operations on GPUs remains poorly developed. Existing solutions (e.g., ScaLapack for CPUs) are not well-integrated with higher level languages and require low-level programming which lengthens experiment and development time. 1. over 500M 1. classifier (e.g. 1. Hadoop or 1. visualize 1. preserve -- -- images a Support Vector enhanced large-scale 3-d privacy for users Organizing uploaded to Machine), a MapReduce reconstructions, and digital rights large-scale social media process that is and navigate for media. sites each day often hard to large-scale 27 collections parallelize. collections of of consumer 2. features seen images that have in many large been aligned to photos scale image maps. processing problems 1. distributed 1. various real 1. Hadoop and 1. data retrieval 1. security and 1. standardized data 1. low-level data sources time data HDFS (platform) and dynamic privacy policy structured/formats data storage Truthy 2. large volume analysis for 2. visualization with extremely high infrastructure Twitter real time anomaly IndexedHBase, 2. data driven data quality for efficient Data streaming detection, stream Hive, SciPy, interactive web mobile access 3. raw data in clustering, signal NumPy interfaces to data compressed classification on (software) 3. API for data 28 formats 3. in-memory query 4. fully dimensional time database, MPI structured data series and (platform) in JSON, users 4. high-speed metadata, Infiniband locations data network 5. multiple data (networking) schemas -- 1. digitize -- -- 1. privacy issues -- -- existing in preserving Crowd photo and anonymity of Sourcing in documents responses in spite Humanities archives of computer 2. analytics recording of 29 include pattern access ID and recognition of all reverse kind (e.g., speech engineering of recognition, unusual user automatic A&V responses analysis, cultural patterns),
operations on GPUs remains poorly developed. Existing solutions (e.g., ScaLapack for CPUs) are not well-integrated with higher level languages and require low-level programming which lengthens experiment and development time. 27 Organizing large-scale collections of consumer photos 1. over 500M images uploaded to social media sites each day 1. classifier (e.g. a Support Vector Machine), a process that is often hard to parallelize. 2. features seen in many large scale image processing problems 1. Hadoop or enhanced MapReduce 1. visualize large-scale 3-d reconstructions, and navigate large-scale collections of images that have been aligned to maps. 1. preserve privacy for users and digital rights for media. -- -- 28 Truthy Twitter Data 1. distributed data sources 2. large volume real time streaming 3. raw data in compressed formats 4. fully structured data in JSON, users metadata, data 5. multiple data schemas 1. various real time data analysis for anomaly detection, stream clustering, signal classification on time series and 1. Hadoop and HDFS (platform) 2. IndexedHBase, Hive, SciPy, NumPy (software) 3. in-memory database, MPI (platform) 4. high-speed Infiniband network (networking) 1. data retrieval and dynamic visualization 2. data driven interactive web interfaces 3. API for data query 1. security and privacy policy 1. standardized data structured/formats with extremely high data quality 1. low-level data storage infrastructure for efficient mobile access to data 29 Crowd Sourcing in Humanities -- 1. digitize existing photo and documents archives 2. analytics include pattern recognition of all kind (e.g., speech recognition, automatic A&V analysis, cultural patterns), -- -- 1. privacy issues in preserving anonymity of responses in spite of computer recording of access ID and reverse engineering of unusual user responses -- -- dimensional locations