Jangwon Gim Sungjoon Lim Hanmin Jung

ISO/IEC JTC1 SC32 Ad-hoc meeting May 29, 2013, Gyeongju Korea

32N2386

Contents

  Background    Brief  history  of  discussions    Case  study    Procedure  for  developing  standardiza9ons  for  Big  Data    Reference  model  for  Big  Data  

  Conclusions

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Discussion  of  Big  Data

  Data  analy9cs    Data  analysis    Baba:  Vocabulary,  Use-­‐case,  and  so  on     Stabilize  Architecture   Define  Interfaces   Standardiza6on  opportuni6es  

  Jim:  The  aspect  of  Big  Data  is  “There  is  many  different  forms”  

  Krishna:  Refers  to  Wikipedia  defini9on  

  Keith  Gorden:  Volume,  Complex,  Velocity  

  Keith  W.  Hare:  Open  Big  Data  

   Volume,  Variety,  Velocity,  Value,  Veracity  Any  combina6on  is  OK.  

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Background

  Emerging  Technologies  For  Big  Data   In  2012,  The  hype  cycle  of  Gartner  

 Diverse  defini6ons  of  technologies  and  services,  having  different  views  of  data  

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Background

  Big  Data  on  hype  cycle  

  A  general  and  common  reference  model  for  Big  Data  is  needed

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Brief  history  of  discussions  

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Issue  Date   Summary  

16  November  2011.  [SC32N2181]  ISO/IEC  JTC  1/SC  32  N2181,  “Resolu6ons  and  topics  from  the  recent  JTC  1  mee6ng  of  par6cular  interest  to  SC  32  par6cipants”,  SC32  Chair  –  Jim  Melton  

12  January  2012.  

[SC32N2198]   ISO/IEC   JTC  1/SC  32  N  2198,   “Analysis  of  2012  Gartner  Technology  Trends”,  JTC1  SWG-­‐P  -­‐  Mario  Wendt  –  Convener     SC  6  Telecommunica6ons  and  informa6on  exchange  between  systems       SC  32  Data  management  and  interchange     SC  39  Sustainability  for  and  by  Informa6on  Technology  

19  March  2012.  [SC32N2199]ISO/IEC   JTC   1/SC   32   N   2199,   “Discussion:   SC   32   Response   to   2011   JTC   1  Resolu6on  33”,  SC32  Chair  –  Jim  Melton  

6  June  2012.   [SC32N2241]  Ad-­‐hoc  on  “Next  gen  analy6cs”  -­‐  Keith  Hair  -­‐  Chair  

The  view  of  Next-­‐Genera9on  Analy9cs  of  SC32  

  Referencing  from    [SC32N2241]  

  Need  a  reference  model  for  Big  Data  to  enhance  interoperability    

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Next-Generation Analytics Social Analytics

From Baba

Architectural

Mechanisms

Metadata

Raw Storage

Case  Study  (1)

  Korea  Ins9tute  of  Science  and  Technology  (KISTI)   Dept.  of  Computer  Intelligence  Research  

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Case  Study  (2)

  Architecture  of  InSciTe  Adap9ve  Service

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Case  Study  (3)

  Seman9c  Analysis   Text  Data  to  Ontology

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Case  Study  (4)

  Seman9c  Analysis   Ontology  Schema

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Case  Study  (5)

  Seman9c  Analysis   Example  of  Seman6c  Analysis  

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Case  Study  (6)

  InSciTe  Service  Func9ons  –  (Hybrid  Vehicle)

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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

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Sites The number of Count

Freebase 1,015,762,951

Yago 224,949,079

DBPedia 449,383,705

DBLP 81,986,947

baseKB 147,549,529

Etc (WhoisWho,NYTimes,LinkedObervedData,…) 2,296,838,760

Total 4,216,470,971

Case  Study  (8)

  In  2013,  System  Architecture  of  InSciTe  Adap9ve  Service

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Procedure  for  developing  a  reference  model  for  Big  Data  

4. Deriving use-cases for applying the Big Data

3. Defining a concept model / a reference model / a framework for Big Data

2. Establishing visions and strategies for achieving the goal of Big Data

1. Eliciting requirements and analyzing the environment of Big Data

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We are here

A lifecycle of Big Data  

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1. 2.

3. 4.

•  Collection/Identification •  Repository/Registry •  Semantic

Intellectualization •  Integration

•  Data Curation •  Data Scientist •  Data Engineer

•  Workflow •  Data Quality

•  Analytics / Prediction

•  Visualization

Reference  Model  for  Big  Data  

  A  Reference  Model  for  Big  Data

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Data Layer

Platform Layer Data Semantic Intellectualization

Data Integration

Data Quality Management

Big Data Management

Data Curation

Service Layer Analysis & Prediction

Security

Data Visualization

Service Support Layer

Workflow Management

Interface

Data Collection

Data Identification (Data Mining & Metadata Extraction)

Data Registry Data Repository

Interface

Interface

Reference  Model  for  Big  Data  

  A  Reference  Model  for  Big  Data

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Data Layer

Platform Layer Data Semantic Intellectualization

Data Integration

Data Quality Management

Big Data Management

Data Curation

Service Layer Analysis & Prediction

Security

Data Visualization

Service Support Layer

Workflow Management

Interface

Data Collection

Data Identification (Data Mining & Metadata Extraction)

Data Registry Data Repository

Interface

Interface

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13249

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Conclusions

  Summary   Analyzing  the  circumstance  of  Big  Data  

 Building  a  framework  for  Big  Data  

 Define  detail  procedure  to  create  the  Big  Data  

  Discussion   Possible  sugges6ons  

•  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  mee6ngs  

 Propose  extended  the  reference  model  of  Big  Data  (NWI)  •  2014.  5  Plenary  mee6ng  

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