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Database Laboratory 2013-10-07 TaeHoon Kim

Work progress. Database Laboratory 2013-10-07 TaeHoon Kim. Work Progress. Work Progress. 1111 **** **** 1110 **** **** 1100 **** ****. Spatial Big-Data Challenges Intersecting Mobility And Cloud Computing Shashi Shekhar , Michael R. Evans, Viswanath Gunturi , KwangSoo Yang

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Database Laboratory 2013-10-07 TaeHoon Kim

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  1. Work progress Database Laboratory 2013-10-07 TaeHoon Kim

  2. Work Progress

  3. Work Progress 1111 **** **** 1110 **** **** 1100 **** ****

  4. Spatial Big-Data Challenges Intersecting Mobility And Cloud Computing ShashiShekhar, Michael R. Evans, ViswanathGunturi, KwangSoo Yang Computer Science & Eng. Faculty, University of Minnesota MobiDE '12 Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access Database Laboratory Regular Seminar 2013-10-07 TaeHoon Kim

  5. Contents • Introduction • Traditional Mobility Services • Emerging Spatial Big Data • New Challenges • Conclusions

  6. Introduction • Mobility is efficient, safe and affordable travel • Inour cities, towns and other places of interest • Mobility services • Routing and Navigation • From Google Maps to consumer GPS devices, society has benefited immensely from mobility services and technology • Scientists use GPS to track endangered species to better understand behavior • Farmers use GPS for precision agriculture to increase crop yields while reducing cost • Hiker, biker, taxi driver know precisely where they are, their nearby points of interest, and how to reach their destinations.

  7. Introduction • However, the size, variety, and update rate of mobility data sets exceed the capacity • To learn, manage, and process the data with reasonable effort • Such data is known as Spatial Big Data • We believe that harnessing SBD represents the next generation of mobility services • Examples of emerging SBD dataset include temporally detailed(TD) roadmap • Provide speeds every minute for every road-segment, GPS trace data from cell-phones, engine measurements of fuel consumption, greenhouse gas(GHG) emissions

  8. Introduction • A 2011 McKinsey Global Institute report estimates savings of “about $500 billion annually by 2020”in terms of fuel and time saved by helping vehicles avoid congestion and reduce idling at red lights of left turns

  9. Introduction • However, SBD raise new challenges • 1. It requires a change in frame of reference, moving from a global snapshot perspective to the perspective the individual object traveling through road network • 2. SBD increase the impact of the partial nature of traditional route query specification • 3. The growing diversity of SBD sources makes it less likely that single algorithms, will be sufficient to discover answer appropriate for all situation • Other challenges • Geo-sensing, privacy, prediction, etc

  10. Traditional Mobility Services • Traditional mobility services utilize digital road map • Graph-based • Digital road map • Road intersections are often modeled as vertices • Road segments connecting adjacent intersections are represented as edges in the graph

  11. Traditional Mobility Services • Route determination services, abbreviated as routing services • Best-route determination • Route comparison • The first deals with determination of a best route given • a start location, end location, optional waypoints and preference function(fastest, shortest, easiest, pedestrian, public transportation …) • Route finding is often based on classic shortest path such as Dijktra’s, A*, hierarchical, materialization, other algorithms for static graphs

  12. Emerging Spatial Big Data • Spatio-Temporal Engine Measurement Data • Datasets may include a time-series of attributes such as vehicles(weight, engine size), engine speed • Fuel efficiency can be estimated from fuel levels and distance traveled as well as engine idling from engine RPM Fig3. Heavy truck fuel consumption as a function of elevation from a recent study at Oak Ridge National Laboratory • Explore the potential of this data to help consumers gain similar fuel savings and GHG emission reduction Figure3

  13. Emerging Spatial Big Data • Spatio-Temporal Engine Measurement Data • Problem : These dataset can grow big • Measurements of 10engine variables, once minute, over 100 million US vehicles in existence, may have 1014 data-items per year • GPS Trace Data • GPS trajectories are becoming available for a large collection of vehicles due to the rapid proliferation of cellphones, in-vehicle navigation devices • Make it possible to make personalized route suggestions to users to reduce fuel consumption and GHG emission GPS record taken at 1minute interval, 24 hour day, 7days a week

  14. Emerging Spatial Big Data • Historical Speed Profiles • The profiles have data for every minutes, which can then be applied to the road segment, building up an accurate picture of speeds based on historical data

  15. New Challenges • 1st : It requires a change in frame of reference, moving from a global snapshot perspective to the perspective the individual object traveling through road network Time Time D1 : 20 D1 : 20 D1 : 10 D1 : 20 D2 : 30 D2 : 30 D1 : 20 D2 : 20 D2 : 30 D2 : 10

  16. New Challenges • 2nd : SBD increases computational cost because it magnifies the impact of the partial nature of thetraditional route query specification • For example, traditional routing identifies a unique route(or small set) • but, SBD may identify a much larger set of solution • What is he computational structure of determining routes that minimize fuel consumption and GHG emission? : Eco-routing • 3rd: The tremendous diversity of SBD sources substantially increases the need for diverse solution methods • For example, TD roadmaps cover an entire country, but provide mean travel-timefor a road-segment for a given start-time in a week

  17. New Challenges • 4th : Use of geospatial reasoning and SBD in sensing and inference across space and time • 5th : Privacy of geographic information inside SBDs is an important challenge • While location information can provide great value to users and industry, streams of such data also introduce spooky privacy concerns of stalking and geo-slavery • 6th : SBD can also be used to make predications • the future path of a hurricane

  18. Conclusion • This paper addresses the emerging challenges posed by such datasets, which we call Spatial Big Data, specifically as they apply to mobility services (e.g transportation and routing) • Challenges • 1th : SBD requires a change in frame of reference, moving from a global snapshot perspective to the perspective the individual object traveling through road network • 2th : SBD increases computational cost because it magnifies the impact of the partial nature of thetraditional route query specification • 3th : Assumption that a single algorithm utilizing a specific dataset is appropriate for all solution

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