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  1. 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

  2. Contents • Background • Brief history of discussions • Case study • Procedure for developing standardizations for Big Data • Reference model for Big Data • Conclusions

  3. 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.

  4. Background • Emerging Technologies For Big Data • In 2012, The hype cycle of Gartner • Diverse definitions of technologies and services, having different views of data

  5. Background • Big Data on hype cycle • A general and common reference model for Big Data is needed

  6. Brief history of discussions

  7. 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

  8. Case Study (1) • Korea Institute of Science and Technology (KISTI) • Dept. of Computer Intelligence Research

  9. Case Study (2) • Architecture of InSciTe Adaptive Service

  10. Case Study (3) • Semantic Analysis • Text Data to Ontology

  11. Case Study (4) • Semantic Analysis • Ontology Schema

  12. Case Study (5) • Semantic Analysis • Example of Semantic Analysis

  13. Case Study (6) • InSciTe Service Functions – (Hybrid Vehicle) Technology Navigation Technology Trend Core Element Technology Convergence Technology Agent Level Agent Partner Integrated Roadmap Report

  14. Case Study (7) • In 2013, About 10 Billion triples from diverse sites will be extracted

  15. Case Study (8) • In 2013, System Architecture of InSciTe Adaptive Service

  16. Procedure for developing a reference model for Big Data We are here

  17. 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

  18. 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

  19. 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

  20. 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