32N2386. Jangwon Gim Sungjoon Lim Hanmin Jung. ISO/IEC JTC1 SC32 Ad-hoc meeting May 29, 2013, Gyeongju Korea

Transcription

1 32N2386 Jangwon Gim 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 standardiza9ons for Big Data Reference model for Big Data Conclusions 2

3 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. 3

4 Background Emerging Technologies For Big Data In 2012, The hype cycle of Gartner Diverse defini6ons of technologies and services, having different views of data 4

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

6 Brief history of discussions Issue Date Summary 16 November [SC32N2181] ISO/IEC JTC 1/SC 32 N2181, Resolu6ons and topics from the recent JTC 1 me e6ng of par6cular interest to SC 32 par6cipants, SC32 Chair Jim Melton 12 January [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 [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 [SC32N2241] Ad- hoc on Next gen analy6cs - Keith Hair - Chair 6

7 The view of Next- Genera9on Analy9cs of SC32 Referencing from [SC32N2241] Architectural Next-Generation Analytics Social Analytics From Baba Mechanisms Metadata Raw Storage Need a reference model for Big Data to enhance interoperability 7

8 Case Study (1) Korea Ins9tute of Science and Technology (KISTI) Dept. of Computer Intelligence Research 8

9 Case Study (2) Architecture of InSciTe Adap9ve Service 9

10 Case Study (3) Seman9c Analysis Text Data to Ontology 10

11 Case Study (4) Seman9c Analysis Ontology Schema 11

12 Case Study (5) Seman9c Analysis Example of Seman6c Analysis 12

13 Case Study (6) InSciTe Service Func9ons (Hybrid Vehicle) Technology Navigation Technology Trend Core Element Technology Convergence Technology Agent Level Agent Partner Integrated Roadmap Report 13

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

15 Case Study (8) In 2013, System Architecture of InSciTe Adap9ve Service 15

16 Procedure for developing a reference model for Big Data 1. Eliciting requirements and analyzing the environment of Big Dat a 2. Establishing visions and strategies for achieving the goal of Big Data 3. Defining a concept model / a reference model / a framework for Big Data We are here 4. Deriving use-cases for applying the Big Data 16

17 A lifecycle of Big Data Collection/Identification Repository/Registry Semantic Intellectualization Integration Analytics / Prediction Visualization Data Curation Data Scientist Data Engineer Workflow Data Quality 17

18 Reference Model for Big Data A Reference Model for Big Data Analysis & Prediction Service Layer Big Data Management Interface Interface Workflow Management Data Quality Management Data Visualization Service Support Layer Data Curation Data Integration Data Semantic Intellectualization Platform Layer Security Interface Data Identification (Data Mining & Metadata Extraction) Data Collection Data Registry Data Repository Data Layer 18

19 Reference Model for Big Data A Reference Model for Big Data??? Analysis & Prediction Service Layer Big Data Management Interface Interface Workflow Management Data Quality Management Data Visualization Service Support Layer Data Curation Data Integration Data Semantic Intellectualization Platform Layer Security Interface Data Identification (Data Mining & Metadata Extraction) Data Collection Data Registry Data Repository Data Layer

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 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 Interim mee6ngs Propose extended the reference model of Big Data (NWI) Plenary mee6ng 20