Rafael Gimenez – Scaling up in a world of geolocated data While the implementation of analytic operations on distributed computing frameworks has been widely describing, enabling the computational core of a Big Data system with capabilities for supporting geospatial querying on data is yet a challenging issue. This session aims to target that specific aspect by reviewing how researchers at BDigital Technology Centre have designed and implemented a stack for advanced Machine Learning on Urban Data by providing a way to geoquery massive amounts of HDFS data from Spark processes without hindering the overall system performance..The geospatial dimension of data is getting revealed as the most natural, powerful and intuitive way to explore the expanding world of data and services. The ability to rely on real-world axis such as places, people, events and things can provide better answers for everyday tasks for individuals, as well as a deep understanding for businesses and administrations.The Urban Data Analytics team at BDigital research efforts are focused on that scenario, with an offering built upon the ability to rapidly deploy pre-defined (but also arbitrary) analytic functions on geospatial time-series of data. Currently available developments already provide support for characterization, classification, clustering, anomaly detection and trajectory mining, while multivariate analytics and predictive functions (both on single and combined time-series) are targeted for the near future.In order to enable such analytic operations on geospatially enabled data, the underlying computational infrastructure must provide the distributed computational processes with a tool to support large-speed and highly dynamic geoquerying operations on massive amounts of data. The combination of end-to-end geoquerying components and GIS enhancements for HDFS data has been implemented and tested by BDigital as the most promising solution for such requirements.

1. Ciudad 2020
Exploratory lines and further work
Joana Simoes, BDigital27/05/2014
Scaling up a in a world of geolocateddata
Rafael Giménez, BDigital Technology Centre

2. Ourteam

3. Ourwork

4. A worldof geolocateddata

5. Applications

8. Spatialdata analytics
Spatialanalysisorspatialstatisticsincludesanyoftheformaltechniqueswhichstudiesentitiesusingtheirtopological,geometricorgeographicproperties

9. Spatialdata analytics
BasicSpatialTypes
Line
Point
Polygon
*plusothers

10. Spatialdata analytics
SpatialTypes

11. Spatialdata analytics
Operations
Contains
Within

12. Spatialdata analytics
Operations
Equals
Disjoint

13. Spatialdata analytics
Operations
Crosses
Touches
Overlaps

14. Spatialdata analytics
Operations
Distance

15. Spatialdata analytics
Operations
Union
Difference

16. Spatialdata analytics
Operations
Intersection
Symmetric Diff

17. Spatialdata analytics
Operations
Buffering
Convex Hull

18. Spatialdata analytics
Operations
Area

19. Spatialdata analytics
Geospatialdataminingtechniques
Density-basedclustering
Hierarchy-basedclustering

20. Spatialdata analytics
Geospatialdataminingtechniques
Heatmaps

21. Spatialdata analytics
Geospatialdataminingtechniques
Trajectorymining

22. Spatialdata analytics
Geospatialdataminingtechniques
Geospatial querying
Spatial anomalies
Geographically weighted regressions
Self-organizing Maps
Agent-based modeling

23. Our Use Cases
Creditcardoperations
Where does peoplebuy?
What is the profile of the customer?
Transit
Could you predict the traffic status?
Could you predict the traffic incidences?
Spatial data analytics

24. Our Datasets
Twitter (Point)
Meteo (Polygon)
Traffic status (Multi-Line-String)
Traffic incidence (Multi-Point)
Cards (Multi-Point)
Demographic (Point & Polygon)
Shapefiles (Multi-Polygon)
Spatial data analytics

25. Our Tools
Queries
Clustering
Heat maps
Trajectory
Anomaly detection
Spatial data analytics

26. Our Stacks

27. Old School Stack -Why Not?
PostGIS
Hadoop + GIS Tools
ElasticSearch
Kibana
Spatial data analytics

29. Hadoop Stack -The new Classic
PostGIS
Hadoop + GIS Tools
ElasticSearch
Kibana
Spatial data analytics

31. Complete Stack -Big Data GIS
PostGIS
Hadoop + GIS Tools
ElasticSearch
Kibana
Spatial data analytics

33. Do’s and Don’ts
Geo Batch
NearReal-Time
Basic Geo Operations
Workflow Composition
Geo Streams
Real-Time
Advanced Geo Op.
Unattended Workflow
Spatial data analytics

34. What’s
next?

35. RFC ;)
Spatial data analytics

36. Moltesgràcies! nom@bdigital.org
rgimenez@bdigital.org
@techisthenewpop@bdigital