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.Policing (e.g., [1,10,14]). Many models have been developed over the years, ranging from reactive community and problemoriented policing models to the more proactive intelligence-led and terror-oriented (i.e., political) policing models. These models run in parallel to the traditional law enforcement model characterized by its paramilitary and bureaucratic "command and control" structure, and focus on incident-driven response to calls for service. Police investigations include a variety of tasks like criminal profiling, crime scene analysis, data processing, and storing and sharing of information. Most information produced by police officers is difficult to represent and thus to access and communicate due to its nature. Police knowledge tends to be implicit and experience-based.Counterterrorism (e.g., [7,8,32]). Before 9/11 (2001), investigations were mainly handled by a nations security services, but are now moving towards joint operations with police in what is often referred to as the emerging policing-security nexus. Counterterrorism investigations are, like many of their targets, covert operations. The goal is to transform intelligence from different sources (humans, signals, images, open, etc.) into actionable intelligence products, typically for governments to take proactive measures in order to thwart high risk plots. Due to the

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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.

We investigate algorithms for canonical labelling of site graphs, i.e. graphs in which edges bind vertices on sites with locally unique names. We first show that the problem of canonical labelling of site graphs reduces to the problem of canonical labelling of graphs with edge colourings. We then present two canonical labelling algorithms based on edge enumeration, and a third based on an extension of Hopcroft's partition refinement algorithm. All run in quadratic worst case time individually. However, one of the edge enumeration algorithms runs in sub-quadratic time for graphs with "many" automorphisms, and the partition refinement algorithm runs in sub-quadratic time for graphs with "few" bisimulation equivalences. This suite of algorithms was chosen based on the expectation that graphs fall in one of those two categories. If that is the case, a combined algorithm runs in sub-quadratic worst case time. Whether this expectation is reasonable remains an interesting open problem

Criminal network investigations in policing, intelligence, and journalism face a number of challenges that can impact their success or failure. Some challenges, like political decisions to increase or reduce investigation resources, or amendments to criminal law that provide investigators with more wide reaching options for survaillence and interrogation are primarily in uenced by jurisprudence researchers and social scientists. Other challenges, such as those related to information about a criminal network, network investigation processes, and human factors during investigations can be supported by software tools to assist criminal network investigators. Based on the information, process, and human factors challenges, we formulated a hypothesis for useful tool support, and analyzed problems related to each challenge. Our response to these problems was a list of requirements that guided the development of new processes, tools, and techniques with the aim of reducing the impact of the challenges and support the hypothesis. We propose hypertext as the key technology to bridge investigators and tools, to provide integrated support of information synhesis and sense-making, and to increase the capabilities of investigators by leveraging man-machine synergies.