Rasmus Rosenqvist Petersen

CrimeFighter Investigator: A Novel Tool for Criminal Network Investigation

Criminal network investigation involves a number of complex tasks and faces many problems. Overall tasks include collection, processing, and analysis of information, in which analysis is the key to successful use of information, it transforms raw data into intelligence. Problems such as information abundance or scarcity and information complexity are typically resolved by adding more manpower resources, inhibiting information sharing. This paper presents a novel tool that supports a human-centered, target-centric model for criminal network investigation. The developed tool provides more comprehensive support for analysis tasks than existing tools.

Node Removal in Criminal Networks

A criminal network is a special kind of social network with emphasis on both secrecy and efficiency. Node removal is a well known technique for destabilization of criminal networks. Deciding which node or group of nodes to remove is dependent on available information and the topology of the criminal network (hierarchical, cellular, etc.), complicating the prediction of network changes following a node removal. The Crime Fighter Investigator tool supports a node removal approach with two perspectives: an inference-based prediction of new probable links and changes in standard social network degree centrality. We test the node removal algorithm on a criminal network aggregated from open source reports, creating hypotheses based on path distance and degree centrality changes.

Hypertext structures for investigative teams

Investigations such as police investigations, intelligence analysis, and investigative journalism involves a number of complex knowledge management tasks. Investigative teams collect, process, and analyze information related to a specific target to create products that can be disseminated to their customers. This paper presents a novel hypertext-based tool that supports a human-centered, target-centric model for investigative teams. The model divides investigative tasks into five overall processes: acquisition, synthesis, sense-making, dissemination, and cooperation. The developed tool provides more comprehensive support for synthesis and sense-making tasks than existing tools.

Asap: a planning tool for agile software development

This paper describes the ASAP planning tool. ASAP uses different hypertext structuring mechanisms to provide support for project planning. The design concepts and prototype features are inspired from previous work on structural computing and spatial hypertext. A use scenario demonstrates the capabilities of the tool to support the Blitz Planning activity from the Crystal Clear agile software development methodology. Future work is aimed at broadening the applicability of ASAP towards general project planning.

Knowledge Management Tools for Terrorist Network Analysis

A terrorist network is a special kind of social network with emphasis on both secrecy and efficiency. Such networks (consisting of nodes and links) needs to be analyzed and visualized in order to gain a deeper knowledge and understanding that enable network destabilization. This paper presents two novel knowledge management tools for terrorist network analysis. CrimeFighter Investigator provides advanced support for human-centered, target-centric investigations aimed at constructing terrorist networks based on disparate pieces of terrorist information. CrimeFighter Assistant provides advanced support for network, node, and link analysis once a terrorist network has been constructed. The paper focuses primarily on the latter tool.

CrimeFighter Investigator: Criminal Network Sense-Making

Criminal network investigation involves a number of complex knowledge management tasks such as collection, processing, and analysis of information. Synthesis and sense-making are core analysis tasks; analysts move pieces of information around, they stop to look for patterns that can help them relate the information pieces, they add new pieces of information and iteration after iteration the information becomes increasingly structured and valuable. Synthesizing emerging and evolving information structures is a creative and cognitive process best performed by humans. Making sense of synthesized information structures (i.e., searching for patterns) is a more logic-based process where computers outperform humans as information volume and complexity increases. CrimeFighter Investigator is a novel tool that supports sense-making tasks through the application of advanced software technologies such as hypertext structure domains, semantic web concepts, known human-computer interaction metaphors, and a tailorable computational model rooted in a conceptual model defining first class entities that enable separation of structural and mathematical models.

Association and Centrality in Criminal Networks

Network-based techniques are widely used in criminal investigations because patterns of association are actionable and understandable. Existing network models with nodes as first class entities and their related measures (e.g., social networks and centrality measures) are unable to capture and analyze the structural richness required to model and investigate criminal network entities and their associations. We demonstrate a need to rethink entity associations with one specific case (inspired by The Wire, a tv series about organized crime in Baltimore, United States) and corroborated by similar evidence from other cases. Our goal is to develop centrality measures for fragmented and non-navigational states of criminal network investigations. A network model with three basic first class entities is presented together with a topology of associations between network entities. We implement three of these associations and extend and test two centrality measures using CrimeFighter Investigator, a novel tool for criminal network investigation. Our findings show that the extended centrality measures offer new insights into criminal networks.

Analysis of Emergent and Evolving Information: The Agile Planning Case

Some information structures are by nature emergent and evolving and as a consequence the retrievable knowledge keeps shifting patterns like a kaleidoscope. Hence, information analysis can be a complex and tedious task. The planning task of agile teams is an example of such a complex information analysis task. In this paper, we present a lightweight planning tool. ASAP is inspired by concepts and principles from spatial hypertext, which have proven successful in supporting information analysis tasks. ASAP runs on a large interactive vertical display on which electronic task cards can be organized into iterations and releases using card hierarchies and separators (a novel visual concept). Several views of the evolving plan are automatically generated to assist the agile team with overviews of tasks, estimates, and assignments. Views are instantly updated to reflect changes to the plan.

QS Mapper: A transparent data aggregator for the Quantified Self: Freedom from particularity using two-way mappings

Quantified Self is a growing community of individuals seeking self-improvement through self-measurement. Initially, personal variables such as diet, exercise, sleep, and productivity are tracked. This data is then explored for correlations, to ultimately either change negative or confirm positive behavioural patterns. Tools and applications that can handle these tasks exist, but they mostly focus on specific domains such as diet and exercise. These targeted tools implement a black box approach to data ingestion and computational analysis, thereby reducing the level of trust in the information reported. We present QS Mapper, a novel tool, that allows users to create two-way mappings between their tracked data and the data model. It is demonstrated how drag and drop data ingestion, interactive explorative analysis, and customisation of computational analysis procures more individual insights when testing Quantified Self hypotheses.

CrimeFighter Investigator: Integrating synthesis and sense-making for criminal network investigation

AbstractCriminal network investigation involves a number of complex tasks and problems. Overall tasks include collection, processing, and analysis of information, in which analysis is the key to successful use of information since it transforms raw data into intelligence. Analysts have to deal with problems such as information volume and complexity which are typically resolved with more resources. This approach together with sequential thinking introduces compartmentalization, inhibits information sharing, and ultimately results in intelligence failure. We view analysis as an iterative and incremental process of creative synthesis and logic-based sense-making where all stakeholders participate and contribute. This paper presents a novel tool that supports a human-centered, target-centric model for criminal network investigation. The developed tool provides more comprehensive support for analysis tasks than existing tools and measures of performance indicate that the integration of synthesis and sense-making is feasible.