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Novel Visualization & Display Technologies
Visualizing data is an essential part of any data analysis. The complexity of projects in large organizations requires the decomposition of those projects into simpler tasks managed by smaller, more or less autonomous teams within the larger organization. In addition, the recent development of sophisticated software tools such as those developed by KBSI has enhanced the effectiveness of decision, design, analysis, and other activities within these teams. However, the division of large projects in such a fashion creates a new problem: the difficulty of sharing information among the different project teams, their contexts, and the different tools supporting activities performed by those teams. The utility of supporting tools is a function of the degree to which they (and the agents that use them) can share information across their different contexts in the sort of distributed environments that are a hallmark of complex projects. However, the data produced by software of this kind is typically maintained in closed architecture databases. In the overwhelming majority of cases, each software tool has its own private data repository.
The central problem with this is not necessarily that the data generated by these tools is inaccessible, but that the semantic content of this data (i.e., the information itself) is typically not accessible to other (human or computer) agents in the organization. Typically, this is because data is not very good at describing itself or representing its meaning in a way that is accessible.
A complex representation (e.g., a data model or a business process model) carries the information in virtue of some established, systematic connection between the components of the representation and the world. It is this connection that determines the semantic content of the data being represented. Typically, however, the semantic rules of a representation system for a given application and the semantic intentions of the application designers are not advertised or in any way accessible to other agents in the organization. This makes it difficult, even impossible, for such agents to determine the semantic content of a database - semantic inaccessibility.
How is it possible to access the semantics of enterprise data across different contexts? How is it possible to fix their semantics objectively in a way that permits accurate interpretation by agents outside the immediate context of this data? Without this ability, the kind of coordination between enterprise teams necessary for a truly integrated environment is not possible. The use of novel visualization mechanisms is perceived as a critical technology enabler to address the semantic inaccessibility challenge.
KBSI has excelled over the last two decades in providing quality data visualization. To briefly name a few, the RampMap™ toolkit, was developed for Tinker Air Force Base to provide digital asset visualization and management of repair facilities, ramp sites, and equipment. KBSI’s Predictive and Preventive Maintenance Expert System (PPMES), is in use at the Corpus Christi Army Depot, to monitor, diagnose, and analyze preventative maintenance action data and to provide on-line monitoring of CCAD mission-critical sensory data. In an ongoing effort, Hybrid Framework for Information Visualization Enablers (HI-FIVE), KBSI is developing novel, model-driven, ontology-centric, standards-based data visualization generation for information portals. HI-FIVE leverages the (DoD-standard) IDEF family of methods, web-services, and, finally, a proven data display format called Data Display Markup Language (DDML), also developed by KBSI.
KBSI’s DASHCON™ is a dashboard configuration and scoring model deployment framework that deploys high-payoff decision dashboards for military commanders. A set of dashboard configuration utilities make it easy for non-programmers to configure dashboards comprised of user-selected scoring models (e.g., provider categorization model).
KBSI’s Personal Data Prospector (PDP®) is an open architecture, web based application that enables rapid knowledge discovery, experiment management, and knowledge applications in distributed, collaborative environments. The PDP® technology supports an end-to-end knowledge discovery process including (1) defining the problem, (2) preparing the data (including data assessment, repair, and validation), (3) performing data search / data processing / data mining, (4) integrating the analysis results, and (5) interpreting and presenting the results for decision-making.
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