technologies & concepts in big data quantified self, internet of things, telematics, and video...
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TECHNOLOGIES & CONCEPTSIN BIG DATA
QUANTIFIED SELF, INTERNET OF THINGS, TELEMATICS, AND VIDEO SEARCH
Amer Aljarallah
IDS 594 Selected Topics in Big Data
RetailerW
eb C
hann
el
DWM
SCRM
SCM/Logistics
Suppliers
Distrib
ution
Infomediaries
Geo-location
Socia
l Med
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