Proximity Generation for Location- Based Mobile Applications “... meanwhile, back at the...

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