1 Spatial Big Data Challenges Intersecting Cloud Computing and Mobility Shashi Shekhar McKnight Distinguished University Professor Department of Computer Science and Engineering University of Minnesota

2 Spatial Databases: Representative Projects only in old plan Only in new plan In both plans Evacutation Route Planning Parallelize Range Queries Storing graphs in disk blocksShortest Paths

3 Why cloud computing for spatial data? Geospatial Intelligence [ Dr. M. Pagels, DARPA, 2006] Estimated at 140 terabytes per day, 150 peta-bytes annually Annual volume is 150x historical content of the entire internet Analyze daily data as well as historical data

4 Eco-Routing 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.

5 Real-time and Historic Travel-time, Fuel Consumption, GPS Tracks 5

6 Eco-Routng Research Challenges Frames of Reference –Absolute to moving object based (Lagrangian) Data model of lagrangian graphs –Conceptual – generalize time-expanded graph –Logical – Lagrangian abstract data types –Physical – clustering, index, Lagrangian routing algorithms Flexible Architecture –Allow inclusion of new algorithms, e.g., gps-track mining –Merge solutions from different algorithms Geo-sensing of events, –e.g., volunteered geographic information (e.g., open street map), –social unrest (Ushahidi), flash-mob, … Geo-Prediction, –e.g., predict track of a hurricane or a vehicle –Challenges: auto-correlation, non-stationarity Geo-privacy

7 Cloud Computing and Spatial Big Data Motivation Case Study 1: Simpler to Parallelize Case Study 2 – Harder Case Study 3 – Hardest Wrap up

8 Simpler: Land-cover Classification Multiscale Multigranular Image Classification into land-cover categories Inputs Output at 2 Scales

9 Parallelization Choice 1. Initialize parameters and memory 2. for each Spatial Scale 3. for each Quad 4. for each Class 5. Calculate Quality Measure 6 end for Class 7. end for Quad 8. end for Spatial Scale 9. Post-processing Input 64 x 64 image (Plymouth County, MA) 4 classes (All, Woodland, Vegetated, Suburban) LanguageUPC Platform Cray X1, 1-8 processors)

10 Harder: Parallelizing Vector GIS ( 1/30) second Response time constraint on Range Query Parallel processing necessary since best sequential computer cannot meet requirement Blue rectangle = a range query, Polygon colors shows processor assignment Set of Polygons Display Graphics Engine Local Terrain Database Remote Terrain Databases 30 Hz. View Graphics 2Hz. 8Km X 8Km Bounding Box High Performance GIS Component 25 Km X 25 Km Bounding Box

11 Data-Partitioning Approach Initial Static Partitioning Run-Time dynamic load-balancing (DLB) Platforms: Cray T3D (Distributed), SGI Challenge (Shared Memory)

12 DLB Pool-Size Choice is Challenging!

13 Hardest – Location Prediction Nest locations Distance to open water Vegetation durability Water depth

14 Ex. 3: Hardest to Parallelize Maximum Likelihood Estimation Need cloud computing to scale up to large spatial dataset. However, computing determinant of large matrix is an open problem!

15 Cloud Computing and Spatial Big Data Motivation: Spatial Big Data in National Security & Eco-routing Case Study 1: Simpler to Parallelize –Map-reduce is okay –Should it provide spatial declustering services? –Can query-compiler generate map-reduce parallel code? Case Study 2 – Harder –Need dynamic load balancing beyond map-reduce Case Study 3 – Hardest –Need new computer science, e.g., Eco-routing algorithms determinant of large matrix Parallel formulation of evacuation route planning

16 Acknowledgments HPC Resources, Research Grants –Army High Performance Computing Research Center-AHPCRC –Minnesota Supercomputing Institute - MSI Spatial Database Group Members –Mete Celik, Sanjay Chawla, Vijay Gandhi, Betsy George, James Kang, Baris M. Kazar, QingSong Lu, Sangho Kim, Sivakumar Ravada USDOD –Douglas Chubb, Greg Turner, Dale Shires, Jim Shine, Jim Rodgers –Richard Welsh (NCS, AHPCRC), Greg Smith Academic Colleagues –Vipin Kumar –Kelley Pace, James LeSage –Junchang Ju, Eric D. Kolaczyk, Sucharita Gopal