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short-paper

Visual support for rastering of unequally spaced time series

Authors:

Christian Bors,

Theresia Gschwandtner,

Silvia MikschAuthors Info & Claims

VINCI '17: Proceedings of the 10th International Symposium on Visual Information Communication and Interaction

Pages 53 - 57

https://doi.org/10.1145/3105971.3105984

Published: 14 August 2017 Publication History

Abstract

Preprocessing is a mandatory first step to make data usable for analysis. While in time series analysis many established methods require data that are sampled in regular time intervals, in practice sensors may sample data at varying interval lengths. Time series rastering is the process of aggregating unequally spaced time series into equal interval lengths. In this paper we discuss critical aspects in the context of time series rastering, and we present a visual design which supports the parametrization of the rastering transformation, communicates the introduced uncertainties and quality issues, and facilitates the comparison of alternative rastering outcomes to achieve optimal results.

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Cited By

Zhu HHan DPan JWei YFeng YWeng LMao KXing YLv JWan QChen W(2023)A visual analysis approach for data imputation via multi-party tabular data correlation strategiesFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.230048025:3(398-414)Online publication date: 29-Dec-2023
https://doi.org/10.1631/FITEE.2300480
Piccolotto NBögl MMiksch S(2023)Visual Parameter Space Exploration in Time and SpaceComputer Graphics Forum10.1111/cgf.1478542:6Online publication date: 3-Apr-2023
https://doi.org/10.1111/cgf.14785
Song HFu YSaket BStasko J(2021)Understanding the Effects of Visualizing Missing Values on Visual Data Exploration2021 IEEE Visualization Conference (VIS)10.1109/VIS49827.2021.9623328(161-165)Online publication date: Oct-2021
https://doi.org/10.1109/VIS49827.2021.9623328
Show More Cited By

Index Terms

Visual support for rastering of unequally spaced time series
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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Information

Published In

cover image ACM Other conferences

VINCI '17: Proceedings of the 10th International Symposium on Visual Information Communication and Interaction

August 2017

158 pages

ISBN:9781450352925

DOI:10.1145/3105971

General Chair:
Robert P. Biuk-Aghai
University of Macau, Macau
,
Program Chairs:
Jie Li
Tianjin University, China
,
Shigeo Takahashi
University of Aizu, Japan

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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KMUTT: King Mongkut's University of Technology Thonburi

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 August 2017

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VINCI '17

Sponsor:

KMUTT

VINCI '17: 10th International Symposium on Visual Information Communication and Interaction

August 14 - 16, 2017

Bangkok, Thailand

Acceptance Rates

VINCI '17 Paper Acceptance Rate 12 of 27 submissions, 44%;

Overall Acceptance Rate 71 of 193 submissions, 37%

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Cited By

Zhu HHan DPan JWei YFeng YWeng LMao KXing YLv JWan QChen W(2023)A visual analysis approach for data imputation via multi-party tabular data correlation strategiesFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.230048025:3(398-414)Online publication date: 29-Dec-2023
https://doi.org/10.1631/FITEE.2300480
Piccolotto NBögl MMiksch S(2023)Visual Parameter Space Exploration in Time and SpaceComputer Graphics Forum10.1111/cgf.1478542:6Online publication date: 3-Apr-2023
https://doi.org/10.1111/cgf.14785
Song HFu YSaket BStasko J(2021)Understanding the Effects of Visualizing Missing Values on Visual Data Exploration2021 IEEE Visualization Conference (VIS)10.1109/VIS49827.2021.9623328(161-165)Online publication date: Oct-2021
https://doi.org/10.1109/VIS49827.2021.9623328
Bernard JHutter MReinemuth HPfeifer HBors CKohlhammer J(2019)Visual‐Interactive Preprocessing of Multivariate Time Series DataComputer Graphics Forum10.1111/cgf.1369838:3(401-412)Online publication date: 10-Jul-2019
https://doi.org/10.1111/cgf.13698
Song HSzafir D(2019)Where's My Data? Evaluating Visualizations with Missing DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286491425:1(914-924)Online publication date: Jan-2019
https://doi.org/10.1109/TVCG.2018.2864914
Bors CGschwandtner TMiksch S(2019)Capturing and Visualizing Provenance From Data WranglingIEEE Computer Graphics and Applications10.1109/MCG.2019.294185639:6(61-75)Online publication date: 1-Nov-2019
https://doi.org/10.1109/MCG.2019.2941856

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