1

Spatial Big Data ChallengesIntersecting Cloud Computing and Mobility

Shashi Shekhar

McKnight Distinguished University ProfessorDepartment of Computer Science and Engineering

University of Minnesota

www.cs.umn.edu/~shekhar

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 M. Pagels, DARPA, 2006]• Estimated at 140 terabytes per day, 150 peta-bytes annually • Annual volume is 150x historical content of the entire internet• Analyze daily data as well as historical data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 Real-time and Historic Travel-time, and Historic Travel-time, Fuel ConsumptionFuel Consumption, , GPS GPS TracksTracks

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

)(2)|()(

)},({maxargˆ

MpenaltyMnobservatiolikelihoodMquality

whereModelqualityodelMModel

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

0

0.25

0.5

0.75

1

1 2 4 8Number of Processors

Eff

icie

ncy

Class-levelQuad-level

01234567

2 4 8Number of Processors

Spe

edup

Class-levelQuad-level

Input • 64 x 64 image (Plymouth County, MA)• 4 classes (All, Woodland, Vegetated, Suburban)

Language UPC

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 assignmentSet of Polygons

Set of Polygons

DisplayGraphics Engine

Local Terrain Databas

e

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 locationsDistance to open water

Vegetation durability Water depth

14

Ex. 3: Hardest to Parallelize

Name Model

Classical Linear Regression

Spatial Auto-Regression

εx βy

εxβWyy ρ

framework spatialover matrix odneighborho -by- :

parameter n)correlatio-(auto regression-auto spatial the:

nnW

• Maximum Likelihood Estimation

• Need cloud computing to scale up to large spatial dataset.• However, computing determinant of large matrix is an open problem!

SSEnn

L 2

)ln(

2

)2ln(ln)ln(

2WI

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