A Reference Model for Big Data

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 study Procedure for developing standardizations for Big Data Reference model for Big Data Conclusions

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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.

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

 Diverse definitions 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 5

Brief history of discussions Issue Date

Summary

16 November 2011.

[SC32N2181] ISO/IEC JTC 1/SC 32 N2181, “Resolutions and topics from the recent JTC 1 me eting of particular interest to SC 32 participants”, 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 Telecommunications and information exchange between systems  SC 32 Data management and interchange  SC 39 Sustainability for and by Information Technology

19 March 2012.

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

6 June 2012.

[SC32N2241] Ad-hoc on “Next gen analytics” - Keith Hair - Chair

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The view of Next-Generation Analytics 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

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

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Case Study (2)  Architecture of InSciTe Adaptive Service

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Case Study (3)  Semantic Analysis  Text Data to Ontology

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Case Study (4)  Semantic Analysis  Ontology Schema

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Case Study (5)  Semantic Analysis  Example of Semantic Analysis

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Case Study (6)  InSciTe Service Functions – (Hybrid Vehicle) Technology Navigation

Technology Trend

Core Element Technology

Convergence Technology

Agent Level

Agent Partner

Integrated Roadmap

Report

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

Sites Freebase

The number of Count 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

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Case Study (8)  In 2013, System Architecture of InSciTe Adaptive Service

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Procedure for developing a reference model for Big Data

1. Eliciting requirements and analyzing the environment of Big Data

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

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

4. Deriving use-cases for applying the Big Data

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A lifecycle of Big Data

• Collection/Identification • Repository/Registry • Semantic 1. Intellectualization • Integration Data

• Analytics / Prediction

2. • Visualization

Insight

Big Data Action • Data Curation • Data Scientist • Data Engineer

3.

Decision

4.

• Workflow • Data Quality

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Reference Model for Big Data  A Reference Model for Big Data

Service Layer

Analysis & Prediction

Big Data Management

Interface

Workflow Management

Data Quality Management

Data Visualization

Service Support Layer

Interface

Data Curation

Data Integration Platform Layer

Data Semantic Intellectualization

Security

Interface

Data Identification (Data Mining & Metadata Extraction) Data Collection

Data Registry

Data Layer

Data Repository

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Reference Model for Big Data  A Reference Model for Big Data

??? 19763 Service Layer

Analysis & Prediction

Big Data Management

Interface

Workflow Management

Data Quality Management

Data Visualization

Service Support Layer

Interface

Data Curation

Data Integration Platform Layer

Data Semantic Intellectualization Interface

9075

Security

13249

Data Identification (Data Mining & Metadata Extraction) Data Collection

Data Registry

Data Layer

Data Repository

11179 19

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

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