1. Proximity Spider Project by Ganesh Naikare Project Advisor: Professor Scott Spetka

2. Outline Requirement Overview Challenges Existing Solutions Design Comparison Conclusion

3. Requirement Overview Web based application to use vast geospatial information Millions of records associated with geospatial information can be added and processed. Real-time geospatial operation capability and responsive nature of an application Application should be accessible via any computer, laptops, mobile devices.

4. Challenges Growing data size hinders performance in relational database technologies No inbuilt support for geospatial operations. Use of API for geospatial operations add up to the response time of an application and has limitations on their use. Need of database which performs well even with large data set and has inbuilt geospatial operations capability.

5. Existing Solutions Google Maps API – Usage limitations Bigtable by Google (fully-managed cloud NoSQL database service) – Not public until May 2015 Hadoop or Big Data – Good performance for large datasets but still need external solution for Lack of support for Spherical geometry in distance calculations.

6. Design Design Goals 99.99% Availability Easy to use interface accessible via mobile devices as well on mobile devices Fast response time Scalable design Low complexity

7. Project Modules Web Interface Google Maps API (for showing locations using pins on a map) Business Logic module Data access service – To translate programming language MongDB queries Database – Data storage, indexing & Geospatial operation engine.

8. Frameworks Used Spring Framework – Java Application Framework (Inversion of Control) SpringData MongoDB – To convert Criteria queries to MongoDB scripts Bootstrap – Responsive design HTML, CSS and JS Framework

9. Geospatial Operations Logic

10. MongoDB’s 2dSphere Index – Works with GeoJSON objects { type: " ", coordinates: } coordinates in longitude, latitude order. Point LineString Polygon MultiPoint MultiLineString MultiPolygon GeometryCollection Data Type Point - { type: "Point", coordinates: [ 40, 5 ] }

11. Geospatial Operations Logic MongoDB’s 2dSphere Index – Works with GeoJSON objects { type: " ", coordinates: } coordinates in longitude, latitude order. Point LineString Polygon MultiPoint MultiLineString MultiPolygon GeometryCollection Data Type Point - { type: "Point", coordinates: [ 40, 5 ] }

12. Spherical Geometry Support Circle circle = new Circle(point, radiusDistance); Criteria criteria = Criteria.where("location").withinSphere(circle); The above criteria gets translated into following query db.places.find( { loc: { $geoWithin: { $centerSphere: [ [ -74, 40.74 ], 100 / 3963.2 ] } } } ) These three queries use radians for distance distance to radians: divide the distance by the radius of the sphere (e.g. the Earth)

13. Use Cases Searching for a location of particular interest

14. Use Cases Searching for a location of particular interest

15. Use Cases Adding a business/POI on the map

16. Performance Comparison

18. Conclusion Performance gain of NoSQL database increases with increasing data set as compared to relational database, allowing faster access. MongoDB Provides advantage of 2dSphere indexes and geospatial operations over other databases Schema less document database provisions storage of data different size, number, content in same collection. This makes application easily scalable.

19. Thank You Any Questions?