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 32N2386

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

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. 3

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

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

Brief history of discussions 6 Issue DateSummary 16 November [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 [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 [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 7 Next-Generation Analytics Social Analytics From Baba Architectural Mechanisms Metadata Raw Storage

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

Case Study (2)  Architecture of InSciTe Adaptive Service 9

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

Case Study (4)  Semantic Analysis Ontology Schema 11

Case Study (5)  Semantic Analysis Example of Semantic Analysis 12

Case Study (6)  InSciTe Service Functions – (Hybrid Vehicle) 13 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 14 SitesThe 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 15

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 16 We are here

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

Reference Model for Big Data  A Reference Model for Big Data 18 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 RegistryData Repository Interface

Reference Model for Big Data  A Reference Model for Big Data 19 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 RegistryData Repository Interface ???

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 Interim meetings Propose extended the reference model of Big Data (NWI) Plenary meeting 20