Database Laboratory TaeHoon Kim

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Database Laboratory Regular Seminar TaeHoon Kim

/18 Contents 1.Introduction 2.Traditional Mobility Services 3.Emerging Spatial Big Data 4.New Challenges 5.Conclusions

/18 Introduction  Mobility is efficient, safe and affordable travel In our 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. 6

/18 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 7

/18 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 8

/18 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 9

/18 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 10

/18 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 11

/18 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 12 Figure3

/18 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 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 13 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

/18 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 14

/18 New Challenges  1 st : It requires a change in frame of reference, moving from a global snapshot perspective to the perspective the individual object traveling through road network 15 Time D1 : 20 D2 : 30 D1 : 20 D2 : 30 D1 : 10 D2 : 20 D1 : 20 D2 : 30 D1 : 20 D2 : 10 Time

/18 New Challenges  2 nd : SBD increases computational cost because it magnifies the impact of the partial nature of the traditional 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  3 rd : 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-time for a road-segment for a given start-time in a week 16

/18 New Challenges  4 th : Use of geospatial reasoning and SBD in sensing and inference across space and time  5 th : 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  6 th : SBD can also be used to make predications the future path of a hurricane 17

/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 the traditional route query specification 3th : Assumption that a single algorithm utilizing a specific dataset is appropriate for all solution 18