VALCRI - Visual Analytics for Sense-making in CRiminal Intelligence analysis
Crime intelligence analysis is becoming a whole new sector of data-based, research-oriented reasoning and sense-making activities. Numerous types of data, such as text reports, audio, video, and others, are being collected and piled up for future investigation into potential, organized crimes. However, many investigative analysts struggle to extract meaningful knowledge from the ocean of data due to lack of analysis and visual supports as well as human's limited cognitive and perceptual abilitiess. The goal of the project is to create a visual analytics-based reasoning and sense-making capability for criminal intelligence and investigative analysts working in police forces across Europe. VALCRI will develop and integrate advanced user interface technologies with powerful data analytic software to extract meaningful information from very large and diverse datasets so the analyst can quickly reach effective and justifiable conclusions.
- AG Keim (Data Analysis and Visualization)
|(2018): Making machine intelligence less scary for criminal analysts : reflections on designing a visual comparative case analysis tool The Visual Computer ; 34 (2018), 9. - S. 1225-1241. - ISSN 0178-2789. - eISSN 1432-2315||
Making machine intelligence less scary for criminal analysts : reflections on designing a visual comparative case analysis tool
A fundamental task in criminal intelligence analysis is to analyze the similarity of crime cases, called comparative case analysis (CCA), to identify common crime patterns and to reason about unsolved crimes. Typically, the data are complex and high dimensional and the use of complex analytical processes would be appropriate. State-of-the-art CCA tools lack flexibility in interactive data exploration and fall short of computational transparency in terms of revealing alternative methods and results. In this paper, we report on the design of the Concept Explorer, a flexible, transparent and interactive CCA system. During this design process, we observed that most criminal analysts are not able to understand the underlying complex technical processes, which decrease the users’ trust in the results and hence a reluctance to use the tool. Our CCA solution implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed visual analytics workflow iteratively supports the interpretation of the results of clustering with the respective feature relations, the development of alternative models, as well as cluster verification. The visualizations offer an understandable and usable way for the analyst to provide feedback to the system and to observe the impact of their interactions. Expert feedback confirmed that our user-centered design decisions made this computational complexity less scary to criminal analysts.
|Period:||01.05.2014 – 31.12.2017|