2228993
Visualization in law enforcement
Visualization techniques have proven to be critical in helping crime analysis. By interviewing and observing Criminal Intelligence Officers (CIO) and civilian crime analysts at the Tucson Police Department (TPD), we found that two types of tasks are important for crime analysis: crime pattern recognition and criminal association discovery. We developed two separate systems that provide automatic visual assistance on these tasks. To help identify crime patterns, a Spatial Temporal Visualization (STV) system was designed to integrate a synchronized view of three types of visualization techniques: a GIS view, a timeline view and a periodic pattern view. The Criminal Activities Network (CAN) system extracts, visualizes and analyzes criminal relationships using spring-embedded and blockmodeling algorithms. This paper discusses the design and functionality of these two systems and the lessons learned from the development process and interaction with law enforcement officers.
32790214
Federating Diverse Collections of Scientific Literature
The Digital Library Initiative (DLI) project at the University of Illinois at Urbana-Champaign is developing the information infrastructure to effectively search technical documents on the Internet. The authors are constructing a large testbed of scientific literature, evaluating its effectiveness under significant use, and researching enhanced search technology. They are building repositories (organized collections) of indexed multiple-source collections and federating (merging and mapping) them by searching the material via multiple views of a single virtual collection. Developing widely usable Web technology is also a key goal. Improving Web search beyond full-text retrieval will require using document structure in the short term and document semantics in the long term. Their testbed efforts concentrate on journal articles from the scientific literature, with structure specified by the Standard Generalized Markup Language (SGML). Research efforts extract semantics from documents using the scalable technology of concept spaces based on context frequency. They then merge these efforts with traditional library indexing to provide a single Internet interface to indexes of multiple repositories.
35410760
Collaborative Workflow Management for Interagency Crime Analysis
To strengthen homeland security, there is a critical need for new tools that can facilitate real time collaboration among various law enforcement agencies. Through a field study, we find that law enforcement work is knowledge intensive and involves complex collaborative processes interrelating a large number of disparate units in a loosely defined virtual organization. To support knowledge intensive collaboration, we propose a new workflow centric framework to seamlessly integrate previously separate techniques from the fields of information retrieval and workflow management. Specifically, we develop a collaborative workflow management framework for interagency crime analysis. The key contribution of our research is that by integrating various state-of-the-art techniques innovatively, the proposed system can support real time collaboration processes in a virtual organization that evolves dynamically.
40127439
Authorship Analysis in Cybercrime Investigation
Criminals have been using the Internet to distribute a wide range of illegal materials globally in an anonymous manner, making criminal identity tracing difficult in the cybercrime investigation process. In this study we propose to adopt the authorship analysis framework to automatically trace identities of cyber criminals through messages they post on the Internet. Under this framework, three types of message features, including style markers, structural features, and content-specific features, are extracted and inductive learning algorithms are used to build feature-based models to identify authorship of illegal messages. To evaluate the effectiveness of this framework, we conducted an experimental study on data sets of English and Chinese email and online newsgroup messages. We experimented with all three types of message features and three inductive learning algorithms. The results indicate that the proposed approach can discover real identities of authors of both English and Chinese Internet messages with relatively high accuracies.
38977435
Sharing and Visualizing Infectious Disease Datasets Using the WNV-BOT Portal System
Infectious disease outbreaks are critical threats to public health and national security. Information systems play a central role in developing an effective comprehensive approach to prevent, detect, respond to, and manage infectious disease outbreaks of plants, animals, and humans.
21266732
User Misconceptions of Information Retrieval Systems
In this paper, we report results of an investigation where thirty subjects were observedperforming subject-based search in an online catalog system. The observationshave revealed a range of misconceptions users have when performing subject-basedsearch. We have developed a taxonomy that characterizes these misconceptions andhypotheses about the causes of the misconceptions. Directions for improving searchperformance are also suggested.
5225848
Towards an Automated Dental Identification System (ADIS)
This paper addresses the problem of developing an automated system for postmortem identification using dental records. The Automated Dental Identification System (ADIS) can be used by law enforcement agencies to locate missing persons using databases of dental x-rays. Currently, this search and identification process is carried out manually, which makes it very time-consuming and unreliable. In this paper, we propose architecture for ADIS, we define the functionality of its components, and we briefly describe some of the techniques used in realizing these components.
15840540
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
Presented is a model of an integrate-and-fire neuron with active dendrites and a spike-timing dependent Hebbian learning rule. The learning algorithm effectively trains the neuron when responding to several types of temporal encoding schemes: temporal code with single spikes, spike bursts and phase coding. The neuron model and learning algorithm are tested on a neural network with a self-organizing map of competitive neurons. The goal of the presented work is to develop computationally efficient models rather than approximating the real neurons. The approach described in this paper demonstrates the potential advantages of using the processing functionalities of active dendrites as a novel paradigm of computing with networks of artificial spiking neurons.
1463101
RDF/RDFS-based Relational Database Integration
We study the problem of answering queries through a RDF/RDFS ontology, given a set of view-based mappings between one or more relational schemas and this target ontology. Particularly, we consider a set of RDFS semantic constraints such as rdfs:subClassof, rdfs:subPropertyof, rdfs:domain, and rdfs:range, which are present in RDF model but neither XML nor relational models. We formally define the query semantics in such an integration scenario, and design a novel query rewriting algorithm to implement the semantics. On our approach, we highlight the important role played by RDF Blank Node in representing incomplete semantics of relational data. A set of semantic tools supporting relational data integration by RDF are also introduced. The approach have been used to integrate 70 relational databases at China Academy of Traditional Chinese Medicine.
1567676
Bringing order to the Web: automatically categorizing search results
We developed a user interface that organizes Web search results into hierarchical categories. Text classification algorithms were used to automatically classify arbitrary search results into an existing category structure on-the-fly. A user study compared our new category interface with the typical ranked list interface of search results. The study showed that the category interface is superior both in objective and subjective measures. Subjects liked the category interface much better than the list interface, and they were 50% faster at finding information that was organized into categories. Organizing search results allows users to focus on items in categories of interest rather than having to browse through all the results sequentially.
30643736
Compound Brushing Explained†
This paper proposes a conceptual model called compound brushing for modeling the brushing techniques used in dynamic data visualization. In this approach, brushing techniques are modeled as higraphs with five types of basic entities: data, selection, device, renderer, and transformation. Using this model, a flexible visual programming tool is designed not only to configure and control various common types of brushing techniques currently used in dynamic data visualization but also to investigate new brushing techniques.