E CO - R OUTING U SING S PATIAL B IG D ATA routing/files/iii_2012.pdf

O UTLINE OF THE T ALK What is Spatial Big Data ? Examples of Spatial Big Data. GPS traces Temporally Detailed roadmaps Engine measurement data Transformative Potential of Spatial Big Data Broad Challenges raised by Spatial Big Data Model traveler's frame of reference Partial nature of the query. Scalable architecture for spatial big data. Other resources.

W HAT IS S PATIAL B IG D ATA Spatial datasets exceeding capacity of current computing systems To manage, process, or analyze the data with reasonable effort Due to Volume, Velocity, Variety Examples of Spatial Big Data (SBD): GPS traces Temporally detailed roadmaps Engine measurement data e.g. GHG emissions and fuel consumption

T RADITIONAL S PATIAL B IG D ATA : D IGITAL R OADMAPS

E XAMPLES OF S PATIAL B IG D ATA : T EMPORALLY D ETAILED R OADMAPS Temporally Detailed Roadmaps List speed/travel time for several start-times in a typical week Shortest route for a specific start- time. Can compare a route across start- times: best start-time

S PATIAL B IG D ATA : GPS TRACES A ND E NGINE M EASUREMENT D ATA Sources: Mobile devices Smart phones, in car/truck GPS devices, GPS collars etc Use Cases: Tracking, Tracing, Improve service, deter theft Model traveler’s frame of ref. Patterns of Life Eco-routing

O UTLINE OF THE T ALK What is Spatial Big Data ? Examples of Spatial Big Data. GPS traces Temporally Detailed roadmaps Engine measurement data Transformative Potential of Spatial Big Data Broad Challenges raised by Spatial Big Data Model traveler's frame of reference Partial nature of the query Growing diversity of sources Other resources.

T RANSFORMATIVE P OTENTIAL OF S PATIAL B IG D ATA : B USINESSES

Significantly reduce US consumption of petroleum, the dominant source of energy for transportation. Reduce the gap between domestic petroleum consumption and production. Reduce greenhouse gas (GHG) emissions T RANSFORMATIVE P OTENTIAL OF S PATIAL B IG D ATA : S OCIETY AND E NVIRONMENT

T RANSFORMATIVE P OTENTIAL OF SBD: E CO -R OUTING U.P.S. Embraces High-Tech Delivery Methods (July 12, 2007) By “The research at U.P.S. is paying off. ……..— saving roughly three million gallons of fuel in good part by mapping routes that minimize left turns.” Minimize fuel consumption and GPG emission rather than proxies, e.g. distance, travel-time avoid congestion, idling at red-lights, turns and elevation changes, etc. Do you idle at green light during traffic congestion?

O UTLINE OF THE T ALK What is Spatial Big Data ? Examples of Spatial Big Data. GPS traces Temporally Detailed roadmaps Engine measurement data Transformative Potential of Spatial Big Data Broad Challenges raised by Spatial Big Data Model traveler's frame of reference Partial nature of the query Growing diversity of sources Other resources.

SBD C HALLENGE : M ODELING T RAVELER ’ S F RAME OF R EFERENCE GOAL: Candidate routes should be evaluated from the perspective of a person moving through the transportation network. Lagrangian Frame of Reference

SBD C HALLENGE : M ODELING T RAVELER ’ S F RAME OF R EFERENCE Other experiences: Synchronized traffic signals ? Turn delays ? Waiting at traffic signals? Need new models for these experiences. GPS traces obtained from in-car navigation devices may have these already Waiting at signals

M ODELING T RAVELER ’ S F RAME OF R EFERENCE : TASK 1 Task: Exploring Data Representations for Modeling Traveler's Frame of Reference in Routing Queries Goal: Explore the challenges raised while designing a data model for routing queries on TD roadmaps. Ref section 3.1 in proposal References: Erik G. Hoel, Wee-Liang Heng, and Dale Honeycutt. High performance multimodal networks. In Advances in Spatial and Temporal Databases, pages , Springer. LNCS G. Gallo, G. Longo, S. Pallottino, and S. Nguyen. Directed hypergraphs and applications. Elsevier, Discrete applied mathematics, 42(2): , 1993.

M ODELING T RAVELER ’ S F RAME OF R EFERENCE : T ASK 2 A Task: Scalable query processing techniques for route recommendation using GPS traces Goal: explore scalable query processing to make route recommendations from GPS tracks without graph traversal algorithms.. Ref section 3.1 in proposal Constraint: assume there exits a GPS trace between the given source and destination References: Y. Zheng and X. Zhou. Computing with spatial trajectories. Springer, Long-Van Nguyen-Dinh, Walid G. Aref, and Mohamed F. Mokbel. Spatio- temporal access methods: Part 2 ( ). IEEE Data Eng. Bull., 33(2):46--55, Mohamed F. Mokbel, Thanaa M. Ghanem, and Walid G. Aref. Spatio-temporal access methods. IEEE Data Eng. Bull., 26(2):40--49, 2003.

M ODELING T RAVELER ’ S F RAME OF R EFERENCE : T ASK 2 B Task: Scalable query processing techniques for route recommendation using GPS traces Goal: explore scalable query processing to make route recommendations from GPS tracks without graph traversal algorithms.. Ref section 3.1 in proposal Constraint: Assumption in Task 2a is dropped. References: Y. Zheng and X. Zhou. Computing with spatial trajectories. Springer, Long-Van Nguyen-Dinh, Walid G. Aref, and Mohamed F. Mokbel. Spatio- temporal access methods: Part 2 ( ). IEEE Data Eng. Bull., 33(2):46--55, Mohamed F. Mokbel, Thanaa M. Ghanem, and Walid G. Aref. Spatio-temporal access methods. IEEE Data Eng. Bull., 26(2):40--49, 2003.

SBD C HALLENGE : P ARTIAL N ATURE OF T RADITIONAL R OUTING Q UERY SBD magnifies the partial nature of the traditional routing query Traditional routing query: “Find shortest path between source and destination” ref section 3.2 in the proposal Additional questions raised by SBD At what start time? Different routes may be optimal at different start-times Preference metric? Route minimizing fuel and GHG may not shortest! TimePreferred Route 7:30amVia Hiawatha 8:30amVia Hiawatha 9:30amVia 35W 10:30amVia 35W …..

SBD C HALLENGE : S CALABLE A RCHITECTURE FOR S PATIAL B IG D ATA Task: Exploring the a `Distributed Architecture' for Routing Queries on TD roadmaps Goal: The goal of this project is to explore efficient storage systems for TD roadmaps which support a big workload of common queries involving SP- TAG, BEST and CTAS algorithms [1,2] You would need to evaluate the performance of G*[3] for SBD work loads. References: [1] Betsy George, Sangho Kim, Shashi Shekhar: Spatio-temporal Network Databases and Routing Algorithms: A Summary of Results. SSTD 2007: [2] Venkata M. V. Gunturi, Ernesto Nunes, KwangSoo Yang, Shashi Shekhar: A Critical-Time-Point Approach to All-Start-Time Lagrangian Shortest Paths: A Summary of Results. SSTD 2011: [3] G* Dynamic Graph Database:

S OME R ESOURCES GeoLife project from MSR: daa38f2b2e13/ T-Drive project from MSR