S
Shawn J. Bohn
Researcher at Pacific Northwest National Laboratory
Publications - 14
Citations - 345
Shawn J. Bohn is an academic researcher from Pacific Northwest National Laboratory. The author has contributed to research in topics: Visual analytics & Data visualization. The author has an hindex of 8, co-authored 14 publications receiving 304 citations. Previous affiliations of Shawn J. Bohn include Battelle Memorial Institute.
Papers
More filters
Proceedings ArticleDOI
Developing an Ontology for Cyber Security Knowledge Graphs
Michael D. Iannacone,Shawn J. Bohn,Grant C. Nakamura,John Gerth,Kelly M. T. Huffer,Robert A. Bridges,Erik M. Ferragut,John R. Goodall +7 more
TL;DR: An ontology developed for a cyber security knowledge graph database is described to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain.
Proceedings ArticleDOI
Real-time visualization of network behaviors for situational awareness
TL;DR: This work presents a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi that provides situational understanding of real-time network activity to help analysts take proactive response steps.
Proceedings ArticleDOI
Two-stage framework for visualization of clustered high dimensional data
TL;DR: A twostage framework for visualizing high dimensional clustered data based on dimension reduction methods is proposed, and several two-stage methods are proposed and their theoretical characteristics as well as experimental comparisons on both artificial and real-world text data sets are presented.
Proceedings ArticleDOI
Heterogeneous data fusion via space alignment using nonmetric multidimensional scaling
TL;DR: A novel graph embedding framework for space alignment is provided, which converts each data set into a graph and assigns zero distance between reference correspondence pairs resulting in a single graph for fusion based on nonmetric multidimensional scaling (MDS).
Proceedings ArticleDOI
Scalable Visual Analytics of Massive Textual Datasets
TL;DR: This paper describes the first scalable implementation of a text processing engine used in visual analytics tools and demonstrates virtually linear scaling when processing multi-gigabyte data sets such as Pubmed.