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Indoor—A New Data Management Frontier Christian S. Jensen, Hua Lu, Bin Yang

Indoor—A New Data Management Frontier Christian S. Jensen, Hua Lu, Bin Yang. Presented by – Aditi Srivastava. Introduction. MOTIVATION : Indoor spaces are unique in their indoor settings (like doors etc.) Euclidean distance doesn’t apply Indoor movement less constrained

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Indoor—A New Data Management Frontier Christian S. Jensen, Hua Lu, Bin Yang

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  1. Indoor—A New Data Management FrontierChristian S. Jensen, Hua Lu, Bin Yang Presented by – Aditi Srivastava

  2. Introduction MOTIVATION : • Indoor spaces are unique in their indoor settings (like doors etc.) • Euclidean distance doesn’t apply • Indoor movement less constrained • GPS positioning unavailable indoors • RFID, Bluetooth etc. used for indoor positioning- rely on proximity analysis- unable to report velocity and exact locations Paper presents graph based model for indoor tracking, approaches for indexing , uncertainty analysis of moving objects

  3. Tracking Indoor Moving Objects • Symbolic Indoor Positioning Online record (deviceID,objectID,t,flag) flag can be ENTER or LEAVE Offline Record (deviceID,objectID,ts,te)

  4. Eg. Reading RFID sequence tags Figure : Preprocessing module

  5. Positioning Device Deployment Graph G=( C,E,∑devices, le) le : E -> 2 ∑devices • Unidirectional Partitioning devices –Eg. reader 21 • Directed Partitioning devices – Eg. Reader 11 & reader 11’ • Presence devices – Eg. Reader 10

  6. Graph model based Indoor Tracking Offline Tracking: Step 1- augment records of form (objectID, deviceID, ts, te) with corr. graph elements (vertices, edges etc.)in time [ts, te] Step2- identify cells object can be in vacant time intervals Step3 - make use of max speed Vmax and reduce the space to smaller regions

  7. Offline tracking contd.

  8. Management of moving Indoor Objects with inherent Uncertainty • Indexing of Moving Objects

  9. Partitioning of Objects helps in having Hashing based object –location indexing technique

  10. Query processing • R tree based indexing can also be done

  11. Uncertainty Analysis of Moving Objects • UR (o,t) is a region that object must be in this region at time t • Uncertainty region of an active object is the activation range of the corresponding device • Uncertainty region of an inactive object is the cells that the object might be in

  12. R1= radius of Maximum Speed Constrained Circle CMSC(0,device16, t) R2= radius of Maximum Speed Constrained Circle CMSC (0,device10, t) {when o.Vmax(tnow-t)<l where l is distance of door d14 from dev10} R3=radius of Maximum Speed Constrained Circle CMSC (0,device10, t) {when o.Vmax(tnow-t)>=l where l is distance of door d14from dev10} R4=o.Vmax(tnow-t-l/o.Vmax)

  13. Conclusion • Indoor spaces are different than outdoor spaces and cannot be modeled on Euclidean distances and spatial networks. Therefore, ‘indoor’ offers new research challenges. Research directions in data management for indoor spaces : • Integration of different type of positioning technologies • Distance aware queries • Enable online prediction of aggregate and individual movements (data mining) • Develop benchmarks for indoor moving object data management and enable comparison of competing techniques

  14. References • Scalable Continuous Range Monitoring of Moving Objects in Symbolic Indoor Space – Jensen et. al. • Graph Model Based In door Tracking- Jensen et. al. • Indexing the trajectories of moving objects in symbolic indoor space- Jensen et. al.

  15. Thank you

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