Data Quality The typical problem is visualization implying quality/accuracy not available in the original data. All data should include metadata for accuracy or circular error probability. Data Types Imagery (various formats NITF, GeoTiff, CADRG) Vector (various formats shape files, kml, text streams: Object types include points, lines, areas, polylines, circles, ellipses. Data Analytics Closest point of approach, deviation from route, point density over time, PCA and ICA Big Data Specific Indexing, retrieval and distributed analysis Challenges (Gaps) Visualization generation and transmission Big Data Specific Visualization of data at the end of low bandwidth wireless connections. Challenges in Mobility Security & Privacy Data is sensitive and must be completely secure in transit and at rest Requirements (particularly on handhelds) Highlight issues for Geospatial data requires unique approaches to indexing and distributed generalizing this use analysis. case (e.g. for ref. architecture) More Information (URLs) Applicable Standards: Geospatial Indexing: Quad Trees, Space Filling Curves (Hilbert Curves) – You can google these for lots of references. Note: The has been some work with in DoD related to this problem set. Specifically, the DCGS-A standard cloud (DSC) stores, indexes, and analyzes some big data sources. However, many issues still remain with visualization. Defense Big Data) Requirements WG Use Case Template Aug 11 2013 NBD(NIST Use Case Title Object identification and tracking from Wide Area Large Format Imagery (WALF) Imagery or Full Motion Video (FMV) Persistent Surveillance – Vertical (area) Defense (Intelligence) Author/Company/Email David Boyd/Data Tactics/dboyd@data-tactics.com Actors/Stakeholders and Civilian Military decision makers their roles and Intelligence Analysts responsibilities Warfighters Goals To be able to process and extract/track entities (vehicles, people, packages) over time from the raw image data. Specifically, the idea is to reduce the petabytes of data generated by persistent surveillance down to a manageable size (e.g. vector tracks) Use Case Description
Data Quality The typical problem is visualization implying quality/accuracy not available in the original data. All data should include metadata for accuracy or circular error probability. Data Types Imagery (various formats NITF, GeoTiff, CADRG) Vector (various formats shape files, kml, text streams: Object types include points, lines, areas, polylines, circles, ellipses. Data Analytics Closest point of approach, deviation from route, point density over time, PCA and ICA Big Data Specific Challenges (Gaps) Indexing, retrieval and distributed analysis Visualization generation and transmission Big Data Specific Challenges in Mobility Visualization of data at the end of low bandwidth wireless connections. Security & Privacy Requirements Data is sensitive and must be completely secure in transit and at rest (particularly on handhelds) Highlight issues for generalizing this use case (e.g. for ref. architecture) Geospatial data requires unique approaches to indexing and distributed analysis. More Information (URLs) Applicable Standards: Geospatial Indexing: Quad Trees, Space Filling Curves (Hilbert Curves) – You can google these for lots of references. Note: The has been some work with in DoD related to this problem set. Specifically, the DCGS-A standard cloud (DSC) stores, indexes, and analyzes some big data sources. However, many issues still remain with visualization. Defense NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013 Use Case Title Object identification and tracking from Wide Area Large Format Imagery (WALF) Imagery or Full Motion Video (FMV) – Persistent Surveillance Vertical (area) Defense (Intelligence) Author/Company/Email David Boyd/Data Tactics/dboyd@data-tactics.com Actors/Stakeholders and their roles and responsibilities Civilian Military decision makers Intelligence Analysts Warfighters Goals To be able to process and extract/track entities (vehicles, people, packages) over time from the raw image data. Specifically, the idea is to reduce the petabytes of data generated by persistent surveillance down to a manageable size (e.g. vector tracks) Use Case Description