Visual comparative case analytics

Criminal Intelligence Analysis (CIA) faces a challenging task in handling high-dimensional data that needs to be investigated with complex analytical processes. State-of-the-art crime analysis tools do not fully support interactive data exploration and fall short of computational transparency in terms of revealing alternative results. In this paper we report our ongoing research into providing the analysts with such a transparent and interactive system for exploring similarities between crime cases. The system 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 (VA) workflow iteratively supports the interpretation of obtained clustering results, the development of alternative models, as well as cluster verification. The visualizations offer a usable way for the analyst to provide feedback to the system and to observe the impact of their interaction

A review of GIS-based information sharing systems

GIS-based information sharing systems have been implemented in many of England and Wales' Crime and Disorder Reduction Partnerships (CDRPs). The information sharing role of these systems is seen as being vital to help in the review of crime, disorder and misuse of drugs; to sustain strategic objectives, to monitor interventions and initiatives; and support action plans for service delivery. This evaluation into these systems aimed to identify the lessons learned from existing systems, identify how these systems can be best used to support the business functions of CDRPs, identify common weaknesses across the systems, and produce guidelines on how these systems should be further developed. At present there are in excess of 20 major systems distributed across England and Wales. This evaluation considered a representative sample of ten systems. To date, little documented evidence has been collected by the systems that demonstrate the direct impact they are having in reducing crime and disorder, and the misuse of drugs. All point to how they are contributing to more effective partnership working, but all systems must be encouraged to record how they are contributing to improving community safety. Demonstrating this impact will help them to assure their future role in their CDRPs. By reviewing the systems wholly, several key ingredients were identified that were evident in contributing to the effectiveness of these systems. These included the need for an effective partnership business model within which the system operates, and the generation of good quality multi-agency intelligence products from the system. In helping to determine the future development of GIS-based information sharing systems, four key community safety partnership business service functions have been identified that these systems can most effectively support. These functions support the performance review requirements of CDRPs, operate a problem solving scanning and analysis role, and offer an interface with the public. By following these business service functions as a template will provide for a more effective application of these systems nationally

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

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

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

Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment

Towards a Theory of Analytical Behaviour: A Model of Decision-Making in Visual Analytics

This paper introduces a descriptive model of the human-computer processes that lead to decision-making in visual analytics. A survey of nine models from the visual analytics and HCI literature are presented to account for different perspectives such as sense-making, reasoning, and low-level human-computer interactions. The survey examines the people and computers (entities) presented in the models, the divisions of labour between entities (both physical and role-based), the behaviour of both people and machines as constrained by their roles and agency, and finally the elements and processes which define the flow of data both within and between entities. The survey informs the identification of four observations that characterise analytical behaviour - defined as decision-making facilitated by visual analytics: bilateral discourse, divisions of labour, mixed-synchronicity information flows, and bounded behaviour. Based on these principles, a descriptive model is presented as a contribution towards a theory of analytical behaviour. The future intention is to apply prospect theory, a economic model of decision-making under uncertainty, to the study of analytical behaviour. It is our assertion that to apply prospect theory first requires a descriptive model of the processes that facilitate decision-making in visual analytics. We conclude it necessary to measure the perception of risk in future work in order to apply prospect theory to the study of analytical behaviour using our proposed model

Analogy of intelligence with other disciplines

Intelligence analysis has many important epistemological resemblances with science (problem solving, discovery, skillful use of tools, knowledge verification) and is more interested in a posteriori than a priori knowledge, on how or the basis on which a proposition may be known. The puzzle metaphor is used in both information and archeology. Both disciplines involve collecting evidence to build as complete a picture as possible. The process of converting raw information into actionable processed intelligence is almost identical for governmental and business organizations. The medical practice of diagnosing identification, collection, analysis and dissemination is similar to that of intelligence. DOI: 10.13140/RG.2.2.26511.1296

You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25 in Vancouver, Canada. Paper will also be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS (InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation method