operation 1. large amounts 1. analytic 1. PERL, Python, 1. analytic flows 1. security -- -- of algorithms C/C++, Matlab, involving users requirements for NIST IAD web working with R development protecting pages, tweets, written language, tools. Create sensitive data images, video speech, human ground-up test while enabling 2. scaling imagery, etc. and measurement meaningful ground-truthing must generally applications developmental to larger data, be tested against performance intrinsic and real or realistic evaluation. annotation data. Shared It’s uncertainty extremely evaluation 31 measurement, challenging to testbeds must performance engineer artificial protect the measurement for data that intellectual incompletely sufficiently property of annotated data, captures the analytic measuring variability of real algorithm analytic data involving developers performance for humans heterogeneous data and analytic flows involving users 1. process key 1. needs to 1. iRODS data 1. general 1. Federate -- -- format types provide general management visulaization across existing DataNet NetCDF, HDF5, analytics software workflows authentication Dicom workflows 2. environments 2. real-time and interoperability through Generic batch data across Storage Security Service and Network API and Protocol Types Pluggable Authentication 32 Modules (GSI, Kerberos, InCommon, Shibboleth). 2. access controls on files independently of the storage location. 1. integration of -- 1. software: -- 1. significant but 1. integration of -- metadata Symfony-PHP, optional security metadata The approaches Linux, MySQL & privacy approaches 33 Discinnet across disciplines including secure across disciplines process servers and anonymization 1. all data types, 1. data graph 1. cloud 1. efficient -- -- -- image to text, processing community based Semantic 34 structures to 2. RDMS resource required visualization is protein sequence needed
operation 31 NIST IAD 1. large amounts of web pages, tweets, images, video 2. scaling ground-truthing to larger data, intrinsic and annotation uncertainty measurement, performance measurement for incompletely annotated data, measuring analytic performance for heterogeneous data and analytic flows involving users 1. analytic algorithms working with written language, speech, human imagery, etc. must generally be tested against real or realistic data. It’s extremely challenging to engineer artificial data that sufficiently captures the variability of real data involving humans 1. PERL, Python, C/C++, Matlab, R development tools. Create ground-up test and measurement applications 1. analytic flows involving users 1. security requirements for protecting sensitive data while enabling meaningful developmental performance evaluation. Shared evaluation testbeds must protect the intellectual property of analytic algorithm developers -- -- 32 DataNet 1. process key format types NetCDF, HDF5, Dicom 2. real-time and batch data 1. needs to provide general analytics workflows 1. iRODS data management software 2. interoperability across Storage and Network Protocol Types 1. general visulaization workflows 1. Federate across existing authentication environments through Generic Security Service API and Pluggable Authentication Modules (GSI, Kerberos, InCommon, Shibboleth). 2. access controls on files independently of the storage location. -- -- 33 The Discinnet process 1. integration of metadata approaches across disciplines -- 1. software: Symfony-PHP, Linux, MySQL -- 1. significant but optional security & privacy including secure servers and anonymization 1. integration of metadata approaches across disciplines -- 34 Semantic 1. all data types, image to text, structures to protein sequence 1. data graph processing 2. RDMS 1. cloud community resource required 1. efficient based visualization is needed -- -- --