Big Data - Where from Where to

Copyright © 2013 Hanmin Jung

Hanmin JungHead of the Dept. of Computer Intelligence Research

KISTI

Big Data:Where from? Where to?


� Very Recent Activities on Big Data

� (National Science and Technology Commission) Member of Big Data Technical Impact Assessment Committee

� (Korea Communications Commission) Sub-committee Chair of Big Data Forum

� (Ministry of Knowledge Economy) Technical Secretary of Big Data Program Planning Committee

� (Ministry of Educational Science and Technology) Member of Big Data Information Strategic Program Expert Committee

� (National IT Industry Promotion Agency) Lecturer of Big Data Expertise Reinforcement Program

Let Me Introduce Myself :-)

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Copyright © 2013 Hanmin Jung3

Questions

Where are Big Data from?

Who gathers and consumes the data?

Is the data used for?


Smart Work

http://files.thinkpool.com/files/bbs/2010/07/21/%EC%8A%A4%EB%A7%88%ED%8A%B8%EC%9B%8C%ED%81%AC1.jpg

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Cloud Computing

� Service Platform Accelerated by Mobile Devices

http://simpleroot.com/wp-content/uploads/2012/10/Remote-Cloud-Computing.jpg

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Cloud Computing – 建建建建て前前前前& 本音本音本音本音

� Introducing iCloud


Cloud Computing

� Google Data Center

http://www.youtube.com/watch?v=avP5d16wEp0


Data Sources

Web -> Social -> Thing

“The next Google or Facebook may well bean Internet of Things company.”by R. MacManus (ReadWriteWeb)


Social Data

http://bynoy.files.wordpress.com/2011/08/united-noy-weblife-60-seconds.jpg


Machine Data

T. Baer, “What is Big Data? The Reality for Analytics”, OVUM, 2011.

Call data recordsCall data records

Sensory dataSensory data

Web log filesWeb log files

Financial Instrument TradeFinancial Instrument Trade


Internet of Things

K. Escherich, “Internet of Things”, 2011.


Big Data in the World

http://www.ektron.com/billcavablog/Big-Data-Big-Content-Big-Challenges/


Infographics for Big Data

http://thumbnails.visually.netdna-cdn.com/big-data_50291c3b16257.jpg


Google.com Traffic

http://siteanalytics.compete.com/naver.com/


Naver.com Traffic

http://siteanalytics.compete.com/naver.com/


Foreseeable Future

� Google Project Glass

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Hype Cycle


Hype Cycle – 2010

Emerging Technologies Hype Cycle 2010


Hype Cycle – 2011



Hype Cycle – 2012



Google Insights

http://www.google.com/insights/search/


Bottleneck in Data Ecosystem

http://quizzicaleyebrow.files.wordpress.com/2011/03/pict0044.jpg


Big Data Ecosystem

http://imexresearch.com/Newsletter_HTML/bd2.png


Big Data Ecosystem

� New Approaches Required for

� Persistence

� Indexing

� Caching and query optimization

� Processing

� Structure

� Query language

� Compression

24T. Baer, “What is Big Data? The Reality for Analytics”, OVUM, 2011.


Insights for Search

http://www.google.com/insights/search/


Mobile Phone

� Worldwide Market Share

� Worldwide mobile device sales to end users in 2008 ~ 2012

Gartner, IDC Worldwide Mobile Phone Tracker

4.0, 14.14.3, 17.19.9, 47.8Apple

7.5, 23.011.0, 31.68.1, 28.45.4, 21.1LG

3.3, 15.8Huawei

Company4Q2012

(%, M. Units)3Q2011

(%, M. Units)3Q2010

(%, M. Units)3Q2009

(%, M. Units)3Q2008

(%, M. Units)

Nokia 17.9, 86.3 27.1, 106.6 31.6,110.4 37.8, 108.5 38.6, 117.9

Samsung 23.0, 111.2 22.3, 87.8 20.5, 71.4 21.0, 60.2 17.0, 52.0

ZTE 3.6, 17.6 4.9, 19.1 3.5, 12.1

Sony Ericsson 4.9, 14.1 8.4, 25.7

Motorola 4.7, 13.6 8.3, 25.4

Others 42.3, 203.8 36.1, 142 32.2, 112.5 20.6, 59.1 20.1, 61.5

Total 482.5 393.7 348.9 287.1 305.4

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CDC Influenza Summary

http://www.cdc.gov/flu/weekly/usmap.htm


Google Flu Trends

J. Ginsberg, “Detecting influenza epidemics using search engine query data”


Voice Search Evaluation

http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//pubs/archive/40491.pdf


Causes of Death

http://image.guardian.co.uk/sys-files/Guardian/documents/2011/10/28/Factfile_deaths_2_2011.pdf


IBM Watson

http://powet.tv/powetblog/wp-content/uploads/2011/02/watson_the_computer_beats_ken_jennings_and_brad_rutter_at_jeopardy_full.jpg


Search

Clustering

Extracting

DecisionSupport

Forecasting

ScenarioPlanning

Advising

Modified from D. Bousfield & P. Fooladi, “STM Information: 2009 Final Market Size and Share Report”, 2010.

Value Pyramid

InSciTe Advanced (2011)

InSciTe Adaptive (2012)

OntoFrame (2005~2009)



Big Data & Decision Making

http://lithosphere.lithium.com/t5/Lithium-s-View/Big-Data-Analytics-Reducing-Zettabytes-of-Data-Down-to-a-Few/ba-p/36378

� Reducing Zettabytes of Data Down to a Few Bits

Data help us make better decisions.

The primary function of analytics is to support decision making.

The challenge of big data analytics isto reduce a lot of data down to a few bits.


Strategic Foresight

R. Rohrbeck, H. Arnold, and J. Heuer, “Strategic Foresight in Multimedia Enterprises”, 2007.

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Quantitative Analytics


TI Projects

� FUSE

� Funded by IARPA (early 2011 ~ early 2016)

� Kick off meeting in summer, 2011

� Foresight and Understanding from Scientific Exposition Program

� Seeks to develop automated methods that aid in the systematic, continuous, and comprehensive assessment of technical emergence using information found in the published scientific, technical, and patent literature

� Partners

� BAE Systems, Brandeis Univ., New York Univ., 1790 Analytics, …


TI Projects

� FUSE


TI Projects

� CUBIST

� Funded by the European Commission (late 2010 ~ late 2013)

� 1st CUBIST workshop in July, 2011

� Combining and Uniting Business Intelligence with Semantic TechnologiesProgram

� Aims to develop new ways to interrogate not only the massive volume data on the Internet, but also analyze the different formats it exist in – such as blogs, wikis, and video

� Partners

� SAP, Ontotext, Sheffield Hallam Univ., …

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TI Projects

� CUBIST


TI Projects

� Common Technologies

� Semantic technologies

� Ontology, reasoning, URI scheme

� Analytics model

� BYOM (e.g. technology opportunity discovery model, technology evolution model, formal concept analysis model)

� Information extraction (InSciTe, FUSE)

� Named entities and events/relations in textual documents

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Our Vision & Architecture

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InSciTe Adaptive (2012)


Data Fact Sheet

� InSciTe Adaptive (2012)

� Articles: 22.6 millions (9.8 millions for papers, 7.6 millions for patents, 5.3 millions for Web data)

� All technical areas (2001~2011)

� Named entities: 1.9 millions

� Authority dictionary: 1.5 millions entries

� LOD data: 290 GB (are being connected)

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Supporting Decision Making

http://4.bp.blogspot.com/-Pf1hkccZZh4/TWDJahBpL2I/AAAAAAAAASU/JHLpXi8d9AQ/s640/meetings.jpg


Data Scientist

http://philanthropy.com/blogs/innovation/matching-data-scientists-and-nonprofits/778


Evidence-based Decision Making

� Advantages

� Ensures that policies are responding to the real needs of the community

� Highlight the urgency of an issue or problem which requires immediate attention

� Enables information sharing amongst other members of the public sector

� Reduces government expenditure which may otherwise be directed into ineffective policies or programs

� Produces an acceptable return on the financial investment that is allocated toward public programs

� Ensures that decisions are made in a way that is consistent with our democratic and political processes which are characterized by transparency and accountability

http://www.abs.gov.au/ausstats/[email protected]/lookup/1500.0chapter32010

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InSciTe Project

http://semantics.kisti.re.kr


Thank you

[email protected]

“A lot of times, people don’t know what they want until you show it to them.”

by Steve Jobs

“Many people won’t be convinced until they’ve seen it for themselves.”

by Jakob Nielsen