Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries

Vung Pham, Tommy Dang

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

2 Scopus citations

Abstract

This paper presents an approach to analyzing two-dimensional temporal datasets focusing on identifying observations that are significant in calculating the outliers of a scatterplot. We also propose a prototype, called Outliagnostics, to guide users when interactively exploring abnormalities in large time series. Instead of focusing on detecting outliers at each time point, we monitor and display the discrepant temporal signatures of each data entry concerning the overall distributions. Our prototype is designed to handle these tasks in parallel to improve performance. To highlight the benefits and performance of our approach, we illustrate and validate the use of Outliagnostics on real-world datasets of various sizes in different parallelism configurations. This work also discusses how to extend these ideas to handle time series with a higher number of dimensions and provides a prototype for this type of datasets.

Original languageEnglish
Title of host publication2019 IEEE Visualization in Data Science, VDS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-37
Number of pages9
ISBN (Electronic)9781728150475
DOIs
StatePublished - Oct 2019
Event2019 IEEE Visualization in Data Science, VDS 2019 - Vancouver, Canada
Duration: Oct 20 2019 → …

Publication series

Name2019 IEEE Visualization in Data Science, VDS 2019
Volume2019-January

Conference

Conference2019 IEEE Visualization in Data Science, VDS 2019
Country/TerritoryCanada
CityVancouver
Period10/20/19 → …

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