Big Data and Future Networks: A Perspective from the...

Big Data and Future Networks:A Perspective

from the United States

Hisashi Kobayashi （小林久志）（小林久志）（小林久志）（小林久志）

Princeton University and

National Institute for Information and Communications Technology

2 Big Data and Future Network Design Hisashi Kobayashi

Acknowledgments

Prof. Tadao Saito, Toyota Info Technology CenterDr. Nozumu Nishinaga, Mr. Masahiro Kiyokawa, andMr. Hiroaki Yano, NICTProf. Mung Chiang, Princeton UniversityDr. Evangelos Eleftheriou, IBM Zurich Research LabMr. Kaiser Fung, Author “Numbers Rule Your World”Dr. Kazuo Iwano, Mitsubishi CorporationProf. Brian L. Mark, George Mason UniversityProf. Dipanker Raychaudhuri, Rutgers UniversityProf. Phuoc Tran-Gia, University of WürzburgProf. Howard Wactlar, CMU and NSF CISE DirectorateProf. Philip Yu, University of Illinois at Chicago


Outline� How Much Information? How Big is Data? 4� President Obama’s Open Government Initiative 12� President Obama’s Big Data Initiative 16� Big Data in Science and Technology Research 17

- NITRD Program, NSF, DARPA, DOE � Big Data in Enterprises 27� Call for Data Science and Data Scientists 36� Big Data and Networks 43� References 51


HOW MUCH INFORMATION?

HOW BIG IS DATA?


Source: The World of Data (by IBM): http://adamov.net.ru/images/share/the-world-of-large-scale-data-processing.jpg


Online:Disk Drives

File Systems300 Petabytes

Offline:Magnetic Tape

CDs8 Exabytes

Analog Data:Paper – Film

Videotape200 Exabytes

/

Petabyte [1,000,000,000,000,000 bytes OR 1015 bytes]

Exabyte [1,000,000,000,000,000,000 bytes OR 1018 bytes]

How Much Data was Out There? [Kobayashi et al. 2005]

Source: http://www.sims.berkeley.edu/research/projects/how-much-info-2003

cf. 2003 Report by a U.C. Berkeley research group.


Some Big Numberskilo 10 3

Mega 106

Giga 10 9

Tera 1012

Peta 1015

Exa 1018

Zetta 1021

Yotta 10 24

� 0.43 x 1018 seconds: The Age of the Universe (13.77 billion years).

� 5 Exabytes: All words ever spoken by human beings (in text) Roy Williams (Caltech, 1993)

� 21 Exabytes/month: Global Internet traffic in 2007Padmasree Warrior (CISCO, March 2010)

� 160 Exabytes: Digital information created, captures, and replicated world wide in 2007(International Data Corporation, 2007)

� 42 Zettabytes: All words ever spoken by human beings (if digitized in 6kHz 16 bit audio)

Mark Lieberman (U. Penn, 2003)


Source: Asigra Info Graphic: http://thumbnails.visually.netdna-cdn.com/big-data-infographic_504f4d2f5bd2f.jpg


Source: - The Retailer's Guide: http://venturebeat.files.wordpress.com/2012/11/retailersbigdata_final.png


Source: http://www.weforum.org/reports/personal-data-emergence-new-asset-classJanuary 2011, Davos Switzerland


� Raw data has little value by itself. � We must process data and extract information in a

usable form.- Big Data tools, e.g., Apache Hadoop, MapReduce- “Data Science,” (data mining, machine learning) - Need for advancing statistical analysis techniquesthat are “scalable.”

� We then must put the information into a valuableaction , e.g., Amazon.com, a better government

Every day, we create 2.5 quintillion (1018) bytes (i.e., 2.5 Exabytes) of data — so much that 90% of the data in the world today has been created in the last two years alone. [IBM]

Open Government Initiative

My administration is committed to creating an unprecedented level of openness in Government. We will work together to ensure the public trust an d establish a system of transparency, public participation, and collaboration. Openness will strengthen our democracy and promote efficiency and effectiveness in Government.

---- President BARACK OBAMA, 01/21/09


� Government should be transparent

- To promote accountability and provides information to citizens

� Government should be participatory

- Knowledge is widely dispersed in society, and public officials benefit from having access to that knowledge.

� Government should be collaborative

- We should use innovative tools, methods and systems to cooperate with nonprofit organizations, businesses, and individuals in the private sector.


Open Government Directive1. Publish Government Information Online

2. Improve the Quality of Government Information

3. Create and Institutionalize a Culture of Open Government

4. Create an Enabling Policy Framework for Open Government

-- Peter R. Orszag, Director, Office of Management and Budget, 12/8/09


http://www.whitehouse.gov/sites/default/files/omb/assets/memoranda_2010/m10-06.pdf

15

Big Data and Future Network Design Hisashi Kobayashi Source: Howard Wactlar, NSF CISE Directorate at N IST Big Data Meeting, June 2012

President Obama’s “Big Data Initiative”

� To advance state-of-the-art technologies to collect , store, preserve, manage, analyze and share Big Data.

� To accelerate the pace of discovery in science and engineering, strengthen the national security, and transform teaching and learning.

� To expand the work force needed to develop and use Big Data technologies.


More than $200 millions in new commitments through six Federal departments and agencies.

- Office of Science and Technology Policy (OSTP)announced on March 29, 2012


BIG DATA IN

SCIENCE AND TECHNOLOGY RESEARCH

NITRD (Networking and Information Technology Research and Development ) Program

� Provides a framework in which many Federal agencies coordinate their R&D efforts on networking and IT .

� Operates under the aegis of the NITRD Subcommittee ofthe National Science and Technology Council (NSTC) ’sCommittee on Technology.

� The National Coordination Office (NCO) supports the NITRD Program by providing technical expertise, planning and coordination and by serving as the Program ’s centralpoint of contact.


The NITRD Program’s focus:

�Big Data (BD)

�Cyber Physical Systems (CPS)

�Cyber Security and Information Assurance (CSIA)

�Health Information Technology R & D (Health IT R&D)

�Human Computer Interaction and Information Management (HCI&IM)

�Etc.



Source: Howard Wactlar, NSF CISE Directorate at N IST Big Data Meeting, June 2012

XDATA Program

� Invest $25 million/year

� Develop computational techniques and software tools , for both semi-structured (e.g., tabular, relational, categorical, meta-data) and unstructured (e.g., text documents, message traffic) data.

- Scalable algorithms for processing imperfect data in distributed data stores;

- Effective human-computer interaction tools for rapidly customizable visual reasoning


Scalable Data Management Analysis and Visualization (SDAV) Institute: ($25 million over 5 years)


Project Leader: Dr. Arie ShoshaniLawrence Berkeley National Laboratory

DOE’s


BIG DATA IN

ENTERPRISES


http://sourcedigit.com/700-big-data-market-size-forecasts-2012-17/

The Big Data market will exceed $50B worldwide by 2017.


The Big Data Market. IDC Japan’s Forecast 2011年年年年 142.5 億円、億円、億円、億円、 2012年年年年 197億円、億円、億円、億円、 2016年年年年 765億円億円億円億円

現在の現在の現在の現在のBigData市場は市場は市場は市場はIT 市場全体の市場全体の市場全体の市場全体の

13兆円の兆円の兆円の兆円の 0.1% 強程度強程度強程度強程度


Source: http://www.microsoft.com/ja-jp/sqlserver/2012/big-data/default.aspx

Another Forecast is much Bigger (by an order of magnitude)


Big Data: The Management Revolution

� Success story of Amazon.com30-40% annual growth in 2008-2012 [HBR]

� Data Analytics (DA) will replace the HiPPO.HiPPO= Highest Paid Person’s Opinion [HBR]

� Data analysts (or data scientists) are inshort supply.

[HBR]: Harvard Business Review, October 2012: http://hbr.org/archive-toc/BR1210Diamond ハーバード・ビジネス・レビューハーバード・ビジネス・レビューハーバード・ビジネス・レビューハーバード・ビジネス・レビュー「ビッグデータ競争元年」「ビッグデータ競争元年」「ビッグデータ競争元年」「ビッグデータ競争元年」February 2013


Big Data in Enterprises cont’d�Big Data exceeds the processing capacity of conventional relational database systems.� Big Data primarily addresses the database (DB)/data warehousing (DWH) aspect of data analysis.� “Apache Hadoop ” is the first technology for Big Data.

-- Distributed data storage-- Analysis algorithms for parallel data


� A distributed computational framework that can process a wide range of datasets.

� High-performance parallel data processing using MapReduce .

� Reliable data storage using the HadoopDistributed File System (HDFS).

- Query language is NoSQL (“Not only SQL”)� Typical users seem obsessed with “quantity”, not

“quality,” of data. More thought should be given how to collect and select data [Kaiser Fung] .


How to handle 3 Vs [IBM]

1. Volume:- Massively parallel processing (e.g., Greenplum data computing) - Distributed computing platform (e.g., Apache Hadoop).

2. Velocity: - Processing of “streaming data” to keep storage requirement

practical. (e.g., Large Hadron Collider at CERN)- Instantaneous response in some applications (e.g., financial trading)

3. Variety: - Need to deal with diverse data types and sources (e.g., text from SNS,

data from sensors, image data, GPS data from mobile phones, etc.)

[IBM] http://www-01.ibm.com/software/data/bigdata


� Data Warehousing (DWH): Store large volumes information from multiple sources.

� Hadoop-based Analytics: Reduce the cost of analyzing massive data.

� Unstructured Database (as well as RDB) and NoSQL� Stream Computing: Continuously analyze data to take action in

real-time.� Text Analytics (or Text Mining): Analyze textual content of

unstructured information, using information retrieval, data miningmachine learning, statistics and computational linguistics.

� Data Visualization Tools (or Infographics): Real-time processing and dashboard presentation.

e.g. Tableau [http://www.tableausoftware.com/], Spotfire [http://spotfire.tibco.jp/], etc.

Big Data Platform


Some Vendors of Big Data Tools� Greenplum: http://en.w ikipedia.org/wiki/Greenplum

- founded in 2003- acquired by EMC in 2010

� Netezza: http://en.wikipedia.org/wiki/Netezza- founded in 2000.- acquired by IBM in 2011 for $1.7B.

� SPSS: http://ja.wikipedia.org/wiki/SPSS -founded in 1988-acquired by IBM in 2009 for $1.2 B)

� Vertica (acquired by HP) � Oracle, SAP and Microsoft also provide Big Data Tools

日本に関しては；日本に関しては；日本に関しては；日本に関しては；日経コンピュータ日経コンピュータ日経コンピュータ日経コンピュータ 2013年年年年 1月月月月10 日号日号日号日号


Call for Better DATA SCIENCEAnd

More DATA SCIENTISTS


� Try to gain “insights” from data, instead of presentingall collected data.

� Study and extend “classical statistical techniques”:- Exploratory Data Analysis (EDA).- Time Series Analysis- Hidden Markov Models (HMMs)- Bayesian Statistics and MCMC- etc.

� “Scalable” Algorithms and Analyticse.g., PageRank Algorithm (an efficient algorithm to

compute eigenvectors of a Markov transition matrix)


Important Subfields of Data Mining

� Data stream mining [Aggrawal]

- Computer network traffic- Web searches- Sensor data

� Graph mining [Aggrawal]

- Web data- Social network analysis- Bio-informatics

C. C. Aggrawal (Ed.) Data Streams: Models and Algorithms, Kluwer Academic PublisherC. C. Aggarwal and H. Wang (Eds.), Managing and Mining Graph Data, Springer


Source: http://blogs.itmedia.co.jp/business20/2012/10/post-2438.html

深刻な日本のデータ・サイエンテ深刻な日本のデータ・サイエンテ深刻な日本のデータ・サイエンテ深刻な日本のデータ・サイエンテイイイイスト不足スト不足スト不足スト不足

データ・アナリシスに関する知識（統計、機械学習など）を持つデータ・アナリシスに関する知識（統計、機械学習など）を持つデータ・アナリシスに関する知識（統計、機械学習など）を持つデータ・アナリシスに関する知識（統計、機械学習など）を持つ

新卒者の数新卒者の数新卒者の数新卒者の数（（（（2008年）：年）：年）：年）：

米国米国米国米国 24,730,中国中国中国中国 17,410,インドインドインドインド 13,270,日本日本日本日本 3,400.（中国では年（中国では年（中国では年（中国では年 +10.4% 増加、日本では増加、日本では増加、日本では増加、日本では -5.3%））））

SAS(Statistical Analysis System) 認定プロフェッショナルの数。認定プロフェッショナルの数。認定プロフェッショナルの数。認定プロフェッショナルの数。

米国米国米国米国 10,544, インドインドインドインド 5,907, 韓国韓国韓国韓国 1,381, 英国英国英国英国 1,242 日本日本日本日本 800。。。。

GDP当りの当りの当りの当りのSAS認定プロフェッショナルの数（米国を認定プロフェッショナルの数（米国を認定プロフェッショナルの数（米国を認定プロフェッショナルの数（米国を100)米国米国米国米国 100, インドインドインドインド 458, 韓国韓国韓国韓国 177, 英国英国英国英国 73, 日本日本日本日本 20.

Source: Diamond ハーバード・ビジネス・レビューハーバード・ビジネス・レビューハーバード・ビジネス・レビューハーバード・ビジネス・レビュー Feb. 2013


[McKinsey] “Big data: The next frontier for innovat ion, competition and productivity,” McKinsey & Co., May 2011


BIG DATA and NETWORKS


Source: - What happens in an Internet Minute? (by Intel):http://www.intel.com/content/dam/www/public/us/en/images/illustrations/embedded-infographic-600-logo.jpg


Big Data vs. Networks� Networks to cope with Big Data.

- Sufficient storage, bandwidth and processing � Big Data to help design and manage

Networks.- Better performance, reliability and security

� Big Data and Networks for a better world.- Transparent government, Law enforcement- Risk management- Innovative applications for value creation

e.g., User behavior tracking and marketing(Privacy and security are critical).


� Cloud computing offers an on-demand accessto a shared pool of configurable resources.

� Big Data requires a novel approach to meet the storage and processing requirements.

� The Cloud can make big data (analytics)accessible to those who couldn’t use otherwise.

� Disk storage performance can be a problem when it is shared by various users.

“Cloud Computing & Networking”: A Platform for Big Data


OpenFlow and FLARE §

will help Data Centers handle Big Data

� Help control of connectivity of Data Centers for big data analytics via virtualization

� Especially useful to a “Multi-tenant Data Center ” environment.

� Facilitate load balancing among Data Centers.

§ FLARE: Deeply Programmable Network (DPN) Architect ure byAki Nakao


“ID/Locator Separation” and “Context-oriented Service” for Big Data

� Where “contexts ” means “data attributes ,” e.g., identity, group association, time, location, etc.� “Data Centric Networking ” (also called “Named Data Networking or NDN”) appears a proper approach to Big Data. � But its performance implications are unclear.� GUID (Globally Unique ID) of MobilityFirstalso facilitates context-oriented service.


Optical Technologies: Fast Transportand Processing of Big Data

� Integrated Optical Path and Optical Packets of the AKARI Architecture.

� Silicon Nanophotonics Technology - Integrates optical and electrical circuitson a single silicon chip, by using 90nmCMOS fabrication line.

cf. IBM Press release, Dec 10, 2012 http://www-03.ibm.com/press/us/en/pressrelease/39641.wss


Additional Issues that Future Network Architectures should Address:

� Interface to Database- Increasingly unstructured and heterogeneous- Requires fast processing and transportation

� The Database community and the Networking community should interact.- No FIA project addresses database issues

� Service Layer for Big Data applications


References[Kobayashi et al 2005] H. Kobayashi, Francois Dolivo, E. Eleftheriou, “35 Years of Progress in Digital Magnetic Recording,” 2005 Eduard Rhein Technology Award Lecture. [IBM] http://www-01.ibm.com/software/data/bigdata[UCB] http://www.sims.berkeley.edu/research/projects/how-much-info-2003 [McKinsey] “Big data: The next frontier for innovation, competition and productivity,” McKinsey & Co., May 2011, http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation[IBM] “IBM Lights Up Silicon Chips to Tackle Big Data,”Press release Dec 12, 2012,http://www-03.ibm.com/press/us/en/pressrelease/39641.wss


Appendix

�Big Data across the Federal Government (4)�NITRD’s Focus (2)�NSF-NIH Initiative (2)�MiKinsey Global Institute’s Report (2)�2012 Summer Olympic Games’ Big Numbers�Data Never Sleeps (Fortune Magazine, 7/ 2012)�Twitter 2012�Big Data for Healthcare

Big Data Across the Federal GovernmentMarch 29, 2012

� Department of Defense (DOD)

Defense Advanced Research Projects Agency (DARPA)

- Anomaly Detection at Multiple Scales (ADAMS) program

- Cyber-Insider Threat (CINDER) program

� Department of Homeland Security (DHS)

- Center of Excellence on Visualization and Data Analytics

� Department of Energy (DOE)

- Advanced Scientific Computing Research (ASCR)

- High Performance Storage System (HPSS)



� Department of Veterans Administration (VA)- Consortium for Healthcare Informatics Research (CHIR)- Corporate Data Warehouse (CDW)- Genomic Information System for Integrated Science (GenISIS)

� Department of Health and Human Services (HHS)Center for Disease Control & Prevention (CDC)- BioSense 2.0 programCenter for Medicare & Medicaid Services (CMS)- A date warehouse based on Hadoop is being developed.- Use of XML database technologies is being evaluated.Food & Drug Administration (FDA)- Virtual Laboratory Environment (VLE)

� National Archives & Record Administration (NARA)- Cyberinfrastructure for a Billion Electronic Records (CI-BER)


� National Aeronautic & Space Administration (NASA) - Earth Science Data and Information System (ESDIS)- Global Earth Observation System of Systems (GEOSS)- Planetary Data System (PDS)- Multimission Archive at Space Telescope Science Institute (MAST)

� National Endowment for the Humanities (NEH)- Digging into Data Challenge

� National Institute of Health (NIH)- The Cancer Imaging Archives (TCIA)- Neuroimaging Informatics Tools and Resource Clearinghouse

(NITRC)- Neuroscience Information Framework (NIF)- Structural Genomics Initiative - WorldWide Protein Data Bank (wwPDB)- Biomedical Informatics Research Network (BIRN)- Collaborative Research in Computational Neuroscience (CRCNS)


� National Science Foundation (NSF) - Core Techniques and Technologies for Advancing Big Data

Science & Engineering- Cyberinfrastructure Framework for 21st Century Science &

Engineering (CIF21)- Data and Software Preservation for Open Science (DASPOS )- Computational and Data-enabled Science and Engineering

(CDS&E) in Mathematical and Statistical Science (CDS&E-MSS)- Open Science Grid (OSG)- Theoretical and Computational Astrophysics Networks (TCAN)

� National Security Agency (NSA)- Vigilant Net: A Competition to Foster and Test Cyber Defense

Situational Awareness at Scale- NSA/CSS Commercial Solutions Center (NCSC)

� United States Geological Survey (USGS) - John Wesley Powell Center for Analysis and Synthesis

The NITRD Program’s focus:

�Big Data (BD)

�Cyber Physical Systems (CPS)

�Cyber Security and Information Assurance (CSIA)

�Health Information Technology R & D (Health IT R&D)

�Human Computer Interaction and Information Management (HCI&IM)


The NITRD Program’s focus – cont’d:

�High Confidence Software and Systems (HCSS)

�High End Computing (HEC)

� Large Scale Networking (LSN)

�Software Design and Productivity (SDP)

�Social, Economic, and Welfare Implication of IT and IT Workforce Development (SEW)

�Wireless Spectrum Research and Development (WSRD


NSF-NIH Big Data Initiative

� Eight (8) fundamental research projects o Big Data were announced on October 3, 2012

� Typically, one to three investigators per project.

� Total of $15 millions, so about $500k/project

1. “Eliminating the Data Ingestion Bottleneck in Big-Data Application,”

M. Farach-Colton (Rutgers) and M. Bendor (Stony Brook)

2. “DataBridge- A Sociometric System for Long-Tail Science Data Collection,” A. Rajaesekar (Univ. of N.C.), G. King (Harvard) and Justin Zhan (NC Agricultura & Tech State Univ.)

3. “A Formal Foundation for Big Data Management,” D. Suciu (Univ. of Washington).


4. “Analytical Approaches to Massive Data Computation with Applications to Genomics,” E. Upfal (Brown)

5. “Distribution-based Machine Learning for High-dimensional Datasets,” A. Singh (CMU)

6. GenomesGlore- Core Techniques, Libraries, and Domain Specific Languages for High-Throughput DNA Sequencing,” S. Aluru (Iowa State) O. Olukotun (Stanford) and W. Feng (Virginia Tech.)

7. “Big Tensor Mining: Theory, Scalable Algorithms and Applications,” C. Faloutos (CMU) N. Sidiropoulos (U. of Minnesota)

8. Discovery and Social Analytics for Large-Scale Scientific Literature,” P. Kantor, T. Joachims (Cornell) and D. Biei (Princeton)



Source: - Big Data at London Summer Games 2012:http://www.cloudtweaks.com/web/content//big-data-infographic1.jpg


Source: - How much data is generated Every Minute:http://blogs-images.forbes.com/davefeinleib/files/2012/07/Big-Data-Infographic.jpg


Source: Facts about Twitter: http://blog.sironaconsulting.com/.a/6a00d8341c761a5 3ef016767bafa2c970b-pi


Source: Info Graphic Healthcare IT: http://www.healthcareitconnect.com/wp-content/uploads/2012/10/Infographic-BIG-DATA.jpg

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