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TECHNOLOGIES & CONCEPTS IN BIG DATA QUANTIFIED SELF, INTERNET OF THINGS, TELEMATICS, AND VIDEO SEARCH Amer Aljarallah IDS 594 Selected Topics in Big Data.

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Presentation on theme: "TECHNOLOGIES & CONCEPTS IN BIG DATA QUANTIFIED SELF, INTERNET OF THINGS, TELEMATICS, AND VIDEO SEARCH Amer Aljarallah IDS 594 Selected Topics in Big Data."— Presentation transcript:

1 TECHNOLOGIES & CONCEPTS IN BIG DATA QUANTIFIED SELF, INTERNET OF THINGS, TELEMATICS, AND VIDEO SEARCH Amer Aljarallah IDS 594 Selected Topics in Big Data

2 Retailer Web Channel DWMS CRM SCM/Logistics Suppliers Distribution Infomediaries Geo-location Social Media Traditional Sources of Data Social Analytics Telematics Cloud Computing Text Analytics In-Memory Analytics Social Media Monitors Speech Recognition Predictive Analytics Internet of ThingsLogical Data Warehouse Video Search Graph Databases Quantified Self

3 The Internet of Things Telematics Video Search Quantified Self

4

5 The General Theme What is it? Current supporting Technologies Applications and Examples How is it related to Big Data? Future/Potentiality

6 QUANTIFIED SELF

7 Quantified Self Quantified Self is a movement promoting the use of self- monitoring through a wide variety of sensors and devices. WearableMobile AppsPortable Devices

8 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

9 Technology Examples

10 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

11 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.

12 INTERNET OF THINGS

13 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

14 Applications (View 1)

15 Applications (View 2)

16 Applications (View 3)

17 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

18 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

19 Future of IoT Horizon: 10 years to maturity Penetration: 1~5% Environment Management Monitoring, optimization, performance assessment Remote Operation/Support Enhance Life Quality

20 TELEMATICS

21 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)

22 Example

23 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)

24 Waze Application

25 Applications

26 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

27 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%

28 VIDEO SEARCH

29 Video Search [The] ability to search within a collection of videos. Audio Speech recognition Speech-to-text/Transcription Video Facial/Object recognition

30 Current Applications Semantic Video Search Search for Concepts Search for objects: cars, Classification Content Management Rich Media Searchability

31 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

32 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

33 GOOGLE TRENDS

34 Google Trends

35

36

37 References 1. Ashton, K. (2009). That Internet of Things Thing. RFiD Journal, 22, Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey.Computer Networks, 54(15), Chui, M., Löffler, M., & Roberts, R. (2010). The internet of things. McKinsey Quarterly, 2, 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 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), 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), Tolve, A. (2013) Telematics and the Value of Big Data, Part I. Telematics Update. Web. 26 Nov Tolve, A. (2013) Telematics and the Value of Big Data, Part II. Telematics Update. Web. 26 Nov

38 THANK YOU! Q&A


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