Next Generation Analyt-ics & Big Data

(A Reference Model for Big Data)

Jangwon GimSungjoon LimHanmin Jung

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

32N2386

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 ArchitectureDefine InterfacesStandardization 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 DataIn 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

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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, “Resolutions and topics from the recent JTC 1 meeting 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

The view of Next-Generation Analytics of SC32

Referencing from [SC32N2241]

Need a reference model for Big Data to enhance interoperability

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Next-Generation AnalyticsSocial Analytics

From Baba

Architectural

Mechanisms

Metadata

Raw Storage

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 AnalysisText Data to Ontology

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Case Study (4)

Semantic AnalysisOntology Schema

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Case Study (5)

Semantic AnalysisExample of Semantic Analysis

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Case Study (6)

InSciTe Service Functions – (Hybrid Vehicle)

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Technology Navigation

TechnologyTrend

Core ElementTechnology

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 Adaptive 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

Data Insight

Action Decision

• Workflow• Data Quality

Big Data

• Analytics / Prediction

• Visualization

Reference Model for Big Data

A Reference Model for Big Data

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Data Layer

Platform LayerData Semantic Intellectualization

Data Integration

Data Quality Management

Big DataManagement

Data Curation

Service LayerAnalysis & 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 LayerData Semantic Intellectualization

Data Integration

Data Quality Management

Big DataManagement

Data Curation

Service LayerAnalysis & Prediction

Security

Data Visualization

Service Support Layer

Workflow Management

Interface

Data Collection

Data Identification (Data Mining & Metadata Extraction)

Data Registry Data Repository

Interface

Interface

9075

13249

11179

19763

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Conclusions

SummaryAnalyzing the circumstance of Big DataBuilding a framework for Big DataDefine detail procedure to create the Big Data

DiscussionPossible suggestions

• New Working Group for the reference model of Big Data New Work Items could be derived from the model

• New Study Group

Future workDiscussion of the concept of NWI

• 2013. 11. Interim meetingsPropose extended the reference model of Big Data (NWI)

• 2014. 5 Plenary meeting

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