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Meanwhile ... back at the server Managing Server-Based Data in Support of the Location-Based m -Business Applications of Location-Variant Mobile Users Jim Wyse 7 th World Congress on the Management of e -Business (2006). m -Business Environment. Mobile Business ( m -Business).
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Meanwhile ... back at the server Managing Server-Based Data in Support of the Location-Based m-Business Applications of Location-Variant Mobile UsersJim Wyse7th World Congress on the Management of e-Business (2006)
Mobile Business (m-Business) • transactions through communication channels that permit a high degree of mobility by at least one of the transactional parties.
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 capability: location awareness. • Yuan and Zhang (2003): “location awareness … is a new dimension for value creation” in a wide variety mobile business applications.
Location Aware Capability • 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 (Butz, Baus, and Kruger 2000) in support of location-referent transactions.
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 selected category? • A proximity query bears criteria that reference static attributes (e.g., hospital) and dynamic attributes (e.g., nearest). • Proximity queries are burdensome to conventional query resolution approaches (Nievergelt and Widmayer, 1997).
Proximity Query Resolution: Proximity Portals The i-DAR Prototype
Location-Aware Linkcell Method • Transforms mu’s position (47.523° N, 119.137° W) into a linkcell (N47W119). • Initiates search sequence at mu’s linkcell {N48W119, N48W118, N47W118, N46W118, ….} • Permits large numbers of locations to be excluded as proximity portal candidates. • Requires an appropriate linkcell ‘size’ to give superior performance.
Figure 4 100,000-Location SCR – Brute Force Results c-effect n-effect
Optimal Linkcell Size Solve …. PTC(S) = 1 – (1 – nTC/N)N/CS 0.6 . . . (A) . . . . for Linkcell “Name Increments” nTC is the number of locations in category, TC, N is total number of locations, and CS is the number of linkcells of size, S, created from the N locations.
MCRs and SCRs • Multiple Category Repositories (MCRs) • Single Category Repositories (SCRs) • Equation (A) applies to MCRs but not to SCRs • For SCRs, nTC = N PTC(S) = 1, for all S.
Single Category Repositories (SCRs) • For SCRs, it is hypothesized that • P(S) = 1 – (1 – S2/4A)N 0.6 . . . (B) • will yield optimal values, where • A is the entire geographical area covered by the repository, • S is the linkcell size, and • N is the number of locations. • Some preliminary results ……
Critical Area for Further Work • Uniform locational distributions assumed • Businesses often locate or co-locate in non-uniform ways: • - pharmacies next to medical clinics • - law firms in legal ‘districts’ • - retail petroleum outlets near highway intersections. • - etc.
Jim Wysewww.busi.mun.ca/jwyse Conference Paper Data Management for Location-Based Mobile Business Applications: The Location-Aware Linkcell Method