Big Data Science Veracity (Robustness Important to make as accurate as possible, subject (collection, Issues) to limitations of computer vision technology. curation, Visualization Visualize large-scale 3-d reconstructions, and analysis, navigate large-scale collections of images that have action) been aligned to maps. Data Quality Features observed in images are quite noisy due both to imperfect feature extraction and to properties of specific images (lens distortions, sensor noise, image effects added by user, etc.) Data Types Images, metadata Data Analytics Big Data Specific Analytics needs continued monitoring and improvement. Challenges (Gaps) Big Data Specific Many/most images are captured by mobile devices; eventual goal is to Challenges in Mobility push reconstruction and organization to phone to allow real-time interaction with the user. Security & Privacy Need to preserve privacy for users and digital rights for media. Requirements Highlight issues for Components of this use case including feature extraction, feature generalizing this use matching, and large-scale probabilistic inference appear in many or most case (e.g. for ref. computer vision and image processing problems, including recognition, architecture) stereo resolution, image denoising, etc. More Information (URLs) http://vision.soic.indiana.edu/disco Note: <additional comments> Deep Learning and Social Media Big Data) Requirements WG Use Case Template Aug NBD(NIST 11 2013 Use Case Title Truthy: Information diffusion research from Twitter Data Vertical (area) Scientific Research: Complex Networks and Systems research Author/Company/Email Filippo Menczer, Indiana University, ; Alessandro Flammini, Indiana University, ; Emilio Ferrara, Indiana University, ; Actors/Stakeholders and Research funded by NFS, DARPA, and McDonnel Foundation. their roles and responsibilities Goals Understanding how communication spreads on socio-technical networks. Detecting potentially harmful information spread at the early stage (e.g., deceiving messages, orchestrated campaigns, untrustworthy information, etc.) Use Case Description (1) Acquisition and storage of a large volume of continuous streaming data from Twitter (~100 million messages per day, ~500GB data/day increasing over time); (2) near real-time analysis of such data, for anomaly detection, stream clustering, signal classification and
Big Data Science (collection, curation, analysis, action) Veracity (Robustness Issues) Important to make as accurate as possible, subject to limitations of computer vision technology. Visualization Visualize large-scale 3-d reconstructions, and navigate large-scale collections of images that have been aligned to maps. Data Quality Features observed in images are quite noisy due both to imperfect feature extraction and to properties of specific images (lens distortions, sensor noise, image effects added by user, etc.) Data Types Images, metadata Data Analytics Big Data Specific Challenges (Gaps) Analytics needs continued monitoring and improvement. Big Data Specific Challenges in Mobility Many/most images are captured by mobile devices; eventual goal is to push reconstruction and organization to phone to allow real-time interaction with the user. Security & Privacy Requirements Need to preserve privacy for users and digital rights for media. Highlight issues for generalizing this use case (e.g. for ref. architecture) Components of this use case including feature extraction, feature matching, and large-scale probabilistic inference appear in many or most computer vision and image processing problems, including recognition, stereo resolution, image denoising, etc. More Information (URLs) http://vision.soic.indiana.edu/disco Note: <additional comments> Deep Learning and Social Media NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013 Use Case Title Truthy: Information diffusion research from Twitter Data Vertical (area) Scientific Research: Complex Networks and Systems research Author/Company/Email Filippo Menczer, Indiana University, ; Alessandro Flammini, Indiana University, ; Emilio Ferrara, Indiana University, ; Actors/Stakeholders and their roles and responsibilities Research funded by NFS, DARPA, and McDonnel Foundation. Goals Understanding how communication spreads on socio-technical networks. Detecting potentially harmful information spread at the early stage (e.g., deceiving messages, orchestrated campaigns, untrustworthy information, etc.) Use Case Description (1) Acquisition and storage of a large volume of continuous streaming data from Twitter (~100 million messages per day, ~500GB data/day increasing over time); (2) near real-time analysis of such data, for anomaly detection, stream clustering, signal classification and