Provenance – Valcri

White Paper WP-2017-005: The Operationalisation of Transparency in VALCRI

Ms Haider — Fri, 13 Jan 2017 09:43:31 +0000

This White Paper presents some research and findings from the EU-funded R&D project VALCRI with regard to the requirement of transparency from legal, ethical, and data protection perspective. Thereby, it addresses difficulties of transparency operationalization and presents possible solution approaches, which are linked to recent R&D in the realm of data, process and reasoning provenance.

Keywords

Crime Analytics, Transparency, Legal, Data Protection Law, Ethics, Provenance.

VALCRI WHITE PAPER SERIES

VALCRI-WP-2017-005 Transparency

Towards Analytical Provenance Visualization for Criminal Intelligence Analysis

Ms Haider — Tue, 06 Sep 2016 15:13:00 +0000

Islam, J., Anslow, C., Xu, K., Wong, W., and Zhang, L. (2016). Towards Analytical Provenance Visualization for Criminal Intelligence Analysis. In Turkay, C. and Wan, T. R., editors, Computer Graphics and Visual Computing (CGVC). The Eurographics Association.

Abstract:

In criminal intelligence analysis to complement the information entailed and to enhance transparency of the operations, it demands logs of the individual processing activities within an automated processing system. Management and tracing of such security sensitive analytical information flow originated from tightly coupled visualizations into visual analytic system for criminal intelligence that triggers huge amount of analytical information on a single click, involves design and development challenges. To lead to a believable story by using scientific methods, reasoning for getting explicit knowledge of series of events, sequences and time surrounding interrelationships with available relevant information by using human perception, cognition, reasoning with database operations and computational methods, an analytic visual judgmental support is obvious for criminal intelligence. Our research outlines the requirements and development challenges of such system as well as proposes a generic way of capturing different complex visual analytical states and processes known as analytic provenance. The proposed technique has been tested into a large heterogeneous event-driven visual analytic modular analyst’’s user interface (AUI) of the project VALCRI (Visual Analytics for Sensemaking in Criminal Intelligence) and evaluated by the police intelligence analysts through it’s visual state capturing and retracing interfaces. We have conducted several prototype evaluation sessions with the groups of end-users (police intelligence analysts) and found very positive feedback. Our approach provides a generic support for visual judgmental process into a large complex event-driven AUI system for criminal intelligence analysis.

https://diglib.eg.org/handle/10.2312/cgvc20161290

VAPD – A Visionary System for Uncertainty Aware Decision Making in Crime Analysis

valcriTeam — Fri, 30 Oct 2015 16:55:00 +0000

F. Stoffel, D. Sacha, G. Ellis, and D. A. Keim, “VAPD – A Visionary System for Uncertainty Aware Decision Making in Crime Analysis,” in Symposium on Visualization for Decision Making Under Uncertainty at IEEE VIS 2015, 2015.

Abstract:

In this paper we describe a visionary system, VAPD, which supports crime analysts in uncertainty aware decision making in use of comparative case analysis. In this scenario, it is crucial for crime analysts to get an accurate estimate of uncertainties included in their data as well as those caused through data transformations and mappings, thus supporting analysts in calibrating their trust in the pieces of evidence gained through data analytics. VAPD consists of one data processing and three visualisation components that adopt a set of guidelines for handling uncertainties. The system focuses on conveying an accurate estimate of these uncertainties on processes and uncertainties that occur within its natural language processing components. Text data analysis is ambiguous and error prone, but is nevertheless an important part of the data analysis. Through its innovative handling of uncertainties, VAPD enables

transparent and reliable decisions based on uncertainty-aware visual analytics.

keywords —Uncertainty, Provenance, Trust-Building, Crime Analysis.