TECHNOLOGIES & CONCEPTSIN BIG DATA

QUANTIFIED SELF, INTERNET OF THINGS, TELEMATICS, AND VIDEO SEARCH

Amer Aljarallah

IDS 594 Selected Topics in Big Data

RetailerW

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hann

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DWM

SCRM

SCM/Logistics

Suppliers

Distrib

ution

Infomediaries

Geo-location

Socia

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ia

Traditional Sources of Data

Social Analytics

Telematics

Cloud Computing

Text Analytics

In-Memory Analytics

Social Media Monitors

Speech Recognition

Predictive Analytics

Internet of Things Logical Data Warehouse

Video Search

Graph Databases

Quantified Self

The Internet of Things

Telematics Video Search

Quantified Self

The General Theme• What is it?

• Current supporting Technologies

• Applications and Examples

• How is it related to Big Data?

• Future/Potentiality

QUANTIFIED SELF

Quantified Self

“Quantified Self is a movement promoting the use of self- monitoring through a wide variety of sensors and devices.”

Wearable Mobile Apps Portable Devices

QS Applications

Focused Categories

• Sports• Body movements• Scales• Activity monitors/trackers

• Health • Vital measurements• Baby monitors

Broad Categories

• Physical activities • Diet • Psychological states and

traits• Mental and cognitive

states and traits• Environmental• Situational• Social

Technology Examples

QS in Big Data

Opportunities

• Data Collection• Health data streams

• Data Integration• Individual & Environmental

data

• Data Analysis• Health warning signals

Challenges

• Practical• Manual• Easiness• Cost

• Mindset• Cultural• Psychological• Sociological

Future of QS• Horizon: 2~5 years to maturity• Penetration: <1%

• Smart Watches• Google, Apple, and Samsung

• Wearable • Clothing Sensors• Monitors

• Others• Carpet• Toilet• Etc.

INTERNET OF THINGS

Internet of Things

“[The] network of physical objects that contain embedded technology to communicate and sense or interact with their

internal states or the external environment.”

Anything that can communicate!

“Ideas and information are important, but things matter much more…”

Kevin Ashton, 2009

Applications (View 1)

Applications (View 2)

Applications (View 3)

Technologies in IoT• Radio-frequency identification (RFID)• Wireless sensor network (WSN)• RFID sensor networks (RSN)• Near field communication (NFC)• Middleware layers

• Intermediary between objects and applications• Data management• Service management• Management of security and access

IoT in Big Data

Opportunities

• Personal• Domotics – home

automation• Assisted living• E-Health

• Business• Automation• Logistics• Business/process

management• Intelligent transportation

Challenges

• Standardization• Naming• Security

• Authentication• Privacy

• Value• Value creation• Cost

Future of IoT• Horizon: 10 years to maturity• Penetration: 1~5%

• Environment Management• Monitoring, optimization, performance assessment

• Remote Operation/Support

• Enhance Life Quality

TELEMATICS

Telematics

“[The] combination of the transmission of information over a telecommunication network and the [computerized]

processing of this information.”

“[The] use of in-car installed and after-factory devices to transmit data in real time back to an organization, including

vehicle use, maintenance requirements, air bag deployment or automotive servicing.

• Platform for usage-based insurance (UBI)• pay-per-use• pay as you drive (PAYD) • pay how you drive (PHYD)

Example

Technologies in Telematics• Wireless communication

• Trunked radio • Cellular communication (GSM, UMTS)• Satellite communication • Dedicated Short Range Communication (DSRC, V2V, V2I)• Broadcasting

• Positioning systems (GPS)• Dead reckoning (position, direction, speed, time, and distance)• Satellite positioning • Cellular communication based positioning • Signpost systems

• Geographical Information Systems (GIS)

Waze Application

Applications

Telematics in Big Data

Opportunities

• Customer preferences• Usage behavior

• Value-added services• Segmentation of customers

based on usage/behavior

• Usage-based insurance• Pay-per-use, PAYD, PHYD

Challenges

• Data collection• Cost/Value• Privacy and Safety

Future of Telematics• Horizon: 5~10 years to maturity• Penetration: 5~20%

• Accurate risk assessment• Recovery of stolen vehicles• Faster claims submittals • Improved roadside assistance• Reduce driver risks

• Telematics can reduce accidents by 30%

VIDEO SEARCH

Video Search

“[The] ability to search within a collection of videos.”

• Audio• Speech recognition• Speech-to-text/Transcription

• Video• Facial/Object recognition

Current Applications• Semantic Video Search

• Search for Concepts• Search for objects: cars,

• Classification

• Content Management

• Rich Media Searchability

Video Search in Big Data

Opportunities

• Plain Search• YouTube, etc.

• Transportation• Surveillance monitors• Surgery analysis• Content Management

(Copyright, Violence, Sexual, …)

Challenges

• Technology• Feature extraction• Non-audio video

Future of Video Search• Horizon: 5~10 years to maturity• Penetration: <1%

• Enterprise Applications• Higher education • Law enforcement • Business products manufacturers • Service organizations • Content Management

GOOGLE TRENDS

Google Trends

Google Trends

Google Trends

References1. Ashton, K. (2009). That ‘Internet of Things’ Thing. RFiD Journal, 22, 97-114.

2. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey.Computer Networks, 54(15), 2787-2805.

3. Chui, M., Löffler, M., & Roberts, R. (2010). The internet of things. McKinsey Quarterly, 2, 1-9.

4. Goel, A. (2008). Fleet telematics [electronic resource]: real-time management and planning of commercial vehicle operations (Vol. 40). Springer.

5. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems.

6. Heudecker, N. (2013). Hype Cycle for Big Data 2013. Gartner Inc., Stamford, CT.

7. Hossain, E., Chow, G., Leung, V., McLeod, R. D., Mišić, J., Wong, V. W., & Yang, O. (2010). Vehicular telematics over heterogeneous wireless networks: A survey. Computer Communications, 33(7), 775-793.

8. Snoek, C., Sande, K., Rooij, O. D., Huurnink, B., Uijlings, J., Liempt, V. M., ... & Smeulders, A. (2009). The MediaMill TRECVID 2009 semantic video search engine. In TRECVID workshop.

9. Swan, M. (2013). The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data, 1(2), 85-99.

10. Tolve, A. (2013) Telematics and the Value of Big Data, Part I. Telematics Update. Web. 26 Nov. 2013.

11. Tolve, A. (2013) Telematics and the Value of Big Data, Part II. Telematics Update. Web. 26 Nov. 2013.

THANK YOU!Q&A