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32N2386. Next Generation Analytics & Big Data (A Reference Model for Big Data). Jangwon Gim Sungjoon Lim Hanmin Jung ISO/IEC JTC1 SC32 Ad-hoc meeting May 29, 2013, Gyeongju Korea. Contents. Background Brief history of discussions Case s tudy
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32N2386 Next Generation Analytics & Big Data(A Reference Model for Big Data) JangwonGim Sungjoon Lim Hanmin Jung ISO/IEC JTC1 SC32 Ad-hoc meeting May 29, 2013, Gyeongju Korea
Contents • Background • Brief history of discussions • Case study • Procedure for developing standardizations for Big Data • Reference model for Big Data • Conclusions
Discussion of Big Data • Data analytics • Data analysis • Baba: Vocabulary, Use-case, and so on • Stabilize Architecture • Define Interfaces • Standardization opportunities • Jim: The aspect of Big Data is “There is many different forms” • Krishna: Refers to Wikipedia definition • Keith Gorden: Volume, Complex, Velocity • Keith W. Hare: Open Big Data • Volume, Variety, Velocity, Value, Veracity Any combination is OK.
Background • Emerging Technologies For Big Data • In 2012, The hype cycle of Gartner • Diverse definitions of technologies and services, having different views of data
Background • Big Data on hype cycle • A general and common reference model for Big Data is needed
Architectural The view of Next-Generation Analytics of SC32 • Referencing from [SC32N2241] • Need a reference model for Big Data to enhance interoperability Mechanisms Metadata Next-Generation Analytics Social Analytics From Baba Raw Storage
Case Study (1) • Korea Institute of Science and Technology (KISTI) • Dept. of Computer Intelligence Research
Case Study (2) • Architecture of InSciTe Adaptive Service
Case Study (3) • Semantic Analysis • Text Data to Ontology
Case Study (4) • Semantic Analysis • Ontology Schema
Case Study (5) • Semantic Analysis • Example of Semantic Analysis
Case Study (6) • InSciTe Service Functions – (Hybrid Vehicle) Technology Navigation Technology Trend Core Element Technology Convergence Technology Agent Level Agent Partner Integrated Roadmap Report
Case Study (7) • In 2013, About 10 Billion triples from diverse sites will be extracted
Case Study (8) • In 2013, System Architecture of InSciTe Adaptive Service
Procedure for developing a reference model for Big Data We are here
A lifecycle of Big Data • Collection/Identification • Repository/Registry • Semantic Intellectualization • Integration • Analytics / Prediction 1. 2. • Visualization Data Insight Big Data Action Decision 3. 4. • Data Curation • Data Scientist • Data Engineer • Workflow • Data Quality
Reference Model for Big Data • A Reference Model for Big Data Service Layer Big Data Management Analysis & Prediction Interface Workflow Management Data Quality Management Data Visualization Service Support Layer Data Curation Interface Platform Layer Data Integration Security Data Semantic Intellectualization Interface Data Layer Data Identification (Data Mining & Metadata Extraction) Data Collection Data Registry Data Repository
Reference Model for Big Data • A Reference Model for Big Data ??? 19763 Service Layer Big Data Management Analysis & Prediction Interface Workflow Management Data Quality Management Data Visualization Service Support Layer Data Curation Interface Platform Layer Data Integration Security Data Semantic Intellectualization 13249 Interface Data Layer 9075 Data Identification (Data Mining & Metadata Extraction) Data Collection Data Registry Data Repository 11179
Conclusions • Summary • Analyzing the circumstance of Big Data • Building a framework for Big Data • Define detail procedure to create the Big Data • Discussion • Possible suggestions • New Working Group for the reference model of Big Data • New Work Items could be derived from the model • New Study Group • Future work • Discussion of the concept of NWI • 2013. 11. Interim meetings • Propose extended the reference model of Big Data (NWI) • 2014. 5 Plenary meeting