures to be used in practice.

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Cited By

Echihabi KTsandilas TGogolou ABezerianos APalpanas T(2022)ProS: data series progressive k-NN similarity search and classification with probabilistic quality guaranteesThe VLDB Journal10.1007/s00778-022-00771-z32:4(763-789)Online publication date: 30-Nov-2022
https://doi.org/10.1007/s00778-022-00771-z
Makris KVonta I(2021)Presentation of Coupling Analysis Techniques of Maximum and Minimum Values Between N Sets of Data Using Matrix [µ][MKN]International Journal of Mathematical, Engineering and Management Sciences10.33889/IJMEMS.2021.6.4.0676:4(1127-1136)Online publication date: 18-Jul-2021
https://doi.org/10.33889/IJMEMS.2021.6.4.067
Paparrizos JLiu CElmore AFranklin MMaier DPottinger RDoan ATan WAlawini ANgo H(2020)Debunking Four Long-Standing Misconceptions of Time-Series Distance MeasuresProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3389760(1887-1905)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3389760
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Data Series Similarity Using Correlation-Aware Measures

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cover image ACM Other conferences

SSDBM '17: Proceedings of the 29th International Conference on Scientific and Statistical Database Management

June 2017

373 pages

ISBN:9781450352826

DOI:10.1145/3085504

General Chair:
Alok Choudhary,
Program Chair:
Kesheng Wu,
Publications Chair:
Bin Dong

Copyright © 2017 ACM.

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Published: 27 June 2017

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SSDBM '17: 29th International Conference on Scientific and Statistical Database Management

June 27 - 29, 2017

IL, Chicago, USA

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Cited By

Echihabi KTsandilas TGogolou ABezerianos APalpanas T(2022)ProS: data series progressive k-NN similarity search and classification with probabilistic quality guaranteesThe VLDB Journal10.1007/s00778-022-00771-z32:4(763-789)Online publication date: 30-Nov-2022
https://doi.org/10.1007/s00778-022-00771-z
Makris KVonta I(2021)Presentation of Coupling Analysis Techniques of Maximum and Minimum Values Between N Sets of Data Using Matrix [µ][MKN]International Journal of Mathematical, Engineering and Management Sciences10.33889/IJMEMS.2021.6.4.0676:4(1127-1136)Online publication date: 18-Jul-2021
https://doi.org/10.33889/IJMEMS.2021.6.4.067
Paparrizos JLiu CElmore AFranklin MMaier DPottinger RDoan ATan WAlawini ANgo H(2020)Debunking Four Long-Standing Misconceptions of Time-Series Distance MeasuresProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3389760(1887-1905)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3389760
Ahsan RNeamtu RBashir MRundensteiner ESarkozy G(2020)Correlation-Based Analytics of Time Series Data2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378155(4482-4491)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378155
Palpanas T(2020)Evolution of a Data Series IndexInformation Search, Integration, and Personalization10.1007/978-3-030-44900-1_5(68-83)Online publication date: 27-Mar-2020
https://doi.org/10.1007/978-3-030-44900-1_5
Mirylenka KScotton PMiksovic CAlaoui S(2020)Linking IT Product RecordsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-43887-6_9(101-111)Online publication date: 28-Mar-2020
https://doi.org/10.1007/978-3-030-43887-6_9
Echihabi KZoumpatianos KPalpanas TBenbrahim H(2019)Return of the Lernaean HydraProceedings of the VLDB Endowment10.14778/3368289.336830313:3(403-420)Online publication date: 1-Nov-2019
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Alseghayer RPetrov DChrysanthis PSharaf MLabrinidis A(2019)DCS: A Policy Framework for the Detection of Correlated Data StreamsReal-Time Business Intelligence and Analytics10.1007/978-3-030-24124-7_12(191-210)Online publication date: 11-Oct-2019
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Echihabi KZoumpatianos KPalpanas TBenbrahim H(2018)The lernaean hydra of data series similarity searchProceedings of the VLDB Endowment10.14778/3282495.328249812:2(112-127)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.14778/3282495.3282498
Alseghayer RPetrov DChrysanthis P(2018)Strategies for Detection of Correlated Data StreamsProceedings of the 5th International Workshop on Exploratory Search in Databases and the Web10.1145/3214708.3214714(1-6)Online publication date: 15-Jun-2018
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