Exploring lekagul sensor events using rules, aggregations, and selections

Bram C.M. Cappers

doi:10.1109/VAST.2017.8585619

Exploring lekagul sensor events using rules, aggregations, and selections

Bram C.M. Cappers

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

Abstract

In this paper we demonstrate how we can study multivariate event sequences in the VAST Mini Challenge 1 data set using our system Eventpad, a notepad editor for event data. We illustrate the effectiveness of multivariate regular expressions, pattern aggregations, and selections to define custom events of interest, discover patterns within sequences, and study differences between sequences. Finally, we discuss our analysis process and summarize some patterns and anomalies we discovered in the data set.

Original language	English
Title of host publication	2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings
Editors	Tobias Schreck, Brian Fisher, Shixia Liu
Publisher	Institute of Electrical and Electronics Engineers
Pages	193-194
Number of pages	2
ISBN (Electronic)	9781538631638
DOIs	https://doi.org/10.1109/VAST.2017.8585619
Publication status	Published - 21 Dec 2018
Event	2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Phoenix, United States Duration: 1 Oct 2017 → 6 Oct 2017

Conference

Conference	2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017
Country/Territory	United States
City	Phoenix
Period	1/10/17 → 6/10/17

Funding

This work is funded by SpySpot, a project in the Cyber Security program of Netherlands Organisation for Scientific Research (NWO).

Keywords

Event visualization
Interaction
Multivariate events
Regular expressions
Sequence alignment

Access to Document

10.1109/VAST.2017.8585619

Cite this

@inproceedings{c945626da626443cbbb5bddb9d42faf8,

title = "Exploring lekagul sensor events using rules, aggregations, and selections",

abstract = "In this paper we demonstrate how we can study multivariate event sequences in the VAST Mini Challenge 1 data set using our system Eventpad, a notepad editor for event data. We illustrate the effectiveness of multivariate regular expressions, pattern aggregations, and selections to define custom events of interest, discover patterns within sequences, and study differences between sequences. Finally, we discuss our analysis process and summarize some patterns and anomalies we discovered in the data set.",

keywords = "Event visualization, Interaction, Multivariate events, Regular expressions, Sequence alignment",

author = "Cappers, {Bram C.M.}",

year = "2018",

month = dec,

day = "21",

doi = "10.1109/VAST.2017.8585619",

language = "English",

pages = "193--194",

editor = "Tobias Schreck and Brian Fisher and Shixia Liu",

booktitle = "2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 ; Conference date: 01-10-2017 Through 06-10-2017",

}

Cappers, BCM 2018, Exploring lekagul sensor events using rules, aggregations, and selections. in T Schreck, B Fisher & S Liu (eds), 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings., 8585619, Institute of Electrical and Electronics Engineers, pp. 193-194, 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017, Phoenix, United States, 1/10/17. https://doi.org/10.1109/VAST.2017.8585619

Exploring lekagul sensor events using rules, aggregations, and selections. / Cappers, Bram C.M.
2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings. ed. / Tobias Schreck; Brian Fisher; Shixia Liu. Institute of Electrical and Electronics Engineers, 2018. p. 193-194 8585619.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Cappers, Bram C.M.

PY - 2018/12/21

Y1 - 2018/12/21

N2 - In this paper we demonstrate how we can study multivariate event sequences in the VAST Mini Challenge 1 data set using our system Eventpad, a notepad editor for event data. We illustrate the effectiveness of multivariate regular expressions, pattern aggregations, and selections to define custom events of interest, discover patterns within sequences, and study differences between sequences. Finally, we discuss our analysis process and summarize some patterns and anomalies we discovered in the data set.

AB - In this paper we demonstrate how we can study multivariate event sequences in the VAST Mini Challenge 1 data set using our system Eventpad, a notepad editor for event data. We illustrate the effectiveness of multivariate regular expressions, pattern aggregations, and selections to define custom events of interest, discover patterns within sequences, and study differences between sequences. Finally, we discuss our analysis process and summarize some patterns and anomalies we discovered in the data set.

KW - Event visualization

KW - Interaction

KW - Multivariate events

KW - Regular expressions

KW - Sequence alignment

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UR - https://vimeo.com/242487107

U2 - 10.1109/VAST.2017.8585619

DO - 10.1109/VAST.2017.8585619

M3 - Conference contribution

AN - SCOPUS:85061059811

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EP - 194

BT - 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings

A2 - Schreck, Tobias

A2 - Fisher, Brian

A2 - Liu, Shixia

PB - Institute of Electrical and Electronics Engineers

T2 - 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017

Y2 - 1 October 2017 through 6 October 2017

ER -

Exploring lekagul sensor events using rules, aggregations, and selections

Abstract

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Keywords

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IEEE Visual Analytics Science & Technology Challenge

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Exploring lekagul sensor events using rules, aggregations, and selections

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Prizes

IEEE Visual Analytics Science & Technology Challenge

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