1,421 research outputs found

    Towards analytical provenance visualization for criminal intelligence analysis

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    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 analysi

    Analytic provenance as constructs of behavioural markers for externalizing thinking processes in criminal intelligence analysis

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    Studying how analysts use interaction in visualization systems is an important part of evaluating how well these interactions support analysis needs, like generating insights or performing tasks. Analytic Provenance commonly known as interaction histories contains information about the sequence of choices that analysts make when exploring data or performing a task. This research work presents a compositional reductionist approach as a way of externalizing analyst’s thinking processes by using markers of analytical behaviour extracted from such interaction histories. Set of Behavioural Markers (BMs) have been identified through a workshop with domain experts and a systematic literature review to use them as cognitive attributes of imagination, insight, transparency, fluidity and rigour to enhance performance in criminal intelligence analysis. A low level semantic action sequence computation also has been proposed as a detection approach of identified BMs and found from computation that BMs can act as bridge between human cognition and computation through semantic interaction. This research work has addressed problems of existing qualitative experiments to extract these BMs through cognitive task analysis and found that the proposed computational technique can be a supplementary approach for validating experimental results

    Analytic provenance as constructs of behavioural markers for externalizing thinking processes in criminal intelligence analysis

    Get PDF
    Studying how analysts use interaction in visualization systems is an important part of evaluating how well these interactions support analysis needs, like generating insights or performing tasks. Analytic Provenance commonly known as interaction histories contains information about the sequence of choices that analysts make when exploring data or performing a task. This research work presents a compositional reductionist approach as a way of externalizing analyst’s thinking processes by using markers of analytical behaviour extracted from such interaction histories. Set of Behavioural Markers (BMs) have been identified through a workshop with domain experts and a systematic literature review to use them as cognitive attributes of imagination, insight, transparency, fluidity and rigour to enhance performance in criminal intelligence analysis. A low level semantic action sequence computation also has been proposed as a detection approach of identified BMs and found from computation that BMs can act as bridge between human cognition and computation through semantic interaction. This research work has addressed problems of existing qualitative experiments to extract these BMs through cognitive task analysis and found that the proposed computational technique can be a supplementary approach for validating experimental results

    Behavioural markers: bridging the gap between art of analysis and science of analytics in criminal intelligence

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    Studying how intelligence analysts use interaction in visualization systems is an important part of evaluating how well these interactions support analysis needs, like generating insights or performing tasks. Intelligence analysis is inherently a fluid activity involving transitions between mental and interaction states through analytic processes. A gap exists to complement these transitions at micro-analytic level during data exploration or task performance. We propose Behavioural markers (BMs) which are representatives of the action choices that analysts make during their analytical processes as the bridge between human cognition and computation through semantic interaction. A low level semantic action sequence computation technique has been proposed to extract these BMs from captured process log. Our proposed computational technique can supplement the problems of existing qualitative approaches to extract such BMs

    CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling

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    Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta-information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user-adaptable and problem-specific machine learning-based retrieval system. An interactive multi-level matrix-based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross-checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real-world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.Comment: 12 pages, 7 figures, Computer Graphics Forum 2021 (pre-peer reviewed version

    Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

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    A fundamental task in Criminal Intelligence Analysis is to analyze the similarity of crime cases, called CCA, to identify common crime patterns and to reason about unsolved crimes. Typically, the data is 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-centred design decisions made this computational complexity less scary to criminal analysts
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