White paper – Valcri

White Paper WP-2017-011: Applying Visual Interactive Dimensionality Reduction to Criminal Intelligence Analysis

Ms Haider — Fri, 13 Jan 2017 11:04:45 +0000

VALCRI provides a challenging and overwhelming high-dimensional dataset that comprises of hundreds of extracted semantic features in addition to the usual spatiotemporal information or metadata. To overcome the curse of dimensionality and to generate low-dimensional representations of these semantic features we apply interactive high-dimensional data analysis techniques with the goal of obtaining clusters of similar crime reports. However, it is still a challenge for crime analysts to make sense of the results and to provide useful interactive feedback to the system. Therefore, we provide several tightly integrated interactive visualizations that allow the analysts to identify clusters of similar crimes from different perspectives and interactively focus their analysis on features or crime records of particular interest.

Keywords

Criminal Intelligence, High-Dimensional Data Analysis, Feature Extraction, Dimensionality Reduction, Visual Analytics

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VALCRI-WP-2017-011 Interactive Visual Dimension Reduction

White Paper WP-2017-010: Roadmap for the Operationalization of Legal and Privacy Requirements in VALCRI Analysis

Ms Haider — Fri, 13 Jan 2017 11:03:35 +0000

This White Paper seeks to illustrate how the LEP guidelines and several legal requirements are set to be further operationalized in the VALCRI project. Based on VALCRI’s data management policy for the live data tests of February 2017, it formulates technical solutions and concrete measures to meet legal data protection requirements adapted to the working environment of criminal analysts.

Keywords

Visual Analytics, Criminal Intelligence Analysis LEP Guidelines, Crime, Analytics, Data Management, Data Protection Principles, Operationalization.

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VALCRI-WP-2017-010 Operationalisation Legal

White Paper WP-2017-009: Analytical Provenance for Criminal Intelligence Analysis

Ms Haider — Fri, 13 Jan 2017 09:55:44 +0000

In criminal intelligence analysis to complement the information entailed and to enhance the 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.

Keywords

Analytic Provenance, Visual Analytics, Transparency, Visualization Design, Sensemaking.

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VALCRI-WP-2017-009 Provenance

White Paper WP-2017-008: Improving Professional Training in Criminal Intelligence Analysis

Ms Haider — Fri, 13 Jan 2017 09:49:13 +0000

The training and development of criminal intelligence professionals has long suffered from a range of challenges including the absence of rigorous training standards and a failure to embrace new disciplines or branches of knowledge. This has undermined analysts’ abilities to keep pace with the evolution of crime. The VALCRI syllabus was developed to address this problem by providing instruction in a holistic set of organisational, operational, informational, technological and cognitive skills. We outline the evolution of this syllabus and what we hope to achieve through its delivery.

Keywords

Criminal intelligence, law enforcement intelligence, intelligence training, professional development, analytic training, intelligence analysis, VALCRI Project

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VALCRI-WP-2017-008 Training

White Paper WP-2017-006: Psychological Factors in Criminal Intelligence Analysis: What they are, why they are important, and how to deal with them

Ms Haider — Fri, 13 Jan 2017 09:45:58 +0000

This paper presents a framework that deals with psychological aspects that are relevant in the daily work of criminal analysts. These aspects include structuring and reasoning of criminal information, understanding and sense-making of information, and the mitigation of cognitive biases in the analytics process. Each of these human factors is described in terms of a problem statement, how the problem is addressed, and which results have been achieved so far. The Human Issues Framework bundles them and gives advice on how they can be taken up by system designers and developers. In this way the Human Issues Framework builds a sound basis for the design and specification of visual analytic systems for criminal analysis from a psychological perspective.

Keywords

Criminal analysis, reasoning, evidential structuring, sense-making, cognitive bias, human issues framework.

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VALCRI-WP-2017-006 Psychology Factors

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.

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VALCRI-WP-2017-005 Transparency

White Paper WP-2017-004: Security and Privacy Technologies in VALCRI

Ms Haider — Fri, 13 Jan 2017 09:41:55 +0000

This paper outlines the goals and major high-level challenges regarding security and privacy within VALCRI, and provides a discussion of the state-of-the-art and VACLRI approaches to overcome respective limitations in the domains of access control, privacy protection, secure logging and model/policydriven security and privacy.

Keywords

Security, privacy, information system, access control, Model Driven Security, Model Driven Privacy, PET, re-identification analysis, anonamization, pseudonymization, logging, LEA

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VALCRI-WP-2017-004 Security PET

White Paper WP-2017-003: Applying Data Science to Criminal Intelligence Analysis

Ms Haider — Sun, 08 Jan 2017 09:49:17 +0000

A major challenge of criminal intelligence analysis is to process large amount of semi-structured or unstructured data such as textual documents and videos and to extract useful information out of the data to support semantic search, sense-making and decision making. In VALCI, a computational framework is developed that incorporates concept extraction, ontology use and evolution, associative search, and image/video analysis for semantic search and knowledge discovery. In this white paper we introduce the key concepts have been applied and their corresponding technologies that have been developed to tackle the challenge.

Keywords

Criminal Intelligence, Semantic Search, Evolving Knowledge Base, Ontology, Associative Search, Concept
Extraction, Video Analysis

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VALCRI-WP-2017-003 Data Science

White Paper WP-2017-002: Analyst User Interface: Thinking Landscape as Design Concept

Ms Haider — Sun, 08 Jan 2017 09:41:56 +0000

In this White Paper we introduce the concept of the Thinking Landscape as the basis for the design of the user interface for the prototype criminal intelligence analysis system being developed in project VALCRI. The Thinking Landscape is a UI design concept that embodies the idea of externalizing the thinking and reasoning processes of the analyst in ways that gives abstract concepts a tangible expression within the computer user interface.

Authors:

B.L. William Wong, Chris Rooney, and Neesha Kodagoda
Middlesex University London

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VALCRI-WP-2017-002 AUI Thinking Landscape

White Paper WP-2017-001: Architecture, Development and Testing Environment for a Visual Analytics-based Criminal Intelligence Analysis System

Ms Haider — Sun, 08 Jan 2017 08:58:28 +0000

The VALCRI architecture is built from different Docker containers that speak with each other using mostly REST interfaces. The architecture is designed to incorporating Security, Ethics, Privacy and Legal (SEPL) solutions. The data stores – the Unstructured Database (UDB) and the Structured database (SDB) – used are controlled by SEPL Enforcement components and a Template Engine manages the previously checked and accepted query templates that can be sent to the data stores. The Advanced User Interface (AUI) server is also designed with SEPL in mind: a Jetty (Java HTTP server and Java Servlet container) instance is created per user by a Jetty Lifecycle Management component. Each such instance lives inside a Docker container to promote isolation. The Jetty instance hosts the mid-tier services, serves the front-end JavaScript code and manages the communication between the mid-tier services and the front-end. The midtier services are written in java and front-end components are implemented using GWT – Google Web Toolkit. The Model View Presenter (MVP) design pattern is implemented. The SEPL Enforcement components, as well as the analysis components, and the Jetty Containers communicate to the CAS (Central Authentication Service) component to ensure that only authorized users can perform certain actions and allows data to be properly restricted. The user management of the CAS component is linked with the LDAP (Lightweight Directory Access Protocol) component to manage and authenticate user credentials. All the components may use the interface exposed by the GrayLog component, ensuring that any action done by any component can be logged in the logging storage. Systematic system integration is a key principle for the success of VALCRI and for that a development pipeline was designed. This pipeline aims to provide continuous system integration while promoting collaboration, contributions, quick feedback to contributions, changing and evolving interfaces, and above all respecting the principle of “keep it working” – allowing to introduce many contributions in small steps while the system continues to compile and work. The GIT Source Control Management (SCM) platform is used for keeping track of changes in the source code. It is accompanied by a GitLab installation that provides a user interface that allows managing the user accounts and the individual code repositories. In order to compile and build the code available from the SCM, a custom build system was developed using Gradle. The Gradle setup is also accompanied with a Nexus component – an artefact repository – which hosts all the compiled, binary VALCRI system components. For each change in the SCM, the source code is automatically rebuilt and all tests are run. In order to do this, a Jenkins installation was put into place. Because the VALCRI architecture includes many services working on different environments, Docker was selected to allow building, shipping and running the complete environments.

Keywords

Visual Analytics, Sense-Making, Criminal Intelligence Analysis, Architecture, REST, Security, Ethics, Privacy and Legal (SEPL), Model View Presenter, Isolation, Google Web Toolkit (GWT), Errai, Docker containers, Central Authentication Service (CAS), LDAP, GrayLog, GIT, Gradle, Nexus, Jenkins

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VALCRI-WP-2017-001 Architecture