Big Data:Opportunity & ChallengesKuncoro WastuwibowoChair, IEEE Indonesia Section

Jakarta, 15 October 2014

The IEEE

IEEE is the world's largest professional association dedicated to advancing technological innovation and excellence for the benefit of humanity. IEEE and its members inspire a global community through IEEE's highly cited publications, conferences, technology standards, and professional and educational activities.

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2,195 Chapters that unite local members with similar technical interests;

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MEMBERS

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MEMBERS

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PSPB IEEE-USA

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The Internet & Big Data

The “Future” InternetInternet

2.0Internet

3.0

Total Experience Services

Context-aware Applications

Internet of Things

Big Data & Analytics

Smart Applications

Wisdom of Crowds

Mash-up Applications

User-Generated Content

Big Data: 3V

“”BIG

DATA

VOLUME VELOCITY VARIETY

VERACITY?????

VALUE?????

VISIBILITY?????

Beyond Data Intelligence

“Business Intelligenc

e”

Data Discovery

Things We Know

Things We Don’t Know

Questions We Ask

Questions We Don’t Ask

How? Map Reduce

Some Big Data ApplicationsScience & Technology• Search for Extra-

Terrestrial Intelligence (SETI)

• Genome projects• CERN’s Large Hadron

Collider• The Square Kilometer

Telescope Array• Social Genome

Business & Community• Consolidation of

government data holdings

• Consumer profile databases

• Customer relationship management

• Social Media• Sensor data

Government-funded Big Data Projects

Big Data in ICT Industry

Ubiquitous Internet: Information Flow

Sensing Data

Cyberspace

Physical Space

Actionable Information

UbiquitousInternet

Data Sensing

SOFT

WAR

E AG

ENT

Web, Social Media

RFID, NFC

Wearable

ICT Implants

ICT IMPLANTS

Human

BIOMETRICS

Human Communication

s

AFFE

CTIV

E CO

MPU

TING

Things NANOTECHNO

LOGY

Things SENSORS

Context: Data for Better Understanding

Device Provider

3rd Party Context Enabler

Context Provider

Service Platform Provider

Content Provider

User Network Provider

Service Provider

Platform Provider

Context Platform

Context Data

Context Data

Context Data

Context

Context-Aware Services

Context Mining: Google Case

Google Search• User’s interest• User’s behaviour

Google Mail• User’s profile• User’s behaviour &

schedule• User’s relation• (Even for Mac & iOS

users)

Google+• User’s relation• User’s behaviour

Google Maps• User’s location• User’s interest

Youtube• User’s interest• User’s network

capacity

Chrome• User’s behaviour• User’s interest

• (Even for Mac users)

Android• User’s device• User’s location• User’s behaviour• User’s application

usage

Google Docs• User’s business• User’s schedule

Google Translate• User’s business• User’s interest

Google Playstore• Guess it!

Google Scholar• Guess it!

Google Drive• Guess it!

Apple vs Google: War of ContextGoogle• Google Search• Google Now• Google Glass

Apple• Siri• Apple Watch• iPhoto

(autometically managed with time, location, event)

• Health

Network Architecture for Big Data

Impact to Network Architecture

Approaches for Internet Backbone

Approaches for Data Center Networks

Starting Small:Big Data for Small

Organisations

Key Investment Area

Successful Startups

MOBILE SERVICES

BIG DATA SOCIAL MEDIA

CLOUD COMPUTING

Small Companies & Big AnalyticsTarget customers by capturing useful information about them

Identify more effective product promotions and create offers targeted to specific customers.

Predict the risk of each customer going to other companies and then identify actions likely to keep them loyal.

Improve efficiency by reducing unused capacity or unnecessary duplication

WHY?

Organizations leveraging analytics will have a greater competitive advantage. Those that don’t will lag behind their peers.

Customer Advising Framework

Big data analytics is valuable to many companies but has been too complex and expensive for smaller businesses. This is beginning to change.

Suvola• Integrated

systems using hardware and software from selected vendors to allow smaller organizations buy simpler, more affordable all-in-one systems for which the seller provides maintenance and support.

Big Vendors• IBM• Oracle• SAP• SAS

Start-ups• QlickTech• Tableau

Software• Tidemark

Software-based Big Data Analytics

Cloud-based Big Data Analytics

UptimeSotwar

e

Continuuity

Right Scale

Amazon

Cloudyn

Cloud Vertical

Newvem

BigML

Insights One

Splunk Storm

Rack space

Cloudability

Impacts to Society

The Internet will enhance global connectivity, fostering more planetary relationships and less ignorance.

The IoT, artificial intelligence, and big data will provide more awareness of the world and our own behavior.

Augmented reality and wearable devices will monitor and give quick feedback on daily life (for example, to enhance personal health).

Political awareness and action will be facilitated. More peaceful change and public uprisings will emerge.

Internet will diminish the meaning of borders, and new “nations” of those with shared interests may emerge.

More Hopeful Theses

Dangerous divides between haves and have-nots may expand, resulting in resentment and possible violence.

Abuses and abusers will “evolve and scale.” Human nature isn’t changing; laziness, bullying, stalking, stupidity, pornography, dirty tricks, and crime will continue, and those who practice them have new capacity to make life miserable for others.

Pressured by these changes, governments and corporations will try to assert power—and at times succeed—as they invoke security and cultural norms.

People will continue—sometimes grudgingly—to make tradeoffs, favoring convenience and perceived immediate gains over privacy. Privacy will become something only the upscale enjoy.

Humans and their current organizations may not respond quickly enough to challenges presented by complex networks.

Most people haven’t yet noticed the profound changes today’s communications networks are already bringing about; these networks will be even more disruptive in the future.

Less Hopeful Theses

Encryption isn’t a perfect solution for securing big data, but it could be a valuable component in a comprehensive privacy solution.

Third parties would create various privacy profiles for consumers who would then select a profile such that data holders would be required to differentiate the way they use data based on each consumer’s selection.

Anonymisation and de-identification have limited relevance because data points linked to one another tend to take on other identifiable attributes.

Deletion and non-retention policies aren’t effective means of protecting individual privacy.

PCAST Reports on Privacy

The focus should be on the actual uses of big data and not so much on its collection and analysis.

To avoid obsolescence, policies and regulations shouldbe stated in terms of intended outcomes and not embed particular technological solutions.

The Government should strengthen its research in privacy-related technologies.

There should be more education and training opportunities concerning privacy protection.

The Government should take the lead by adopting policies that stimulate the use of practical privacy-protecting technologies that exist today.

PCAST Recommendation

Thank YouKuncoro WastuwibowoChairIEEE Indonesia Section

info@IEEE.web.idhttp://IEEE.web.id@IEEEIndonesia

Let’s discussIEEE Indonesia Section

info@IEEE.web.idhttp://IEEE.web.id@IEEEIndonesia

Brian M. Gaff, Heather Egan Sussman, Jennifer Geetter, Privacy and Big Data, IEEE Computer, June 2014

George F. Hurlburt, Jeffrey Voas, Big Data, Networked Worlds, IEEE Computer, April 2014

Jason Kolb & Jeremy Kolb, The Big Data Revolution, Applied Data Labs Inc 2013.Neal Leavitt, Bringing Big Analytics to the Masses, IEEE Computer, January

2013Niklas Elmqvist & Pourang Irani, Ubiquitous Analytics: Interacting with Big

Data Anywhere, Anytime, IEEE Computer, April 2013Pew Research Center, Digital Life in 2025, March 2014President’s Council of Advisors on Science and Technology, Report to the

President: Big Data and Privacy – A Technological Perspective, May 2014.Xiaomeng Yi, Fangming Liu, Jiangchuan Liu, and Hai Jin, Building a Network

Highway for Big Data: Architecture and Challenges, IEEE Network, July/August 2014

Yin Zhang, Min Chen, Shiwen Mao, Long Hu, and Victor C. M. Leung, CAP: Community Activity Prediction Based on Big Data Analysis, IEEE Network, July/August 2014

References