KEYWORDS: Data modeling, Analytical research, Inspection, Visualization, Web 2.0 technologies, Systems modeling, Statistical analysis, Analytics, Defense and security, Defense technologies, Data centers, Data mining, System identification
Real-time Analytics Platform for Interactive Data-mining (RAPID), a collaboration of University of Melbourne and
Australia’s Defense Science and Technology Group (DSTG), consumes data streams, performs analytics computations,
and produces high-quality knowledge for analysts. RAPID takes topic seed words and autonomously identifies emerging
keywords in the data. Users direct the system, setting time-windowing parameters, thresholds, update intervals and
sample rates. Apache Storm and Apache Kafka permit real-time streaming while logging options support off-line
processing. Decision-support scenarios feature Commander Critical Information Requirements involving comparisons
over time and time-sequencing of events, capabilities particularly well-served by RAPID technology, to be demonstrated
in the presentation.
The Human-Assisted Machine Information Exploitation (HAMIE) investigation utilizes large-scale online data
collection for developing models of information-based problem solving (IBPS) behavior in a simulated time-critical
operational environment. These types of environments are characteristic of intelligence workflow processes conducted
during human-geo-political unrest situations when the ability to make the best decision at the right time ensures strategic
overmatch. The project takes a systems approach to Human Information Interaction (HII) by harnessing the expertise of
crowds to model the interaction of the information consumer and the information required to solve a problem at different
levels of system restrictiveness and decisional guidance. The design variables derived from Decision Support Systems
(DSS) research represent the experimental conditions in this online single-player against-the-clock game where the
player, acting in the role of an intelligence analyst, is tasked with a Commander’s Critical Information Requirement
(CCIR) in an information overload scenario. The player performs a sequence of three information processing tasks
(annotation, relation identification, and link diagram formation) with the assistance of ‘HAMIE the robot’ who offers
varying levels of information understanding dependent on question complexity. We provide preliminary results from a
pilot study conducted with Amazon Mechanical Turk (AMT) participants on the Volunteer Science scientific research
platform.
KEYWORDS: Visualization, Databases, Data modeling, Analytical research, Chemical engineering, Human-machine interfaces, Chemical analysis, Internet, Information technology, Materials science
Within operational environments decisions must be made quickly based on the information available. Identifying an appropriate knowledge base and accurately formulating a search query are critical tasks for decision-making effectiveness in dynamic situations. The spreading of graph data management tools to access large graph databases is a rapidly emerging research area of potential benefit to the intelligence community. A graph representation provides a natural way of modeling data in a wide variety of domains. Graph structures use nodes, edges, and properties to represent and store data. This research investigates the advantages of information search by graph query initiated by the analyst and interactively refined within the contextual dimensions of the answer space toward a solution. The paper introduces SLQ, a user-friendly graph querying system enabling the visual formulation of schemaless and structureless graph queries. SLQ is demonstrated with an intelligence analyst information search scenario focused on identifying individuals responsible for manufacturing a mosquito-hosted deadly virus. The scenario highlights the interactive construction of graph queries without prior training in complex query languages or graph databases, intuitive navigation through the problem space, and visualization of results in graphical format.
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