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Information Quality in Information Fusion and Decision Making pp 563–586Cite as

Quantify: An Information Fusion Model Based on Syntactic and Semantic Analysis and Quality Assessments to Enhance Situation Awareness

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Part of the book series: Information Fusion and Data Science ((IFDS))

Abstract

Situation awareness is a concept especially important in the area of criminal data analysis and refers to the level of consciousness that an individual or team has about a situation, in this case a criminal event. Being unaware of crime situations can cause decision-makers to fail, affecting resource allocation for crime mitigation and jeopardizing human safety and their patrimony. Data and information fusion present opportunities to enrich the knowledge about crime situations by integrating heterogeneous and synergistic data from different sources. However, the problem is complicated by poor quality of information, especially when humans are the main sources of data. Motivated by the challenges in analyzing complex crime data and by the limitations of the state of the art on critical situation assessment approaches, this chapter presents Quantify, a new information fusion model. Its main contribution is the use of the information quality management throughout syntactic and semantic fusion routines to parameterize and to guide the work of humans and systems. To validate the new features of the model, a case study with real crime data was conducted. Crime reports were submitted to the modules of the model and had situations depicted and represented by an Emergency Situation Assessment System. Results highlighted the limitations of using only lexical and syntactical variations to support data and information fusion and the demand and benefits provided by quality and semantic means to assess crime situations.

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Author information

Authors and Affiliations

  1. Graduate School in Information Science, São Paulo State University (UNESP), Marília, Brazil

    Leonardo Castro Botega, Valdir Amancio Pereira Junior & Gustavo Marttos Cáceres Pereira

  2. Computer Science and Information Systems, University Centre Eurípides of Marília (UNIVEM), Marília, Brazil

    Allan Cesar Moreira de Oliveira & Jordan Ferreira Saran

  3. Institute of Computing, State University of Campinas (UNICAMP), Campinas, Brazil

    Lucas Zanco Ladeira

  4. Institute of Mathematics and Computer Science, University of São Paulo (USP), São Carlos, Brazil

    Seiji Isotani

Authors
  1. Leonardo Castro Botega
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  2. Allan Cesar Moreira de Oliveira
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  3. Valdir Amancio Pereira Junior
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  4. Jordan Ferreira Saran
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  5. Lucas Zanco Ladeira
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  6. Gustavo Marttos Cáceres Pereira
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Corresponding author

Correspondence to Leonardo Castro Botega .

Editor information

Editors and Affiliations

  1. IMT-Atlantique, Brest, France

    Éloi Bossé

  2. The State University of New York at Buffalo, Buffalo, NY, USA

    Galina L. Rogova

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Botega, L.C. et al. (2019). Quantify: An Information Fusion Model Based on Syntactic and Semantic Analysis and Quality Assessments to Enhance Situation Awareness. In: Bossé, É., Rogova, G. (eds) Information Quality in Information Fusion and Decision Making. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-030-03643-0_23

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  • DOI: https://doi.org/10.1007/978-3-030-03643-0_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03642-3

  • Online ISBN: 978-3-030-03643-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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