This position paper explores a means of improving cybersecurity using Big Data technologies augmented by ontology for preventing or reducing losses from cyber attacks. Because of the priority of this threat to national security, it is necessary to attain results far superior to those found in modernday security operations centers (SOCs). Focus is on the potential application of ontology engineering to this end. Issues and potential next steps are discussed. Keywords—big data; ontology; cybersecurity; modeling, search; discovery; analytics; variety; metadata

Surveillance applications to monitor health-related data have matured rapidly over the last several years. A newly emerging development is an emphasis on harvesting and evaluating the timely but potentially inaccurate information present in unstructured sources such as Internet news feeds and sites. An important development for the surveillance on both structured and unstructured datasets is the exchange not of the primary datasets that feed these systems, but of the evaluated results of such analysis. This paper introduces recent work addressing a model for the recording and tracking of events and for the dissemination of information about these events to other agencies. It will introduce a structured relational database model for events, an ontology for infectious disease events, and a semantic web representation. The strengths and weaknesses of the three approaches and future directions will be discussed.

The systems integration questions really extend beyond database and visualization aspects to include external data processing services such as numerical compute engines, statistical packages, neural networks, and the like. In constructing a system, several different design dimensions must be considered, such as ease of use, performance, size of data, coupling of systems, extensibility, heterogeneity, migration paths, distribution, etc., as outlined previously. In addition, in designing systems such as dataflow or object oriented modeled systems, it may be possible to connect two different procedures, or operations over data, but it may not be meaningful to do so. Efforts aimed at improving the integration of visualization and database systems are currently underway (e.g. Sequoia 2000, Aurora from Xidak, and PAGEIN), differing in scope and design goals. We hope that heightened awareness of the needs of users with data management and visualization problems will further increase interaction between developers of database and visualization systems in the near future, to address the numerous challenges that lie ahead.