Probabilistic model-based assessment of information quality in uncertain domains

Steffen Michels; Marina Velikova; Peter J.F. Lucas

doi:10.1007/978-3-642-35101-3_75

Probabilistic model-based assessment of information quality in uncertain domains

Steffen Michels^*, Marina Velikova, Peter J.F. Lucas

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the information they are based on. In this paper, we propose a novel method for assessing the quality of information taking into account uncertainty. Two properties - soundness and completeness - of the information are used to define the notion of information quality and their expected values are defined using a probabilistic model output. Simulation experiments with data from a maritime scenario demonstrates the usage of the proposed method and its potential for decision support in complex tasks such as surveillance.

Original language	English
Title of host publication	AI 2012
Subtitle of host publication	Advances in Artificial Intelligence - 25th Australasian Joint Conference, Proceedings
Pages	890-901
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-35101-3_75
Publication status	Published - 2012
Externally published	Yes
Event	25th Australasian Joint Conference on Artificial Intelligence, AI 2012 - Sydney, Australia Duration: 4 Dec 2012 → 7 Dec 2012 Conference number: 25

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7691 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th Australasian Joint Conference on Artificial Intelligence, AI 2012
Abbreviated title	AI 2012
Country/Territory	Australia
City	Sydney
Period	4/12/12 → 7/12/12

Keywords

n/a OA procedure

Access to Document

10.1007/978-3-642-35101-3_75

Cite this

Michels, S., Velikova, M., & Lucas, P. J. F. (2012). Probabilistic model-based assessment of information quality in uncertain domains. In AI 2012: Advances in Artificial Intelligence - 25th Australasian Joint Conference, Proceedings (pp. 890-901). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7691 LNAI). https://doi.org/10.1007/978-3-642-35101-3_75

Michels, Steffen ; Velikova, Marina ; Lucas, Peter J.F. / Probabilistic model-based assessment of information quality in uncertain domains. AI 2012: Advances in Artificial Intelligence - 25th Australasian Joint Conference, Proceedings. 2012. pp. 890-901 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{54b0fe0ce33e4bd0b41a3d876ac2daef,

title = "Probabilistic model-based assessment of information quality in uncertain domains",

abstract = "In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the information they are based on. In this paper, we propose a novel method for assessing the quality of information taking into account uncertainty. Two properties - soundness and completeness - of the information are used to define the notion of information quality and their expected values are defined using a probabilistic model output. Simulation experiments with data from a maritime scenario demonstrates the usage of the proposed method and its potential for decision support in complex tasks such as surveillance.",

keywords = "n/a OA procedure",

author = "Steffen Michels and Marina Velikova and Lucas, \{Peter J.F.\}",

year = "2012",

doi = "10.1007/978-3-642-35101-3\_75",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "890--901",

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note = "25th Australasian Joint Conference on Artificial Intelligence, AI 2012, AI 2012 ; Conference date: 04-12-2012 Through 07-12-2012",

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Michels, S, Velikova, M & Lucas, PJF 2012, Probabilistic model-based assessment of information quality in uncertain domains. in AI 2012: Advances in Artificial Intelligence - 25th Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7691 LNAI, pp. 890-901, 25th Australasian Joint Conference on Artificial Intelligence, AI 2012, Sydney, New South Wales, Australia, 4/12/12. https://doi.org/10.1007/978-3-642-35101-3_75

Probabilistic model-based assessment of information quality in uncertain domains. / Michels, Steffen; Velikova, Marina; Lucas, Peter J.F.
AI 2012: Advances in Artificial Intelligence - 25th Australasian Joint Conference, Proceedings. 2012. p. 890-901 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7691 LNAI).

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

TY - GEN

T1 - Probabilistic model-based assessment of information quality in uncertain domains

AU - Michels, Steffen

AU - Velikova, Marina

AU - Lucas, Peter J.F.

N1 - Conference code: 25

PY - 2012

Y1 - 2012

N2 - In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the information they are based on. In this paper, we propose a novel method for assessing the quality of information taking into account uncertainty. Two properties - soundness and completeness - of the information are used to define the notion of information quality and their expected values are defined using a probabilistic model output. Simulation experiments with data from a maritime scenario demonstrates the usage of the proposed method and its potential for decision support in complex tasks such as surveillance.

AB - In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the information they are based on. In this paper, we propose a novel method for assessing the quality of information taking into account uncertainty. Two properties - soundness and completeness - of the information are used to define the notion of information quality and their expected values are defined using a probabilistic model output. Simulation experiments with data from a maritime scenario demonstrates the usage of the proposed method and its potential for decision support in complex tasks such as surveillance.

KW - n/a OA procedure

UR - https://www.scopus.com/pages/publications/84871392944

U2 - 10.1007/978-3-642-35101-3_75

DO - 10.1007/978-3-642-35101-3_75

M3 - Conference contribution

AN - SCOPUS:84871392944

SN - 9783642351006

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - AI 2012

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Michels S, Velikova M, Lucas PJF. Probabilistic model-based assessment of information quality in uncertain domains. In AI 2012: Advances in Artificial Intelligence - 25th Australasian Joint Conference, Proceedings. 2012. p. 890-901. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-35101-3_75

Probabilistic model-based assessment of information quality in uncertain domains

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