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WIREs Data Mining Knowl Discov
Impact Factor: 2.541

Forensic intelligence and the analytical process

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Abstract A review was undertaken of the developments made with integrating forensic evidence into the analytical process to support police investigations. Evidence such as DNA, fingerprints, fibers, accelerants, tyre marks, and so forth, can support to differing degrees the various working theories or hypotheses about the nature of the alleged crime, the persons of interest and the modus operandi. Investigators however, either forensic or detective, bring various biases to evidence capture and analysis, biases which are better understood in the intelligence community. Structured analytical techniques have a long history in intelligence analysis, for example analysis of competing hypotheses, which serves several purposes: information sharing, clarity of communication, and to highlight the common forms of bias brought to bear in an investigation. We illustrate the representation of links based on traces and intelligence, and how these can be stored in databases permitting better “reasoning” with evidence. We also present some recommendations for integration of forensic intelligence into the investigative analytic process and review information systems in this area. This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Application Areas > Society and Culture Fundamental Concepts of Data and Knowledge > Knowledge Representation
Scientific investigation method. (Reprinted with permission from Dutelle (). Copyright 2016 Elsevier) Explains an iterative approach to the examination of crime scenes, drawing parallels with the scientific method. Hypotheses are formed in relation to evidence unveiled through systematic searches. As new evidence is brought to light during a live investigation, priorities and crime scene strategies are adapted to achieve the best outcome
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Reconstruction of series and counting of links. (Reprinted with permission from Rossy et al. (). Copyright 2013)
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Knowledge containers of a case‐based reasoning (CBR) system. (Reprinted with permission from Richter (). Copyright 2003 Jones & Bartlett Learning). Each domain is different, and therefore has a different vocabulary. The reasoning process will always be the same, to find a previous case and apply its solution, however in each domain (and subdomain) there will need to be a differently crafted similarity measure and adaptation knowledge
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Case‐based reasoning cycle. First, a case base is developed, consisting of previously solved cases, or problems with known solutions. An unsolved problem is encountered, and is termed a query case. The most similar cases are retrieved and their (known) solutions are adapted for use with this query case, according to the closeness in similarity. This is now a “solved case” which is assessed, resulting in a “tested/repaired” case. This is now inserted into the case base, and should a future query be of this type, then the case base can resuse this solution
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Fusion of different evidence types in Burglary cases using a Bayesian network. (Reprinted with permission from Oatley and Ewart (). Copyright 2003 Elsevier)
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Detective‐forensic intercept
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Fundamental Concepts of Data and Knowledge > Knowledge Representation
Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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