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User-Driven Methodology for Data Quality Assessment in the Context of Robbery Events

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New Contributions in Information Systems and Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 353))

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Abstract

Situation assessment is a significant process for Situation Awareness (SAW) improvement since this process can be degraded if obtained by means of low quality data, which undermines the decision-making process. Identification of situations in military systems is of utmost importance since the information acquired is used in order to answer emergency calls in which poor understanding of situations for further resources allocation to answer it impacts on the victims involved. In order to assist the first level of SAW formation in the context of military situation assessment of robbery, the present work aims to present a methodology to provide support to the development of military decision-making systems addressed to represent and evaluate data quality. Such methodology is composed by gathering requirements for the robbery domain, the establishment of quantitative metrics for quality evaluation such as completeness and timeliness, the development of an ontology to capture and represent the semantic knowledge and the enforcement of evaluation functions according to scores set by means of quality metrics. Finally, it will be addressed a case study in response to robbery reports to illustrate the applicability of the methodology.

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Correspondence to Jessica Souza .

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© 2015 Springer International Publishing Switzerland

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Souza, J., Botega, L., Segundo, J.E.S., Berti, C. (2015). User-Driven Methodology for Data Quality Assessment in the Context of Robbery Events. In: Rocha, A., Correia, A., Costanzo, S., Reis, L. (eds) New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-319-16486-1_16

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  • DOI: https://doi.org/10.1007/978-3-319-16486-1_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16485-4

  • Online ISBN: 978-3-319-16486-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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