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ST-Toolkit: a Framework for Trajectory Data Warehousing Authors AGILE 2011 Utrecht – 20/04/2011 Simone Campora Jose Fernandes De Macedo Laura Spinsanti
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Trajectory Data Warehousing The motivation behind Trajectory Data Warehouses (TrDWs) is to  transform  raw moving objects' trajectories to valuable information that can be exploited for  decision-making  purposes in ubiquitous applications, such as location-based services, traffic control management, etc using an OLAP (or STOLAP) fashion.
Why Trajectory Data Warehousing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Work Spatio-Temporal DBMS Secondo (Güting  et al .) Hermes (Pelekis  et al .) Trajectory Data Warehouses Università di Venezia (Orlando  et al .)
Why Trajectory Data Warehousing Solution Comparison
Our Contribution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Trajectory Extraction
Generic Data Warehouse Schema ,[object Object],[object Object],[object Object],[object Object]
[object Object],Data Warehouse Design Issues Lack of standard interfaces every commercial/academic solution is implementing different approaches to istantiate multi dimensional models into Databases ,[object Object],[object Object],[object Object]
Generic Data Warehouse Architecture
Example: Data Warehouse Design First Step Data are Streamed From Raw Datasets into Primary Memory Second Step Java Objects are Buffered and Istantiated Asychronously Sent to the RDBMS Third Step Java Objects are Persisted into RDBM and properly Indexed Fourth Step The MultiDimensional Model is istantiated from RDBMS data sources + DW Metedata Definitions
Some Experiments ,[object Object],[object Object],[object Object],[object Object],[object Object],What is the role of semantics in query complexity? Features Value Records 2075213 Trajectories 83134 Stops 464584 Moves 1527495 POIs 39776
SOLAP Query ,[object Object],[object Object],int theta = 10; SpatialFilter filter = new DistanceFilter(  eventDimension.getProperty("Event Shape"),  new Point( 45.28,9.12),theta); OlapQuery  query = new OlapQuery();   query.addSelection(stopMeasure,OlapQuery.COLUMNS);   query.addSelection(eventDimension,OlapQuery.ROWS); query.addFilter(filter); query.setCube(stCube); query.execute();
STOLAP Query ,[object Object],[object Object],SpatialFilter filter = new DistanceFilter( eventDimension.getProperty("Event Shape"), stopMeasure, 1); OlapQuery query = new OlapQuery(); query.addSelection(presenceMeasure,OlapQuery.COLUMNS); query.addSelection(eventDimension,OlapQuery.ROWS); query.addFilter(filter); query.addCondition("[Event].[Food Shop]"); query.addCondition("[Trajectory].[Trajectory Group].[Number of Trajectories > 10]"); query.setCube(stCube); query.execute(); N_VISITS OBJET_ID 64640  89754 56055  78796 52015  70702 49995  76930 47470  79088 46460  82085
Presence Measure Validation Presence Measure:  Problem: how to aggregates the number of  trajectories within a hierarchical fully-geometric  dimension avoiding the double-counting problem ?
Some Experiments Presence Measure: understanding the problem 1 1 1 1 0 1 1
Presence Measure Validation Solution : define an aggregation  algorithm that can use  spatial operators!  Our application can define SQL injections for spatial-aggregates : String sqlExpression = "case when get_trj_space_area_intersections(trdw_episode_facts.geom) > 0 then ceil(1/get_trj_space_area_intersections(trdw_episode_facts.geom)) else 0 end "; Measure presence = new VirtualMeasure(“Trj Presence Measure", factTable, “presence", sqlExpression);
Presence Measure Validation Results on 260 Trajectories subset Milano – Arese: 2 Milano – Assago: 2 Milano – Bollate: 1 Milano – Bresso: 2 Milano – Buccinasco: 2 Milano - Cesano Boscone: 6 Milano – Cormano: 2 Milano – Corsico: 2 Milano - Cusano Milanino: 2 Milano – Gaggiano: 2 Milano - Locate di Triulzi: 2 Milano – Milano: 186 Milano – Novate: 2 Milano – Opera: 2 Milano – Pero: 2 Milano - Peschiera Borromeo: 2 Milano – Rho: 14 Milano – Rozzano: 2 Milano - San Donato Milanese: 1 Milano - San Giuliano Milanese: 6 Milano – Segrate: 2 Milano - Settimo Milanese: 8 Milano - Trezzano Rosa: 4 Milano - Zibido San Giacomo: 2 Monza and Brianza – Mezzano: 2 Milano: 258  Monza and Brianza: 2 Lombardia: 260
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Thanks for your attention Any Question? Suggestions? Comments? For more information:  http://st-toolkit.sourceforge.net/   Thanks for the attention

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ST-Toolkit, a Framework for Trajectory Data Warehousing

  • 1. ST-Toolkit: a Framework for Trajectory Data Warehousing Authors AGILE 2011 Utrecht – 20/04/2011 Simone Campora Jose Fernandes De Macedo Laura Spinsanti
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  • 3. Why Trajectory Data Warehousing The motivation behind Trajectory Data Warehouses (TrDWs) is to transform raw moving objects' trajectories to valuable information that can be exploited for decision-making purposes in ubiquitous applications, such as location-based services, traffic control management, etc using an OLAP (or STOLAP) fashion.
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  • 5. Related Work Spatio-Temporal DBMS Secondo (Güting et al .) Hermes (Pelekis et al .) Trajectory Data Warehouses Università di Venezia (Orlando et al .)
  • 6. Why Trajectory Data Warehousing Solution Comparison
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  • 11. Generic Data Warehouse Architecture
  • 12. Example: Data Warehouse Design First Step Data are Streamed From Raw Datasets into Primary Memory Second Step Java Objects are Buffered and Istantiated Asychronously Sent to the RDBMS Third Step Java Objects are Persisted into RDBM and properly Indexed Fourth Step The MultiDimensional Model is istantiated from RDBMS data sources + DW Metedata Definitions
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  • 16. Presence Measure Validation Presence Measure: Problem: how to aggregates the number of trajectories within a hierarchical fully-geometric dimension avoiding the double-counting problem ?
  • 17. Some Experiments Presence Measure: understanding the problem 1 1 1 1 0 1 1
  • 18. Presence Measure Validation Solution : define an aggregation algorithm that can use spatial operators! Our application can define SQL injections for spatial-aggregates : String sqlExpression = "case when get_trj_space_area_intersections(trdw_episode_facts.geom) > 0 then ceil(1/get_trj_space_area_intersections(trdw_episode_facts.geom)) else 0 end "; Measure presence = new VirtualMeasure(“Trj Presence Measure", factTable, “presence", sqlExpression);
  • 19. Presence Measure Validation Results on 260 Trajectories subset Milano – Arese: 2 Milano – Assago: 2 Milano – Bollate: 1 Milano – Bresso: 2 Milano – Buccinasco: 2 Milano - Cesano Boscone: 6 Milano – Cormano: 2 Milano – Corsico: 2 Milano - Cusano Milanino: 2 Milano – Gaggiano: 2 Milano - Locate di Triulzi: 2 Milano – Milano: 186 Milano – Novate: 2 Milano – Opera: 2 Milano – Pero: 2 Milano - Peschiera Borromeo: 2 Milano – Rho: 14 Milano – Rozzano: 2 Milano - San Donato Milanese: 1 Milano - San Giuliano Milanese: 6 Milano – Segrate: 2 Milano - Settimo Milanese: 8 Milano - Trezzano Rosa: 4 Milano - Zibido San Giacomo: 2 Monza and Brianza – Mezzano: 2 Milano: 258 Monza and Brianza: 2 Lombardia: 260
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  • 21. Thanks for your attention Any Question? Suggestions? Comments? For more information: http://st-toolkit.sourceforge.net/ Thanks for the attention