proximity generation for location- based mobile applications “... meanwhile, back at the...
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Proximity Generation for Location-Proximity Generation for Location-Based Mobile Applications Based Mobile Applications
“ . . . meanwhile, back at the server.”“ . . . meanwhile, back at the server.”
Jim WyseJim Wyse
Canadian Information Processing Society NL, June 2012Canadian Information Processing Society NL, June 2012
Wireless Communications and Mobile Computing Research Centre (WCMCRC), Faculty of Wireless Communications and Mobile Computing Research Centre (WCMCRC), Faculty of Engineering and Applied Science, Memorial UniversityEngineering and Applied Science, Memorial University
Web-Based LBMSWeb-Based LBMS
Mobile BusinessMobile Business
• transactions through communication channels that permit a high degree of mobility by at least one of the transactional parties.
• 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.
Location-Based Location-Based mm-Business-Business
Location-AwarenessLocation-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.
The Data Management ProblemThe 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
Proximity Generation – An ExampleProximity Generation – An Example
The Client-Based The Client-Based ii-DAR Prototype -DAR Prototype (Architecture: Client-Based Functionality, Server-Based Locations Repository)(Architecture: Client-Based Functionality, Server-Based Locations Repository)
Web-Based Web-Based ii-Prox Prototype-Prox Prototype
(Architecture: Functionality and Locations Repository are both Server-Based)(Architecture: Functionality and Locations Repository are both Server-Based)
i-Prox Tracking GPSi-Prox Tracking GPS
Other Proximity GeneratorsOther Proximity Generators
Weblocal
Yellow Pages
foursquare
GEOS IERC
WiGLE
Selected i-Prox ImplementationsSelected 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
Small Craft HarboursSmall Craft Harbours
Selected i-Prox ImplementationsSelected 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
Selected i-Prox ImplementationsSelected 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
Selected i-Prox ImplementationsSelected 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
Selected i-Prox ImplementationsSelected 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
Under the HoodUnder the Hood
. . . meanwhile, back at the server. . . meanwhile, back at the server
Locations Server and RepositoryLocations Server and Repository
Conventional ‘Enumerative’ MethodsConventional ‘Enumerative’ Methods
A. Select locations in targeted business category.
B. Calculate user-relative distances to selected locations.
C. Sort selected locations by user-relative distance.
D. 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).
The Problem (. . . and a Solution?)The Problem (. . . and a Solution?)
Linkcell TransformationLinkcell TransformationGeographical Space Geographical Space Relational Space Relational Space
Location-Aware Linkcell MethodLocation-Aware Linkcell Method• Transforms Transforms mumu’s’s position (47.523 position (47.523° N, 119.137° W) into a ° N, 119.137° W) into a
linkcell (N47W119).linkcell (N47W119).
• Initiates a Initiates a search spiral search spiral pivoting clockwise around pivoting clockwise around mumu’s ’s linkcell: linkcell: {N48W119, N48W118, N47W118, N46W118, {N48W119, N48W118, N47W118, N46W118, N46W119, N46W120, N47W120, N48W120, …}N46W119, N46W120, N47W120, N48W120, …}
• Permits large numbers of locations to be excluded as Permits large numbers of locations to be excluded as proximity portal candidates.proximity portal candidates.
• Requires an appropriate linkcell ‘size’ (S) to give superior Requires an appropriate linkcell ‘size’ (S) to give superior performance.performance.
Linkcell ConstructionLinkcell Construction
Location LLocation Lii appears in relational table named for X appears in relational table named for X ‘N’[SL + 3*S]‘W’[EL + 2*S] ‘N’[SL + 3*S]‘W’[EL + 2*S]
For SL of 20For SL of 20°°N, EL of 050N, EL of 050°°W, and S of 1W, and S of 1°°, we get:, we get:
Relational Table for LRelational Table for Lii: N[20+3*1]W[50+2*1] = N23W052: N[20+3*1]W[50+2*1] = N23W052
Proximity Generation: PerformanceProximity Generation: Performance
Linkcell Size (S)Linkcell Size (S)
Que
ry R
esol
utio
n T
ime
(ms)
Que
ry R
esol
utio
n T
ime
(ms)
Linkcell Performance Analyzer Linkcell Performance Analyzer (LPA)(LPA)
S for Optimal Performance?S for Optimal Performance?
‘Brute Force’ or Solve ….
P (S) = 1 – (1 – S2/4A)N 0.6 . . . (A)
. . . . for relational table name increments: ‘N’[SL + 3*N’[SL + 3*SS]]‘W’[EL + 2*W’[EL + 2*SS] = (for ex. N23W052)] = (for ex. N23W052)
N is total number of locations, and
CS is the number of linkcells of size, S, created
from the N locations.
Optimal Linkcell Size, SOptimal Linkcell Size, S
Locations Repository: Scenario ALocations Repository: Scenario A
Locations Repository: Scenario BLocations Repository: Scenario B
Four ‘S’ CandidatesFour ‘S’ Candidates
SSPP: : P (S) = 1– (1 – S2/4A)N (Probabilistic)Probabilistic)
SSLL: S = (A/N): S = (A/N)1/21/2 (Equi-Areal) (Equi-Areal)
SSUU: S = 3 (A/N): S = 3 (A/N)1/21/2 (Spiral Avoidance) (Spiral Avoidance)
SSMM: S = 2 (A/N): S = 2 (A/N)1/21/2 (Optimality Interval Median) (Optimality Interval Median)
Proximity Generation PerformanceProximity Generation PerformanceScenario B: 50,000-Location RepositoryScenario B: 50,000-Location Repository
Linkcell Determination Method
Linkcell Size
Proximity Generation
Performance(milliseconds)
SL: Equi-Areal 0.00447 50
SP: Probabilistic 0.00484 48
SM: Opt. Interval Median 0.00894 46
SU: Spiral Avoidance 0.01341 66
Unconstrained Enumerative Method: 121,500 ms (approx. 2 minutes or 2600X) Unconstrained Enumerative Method: 121,500 ms (approx. 2 minutes or 2600X)
Proximity GenerationProximity GenerationRepository Size VariationsRepository Size Variations
Proximity GenerationProximity GenerationAreal Size Variations for 50,000-Location RepositoryAreal Size Variations for 50,000-Location Repository
Proximity GenerationProximity GenerationAreal Size Variations for 100,000-Location RepositoryAreal Size Variations for 100,000-Location Repository
ConclusionConclusion
SSMM: Optimality Interval Median: Optimality Interval Median
• Flattest proximity generation profile (scalability)Flattest proximity generation profile (scalability)
• Lowest proximity generation profile (performance)Lowest proximity generation profile (performance)
• Easily determined (manageability) Easily determined (manageability)
Research OutputsResearch OutputsArticles – Professional/Academic Press
Mobile Computing: Concepts, Methods, Tools, and Applications (2009)
Advanced Principles for Improving Database Design, Systems Modeling, and Software Development (2009)
Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends (2009)
Journal ArticlesInternational Journal of Web Engineering and Technology (2012)International Journal of Wireless and Mobile Computing (2009)Journal of Database Management (2006)International Journal of Mobile Communications (2003)
PatentsCanada 2010 - OptimizationUnited States 2004 - Linkcells
Jim Wyse, ISPJim Wyse, ISP
www.busi.mun.ca/jwyse
Thank you!!
Meanwhile, Back at the Server: Proximity Generation for Meanwhile, Back at the Server: Proximity Generation for Location-Based Mobile ApplicationsLocation-Based Mobile Applications