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Web-Based LBMS

Proximity Generation for Location-Based Mobile Applications “ . . . meanwhile, back at the server.” Jim Wyse Canadian Information Processing Society NL, June 2012 Wireless Communications and Mobile Computing Research Centre (WCMCRC), Faculty of Engineering and Applied Science, Memorial University.

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Web-Based LBMS

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  1. Proximity Generation for Location-Based Mobile Applications “ . . . meanwhile, back at the server.”Jim WyseCanadian Information Processing Society NL, June 2012Wireless Communications and Mobile Computing Research Centre (WCMCRC), Faculty of Engineering and Applied Science, Memorial University

  2. Web-Based LBMS

  3. Mobile Business • transactions through communication channels that permit a high degree of mobility by at least one of the transactional parties.

  4. Location-Based m-Business • m-business with location-referent transactions: transactions in which the geographical proximity of the transactional parties is a material transactional consideration. • Critical technological capability: location awareness.

  5. Location-Awareness The capability to obtain and use the geo-positions of the transactional parties to perform one or more of the CRUD (create, retrieve, update, delete) functions of data management.

  6. The Data Management Problem • Location-referent transactions are supported by proximity queries: What is my proximity to a goods-providing (or service-offering) location in a specified category? • A proximity query bears criteria that reference static attributes (e.g., hospital) and dynamic attributes (e.g., nearest). • Proximity queries are burdensome to servers using conventional query resolution approaches

  7. Proximity Generation – An Example The Client-Based i-DAR Prototype (Architecture: Client-Based Functionality, Server-Based Locations Repository)

  8. Web-Based i-Prox Prototype (Architecture: Functionality and Locations Repository are both Server-Based)

  9. i-Prox Tracking GPS

  10. Other Proximity Generators Weblocal Yellow Pages foursquare GEOS IERC WiGLE

  11. Selected i-Prox Implementations 1: Small Craft Harbours(Marine Services) 2: Smart Bay(Real-time Weather Conditions, etc.) 3: Public Libraries(Free Wireless Internet) 4: Avalon Accomodations(Small Inns, B&Bs) 5: Town of Placentia

  12. Small Craft Harbours

  13. Selected i-Prox Implementations 1: Small Craft Harbours(Marine Services) 2: Smart Bay(Real-time Weather Conditions, etc.) 3: Public Libraries(Free Wireless Internet) 4: Avalon Accomodations(Small Inns, B&Bs) 5: Town of Placentia

  14. Selected i-Prox Implementations 1: Small Craft Harbours(Marine Services) 2: Smart Bay(Real-time Weather Conditions, etc.) 3: Public Libraries(Free Wireless Internet) 4: Avalon Accomodations(Small Inns, B&Bs) 5: Town of Placentia

  15. Selected i-Prox Implementations 1: Small Craft Harbours(Marine Services) 2: Smart Bay(Real-time Weather Conditions, etc.) 3: Public Libraries(Free Wireless Internet) 4: Avalon Accomodations(Small Inns, B&Bs) 5: Town of Placentia

  16. Selected i-Prox Implementations 1: Small Craft Harbours(Marine Services) 2: Smart Bay(Real-time Weather Conditions, etc.) 3: Public Libraries(Free Wireless Internet) 4: Avalon Accomodations(Small Inns, B&Bs) 5: Town of Placentia

  17. Under the Hood . . . meanwhile, back at the server

  18. Locations Server and Repository

  19. Conventional ‘Enumerative’ Methods Select locations in targeted business category. Calculate user-relative distances to selected locations. Sort selected locations by user-relative distance. Populate the user’s proximity with the ‘k’ nearest locations. Variations: (1) B, C, D, and then A; (2) Range-based selection Methods from Computational Geometry: Chevaz et al. (2001), Gaede and Guther (1998).

  20. The Problem (. . . and a Solution?)

  21. Linkcell TransformationGeographical Space  Relational Space

  22. Location-Aware Linkcell Method • Transforms mu’s position (47.523° N, 119.137° W) into a linkcell (N47W119). • Initiates a search spiral pivoting clockwise around mu’s linkcell: {N48W119, N48W118, N47W118, N46W118, N46W119, N46W120, N47W120, N48W120, …} • Permits large numbers of locations to be excluded as proximity portal candidates. • Requires an appropriate linkcell ‘size’ (S) to give superior performance.

  23. Linkcell Construction Location Li appears in relational table named for X  ‘N’[SL + 3*S]‘W’[EL + 2*S] For SL of 20°N, EL of 050°W, and S of 1°, we get: Relational Table for Li: N[20+3*1]W[50+2*1] = N23W052

  24. Proximity Generation: Performance Query Resolution Time (ms) Linkcell Size (S)

  25. Linkcell Performance Analyzer (LPA)

  26. S for Optimal Performance?

  27. Optimal Linkcell Size, S ‘Brute Force’ or Solve …. P(S) = 1 – (1 – S2/4A)N 0.6 . . . (A) . . . . for relational table name increments: ‘ N’[SL + 3*S]‘W’[EL + 2*S] = (for ex. N23W052) N is total number of locations, and CS is the number of linkcells of size, S, created from the N locations.

  28. Locations Repository: Scenario A

  29. Locations Repository: Scenario B

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