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Representing Uncertainty, Profile and Movement History in Mobile Objects Databases

Representing Uncertainty, Profile and Movement History in Mobile Objects Databases. Authors: Eduardo Nóbrega Valeria Times José Rolim. Schedule. Introduction Basic Concepts Related Work The Data model Obtained Results Conclusions Contributions Future Work.

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Representing Uncertainty, Profile and Movement History in Mobile Objects Databases

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  1. Representing Uncertainty, Profile and Movement History in Mobile Objects Databases Authors: Eduardo Nóbrega Valeria Times José Rolim

  2. Schedule • Introduction • Basic Concepts • Related Work • The Data model • Obtained Results • Conclusions • Contributions • Future Work Eduardo Nóbrega, Valeria Times and José Rolim

  3. Introduction • Advance of Technology • Portable devices • Display resolution, troughput, storage, reduction of the dimensions, weight and energy consumption. • Wireless networks • Bandwith, Infrastructure more efficient (cellular nets – GSM and CDMA, satellite communication). Eduardo Nóbrega, Valeria Times and José Rolim

  4. Introduction • Examples of Mobile applications: • Real time applications sensible to location (e.g. Fleet management, tracking of trains and aircrafts, ships monitoring); • Applications of Geographic Information System involving time (e.g. control of regions evolutions). Eduardo Nóbrega, Valeria Times and José Rolim

  5. Introduction • Location Based Retrieval • Continuous Movement • Distrubuted data Updates Eduardo Nóbrega, Valeria Times and José Rolim

  6. Motivation • Research have been done to solve the mobile objects problems. • Continuous updates • Data model design • Spatial operators Eduardo Nóbrega, Valeria Times and José Rolim

  7. Basic Concepts • Movement profiles • Dynamic attributes • Deviation • Uncertainty Eduardo Nóbrega, Valeria Times and José Rolim

  8. Basic Concepts Eduardo Nóbrega, Valeria Times and José Rolim

  9. Related Work Eduardo Nóbrega, Valeria Times and José Rolim

  10. The Data Model • Is divided in two modules: • Client Module • Deals with the location capture, calculates the uncertainty and the deviation, and makes the location prediction; • Server Module • Location prediction and stores the movement profile of all mobile objects; Eduardo Nóbrega, Valeria Times and José Rolim

  11. The Data Model Eduardo Nóbrega, Valeria Times and José Rolim

  12. The Data Model Eduardo Nóbrega, Valeria Times and José Rolim

  13. The Data Model • Basic Classes Eduardo Nóbrega, Valeria Times and José Rolim

  14. The Data Model Eduardo Nóbrega, Valeria Times and José Rolim

  15. The Data Model Eduardo Nóbrega, Valeria Times and José Rolim

  16. Obtained Results • Movement Simulator Eduardo Nóbrega, Valeria Times and José Rolim

  17. Obtained Results • An example of a input file given to the movement simulator : • <Position X>, <Position Y>, <Band width>, <Availability>, <Processor usage>, <Time for the next reading> 1: 275,270,256,133,60,1100 2: 276,270,256,133,60,1700 3: 277,270,256,133,60,3200 4: 278,270,256,133,60,1500 5: 279,270,256,133,65,2900 6: 280,272,256,133,62,1500 Eduardo Nóbrega, Valeria Times and José Rolim

  18. Obtained Results • Arco Verde-Pesqueira Route Eduardo Nóbrega, Valeria Times and José Rolim

  19. Speed X Time Unit 5 4 3 Speed 2 1 0 0 200 400 600 800 1000 Time Unit Obtained Results • Speed Variation by Time Unit Eduardo Nóbrega, Valeria Times and José Rolim

  20. Deviation X Time Unit 8 7 6 5 4 Deviation 3 2 1 0 0 200 400 600 800 1000 Time Unit Obtained Results • Deviation variation by Time Unit Eduardo Nóbrega, Valeria Times and José Rolim

  21. Uncertainty X Time Unit 6 5 4 3 Uncertainty 2 1 0 0 200 400 600 800 1000 Time Unit Obtained Results • Uncertainty Variation by Time Unit Eduardo Nóbrega, Valeria Times and José Rolim

  22. Predicted distance X Real distance 600 500 400 Distance Real Distance 300 Predicted Distance 200 100 0 0 200 400 600 800 1000 Time Unit Obtained Results • Comparison between the predicted distance and the real distance. Eduardo Nóbrega, Valeria Times and José Rolim

  23. Contributions • A conceptual data model for moving objects has been developed: • Position prediction is done based on history movement • Modeling of trajectory uncertainty is done based on deviation calculation • A prototype has been implemented: • For the developed application, the obtained predicted distances were very similar to the real distances Eduardo Nóbrega, Valeria Times and José Rolim

  24. Conclusions • The major problem of mobile objects database is to deal with the continuous movements. • To deals with the object movements the ADR was used Eduardo Nóbrega, Valeria Times and José Rolim

  25. Future Work • Add spatial operators to the proposed data model • Add the profile and movement history to the prototype to improve the movement prediction Eduardo Nóbrega, Valeria Times and José Rolim

  26. The End Eduardo Nóbrega, Valeria Times and José Rolim

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