Ciudad 2020 
Exploratory lines and further work 
Joana Simoes, BDigital27/05/2014 
Scaling up a in a world of geolocatedda...
Ourteam
Ourwork
A worldof geolocateddata
Applications
Spatialdata analytics 
Spatialanalysisorspatialstatisticsincludesanyoftheformaltechniqueswhichstudiesentitiesusingtheirtop...
Spatialdata analytics 
BasicSpatialTypes 
Line 
Point 
Polygon 
*plusothers
Spatialdata analytics 
SpatialTypes
Spatialdata analytics 
Operations 
Contains 
Within
Spatialdata analytics 
Operations 
Equals 
Disjoint
Spatialdata analytics 
Operations 
Crosses 
Touches 
Overlaps
Spatialdata analytics 
Operations 
Distance
Spatialdata analytics 
Operations 
Union 
Difference
Spatialdata analytics 
Operations 
Intersection 
Symmetric Diff
Spatialdata analytics 
Operations 
Buffering 
Convex Hull
Spatialdata analytics 
Operations 
Area
Spatialdata analytics 
Geospatialdataminingtechniques 
Density-basedclustering 
Hierarchy-basedclustering
Spatialdata analytics 
Geospatialdataminingtechniques 
Heatmaps
Spatialdata analytics 
Geospatialdataminingtechniques 
Trajectorymining
Spatialdata analytics 
Geospatialdataminingtechniques 
Geospatial querying 
Spatial anomalies 
Geographically weighted reg...
Our Use Cases 
Creditcardoperations 
Where does peoplebuy? 
What is the profile of the customer? 
Transit 
Could you predi...
Our Datasets 
Twitter (Point) 
Meteo (Polygon) 
Traffic status (Multi-Line-String) 
Traffic incidence (Multi-Point) 
Cards...
Our Tools 
Queries 
Clustering 
Heat maps 
Trajectory 
Anomaly detection 
Spatial data analytics
Our Stacks
Old School Stack -Why Not? 
PostGIS 
Hadoop + GIS Tools 
ElasticSearch 
Kibana 
Spatial data analytics
Hadoop Stack -The new Classic 
PostGIS 
Hadoop + GIS Tools 
ElasticSearch 
Kibana 
Spatial data analytics
Complete Stack -Big Data GIS 
PostGIS 
Hadoop + GIS Tools 
ElasticSearch 
Kibana 
Spatial data analytics
Do’s and Don’ts 
Geo Batch 
NearReal-Time 
Basic Geo Operations 
Workflow Composition 
Geo Streams 
Real-Time 
Advanced Ge...
What’s 
next?
RFC ;) 
Spatial data analytics
Moltesgràcies! nom@bdigital.org 
rgimenez@bdigital.org 
@techisthenewpop@bdigital
Rafael Gimenez – Scaling up in a world of geolocated data - NoSQL matters Barcelona 2014
Rafael Gimenez – Scaling up in a world of geolocated data - NoSQL matters Barcelona 2014
Rafael Gimenez – Scaling up in a world of geolocated data - NoSQL matters Barcelona 2014
Rafael Gimenez – Scaling up in a world of geolocated data - NoSQL matters Barcelona 2014
Rafael Gimenez – Scaling up in a world of geolocated data - NoSQL matters Barcelona 2014
Nächste SlideShare
Wird geladen in …5
×

Rafael Gimenez – Scaling up in a world of geolocated data - NoSQL matters Barcelona 2014

992 Aufrufe

Veröffentlicht am

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.

Veröffentlicht in: Daten & Analysen
0 Kommentare
0 Gefällt mir
Statistik
Notizen
  • Als Erste(r) kommentieren

  • Gehören Sie zu den Ersten, denen das gefällt!

Keine Downloads
Aufrufe
Aufrufe insgesamt
992
Auf SlideShare
0
Aus Einbettungen
0
Anzahl an Einbettungen
3
Aktionen
Geteilt
0
Downloads
13
Kommentare
0
Gefällt mir
0
Einbettungen 0
Keine Einbettungen

Keine Notizen für die Folie

Rafael Gimenez – Scaling up in a world of geolocated data - NoSQL matters Barcelona 2014

  1. 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. 2. Ourteam
  3. 3. Ourwork
  4. 4. A worldof geolocateddata
  5. 5. Applications
  6. 6. Spatialdata analytics Spatialanalysisorspatialstatisticsincludesanyoftheformaltechniqueswhichstudiesentitiesusingtheirtopological,geometricorgeographicproperties
  7. 7. Spatialdata analytics BasicSpatialTypes Line Point Polygon *plusothers
  8. 8. Spatialdata analytics SpatialTypes
  9. 9. Spatialdata analytics Operations Contains Within
  10. 10. Spatialdata analytics Operations Equals Disjoint
  11. 11. Spatialdata analytics Operations Crosses Touches Overlaps
  12. 12. Spatialdata analytics Operations Distance
  13. 13. Spatialdata analytics Operations Union Difference
  14. 14. Spatialdata analytics Operations Intersection Symmetric Diff
  15. 15. Spatialdata analytics Operations Buffering Convex Hull
  16. 16. Spatialdata analytics Operations Area
  17. 17. Spatialdata analytics Geospatialdataminingtechniques Density-basedclustering Hierarchy-basedclustering
  18. 18. Spatialdata analytics Geospatialdataminingtechniques Heatmaps
  19. 19. Spatialdata analytics Geospatialdataminingtechniques Trajectorymining
  20. 20. Spatialdata analytics Geospatialdataminingtechniques Geospatial querying Spatial anomalies Geographically weighted regressions Self-organizing Maps Agent-based modeling
  21. 21. 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
  22. 22. 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
  23. 23. Our Tools Queries Clustering Heat maps Trajectory Anomaly detection Spatial data analytics
  24. 24. Our Stacks
  25. 25. Old School Stack -Why Not? PostGIS Hadoop + GIS Tools ElasticSearch Kibana Spatial data analytics
  26. 26. Hadoop Stack -The new Classic PostGIS Hadoop + GIS Tools ElasticSearch Kibana Spatial data analytics
  27. 27. Complete Stack -Big Data GIS PostGIS Hadoop + GIS Tools ElasticSearch Kibana Spatial data analytics
  28. 28. 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
  29. 29. What’s next?
  30. 30. RFC ;) Spatial data analytics
  31. 31. Moltesgràcies! nom@bdigital.org rgimenez@bdigital.org @techisthenewpop@bdigital

×