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From Location, Location, Location to Context, Context, Context Applying Location-Aware Linkcell-Based Data Management to Context-Aware Mobile Business Services Jim Wyse International Conference on Mobile Business (2007) Location-Based Context-Based 1. Location-Sensitive Mobile Services … incorporating … 2. Location-Aware Business Processes … supporting … 3. Location-Referent Transactions 1. Context-Sensitive Mobile Services … incorporating … 2. Context-Aware Business Processes … supporting … 3. Context-Referent Transactions Some Definitions (Delineations) m-Business: a set of transaction-supporting business processes that (a) interfaces with a communication channel permitting a significant degree of mobility by at least one of the transactional parties and (b) incorporates at least one CRUD function in the management of transaction-relevant data. Location-Based m-Business: m-Business in which the location of a mobile transactional party is required by a least one CRUD function. Context-Aware m-Business: m-Business in which one or more circumstances constituting a mobile transactional party’s situation is required by at least one CRUD function. Notation Mobile User’s Situation Set of Circumstances MUS {C0, C1, . . ., CN} Let C0 represent a mobile user’s spatial circumstance, then MUS {C0, C1, . . ., CN} requires a Context-Aware m-Business Service (also Location-Aware Proximity Portal Problem) MUS {C1, . . ., CN} requires a Context-Aware m-Business Service (not Location-Aware) The Article … 1. Reviews Linkcell-Based Data Management (as a solution to the Proximity Portal Problem). 2. Presents a Prototypical Context-Aware System Decomposition. 3. Proposes a Reformulated Linkcell-Based Method for Context-Aware Applications. Proximity Portals: An Example The i-DAR Prototype 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). m-Business Environment The Problem (. . . and a Solution?) Method-(The Problem) and Method- (The Solution) 4,500 3,500 3,000 2,500 2,000 1,500 1,000 500 Repository Size (thousands of locations) 49 47 45 43 41 39 37 35 33 31 29 27 25 23 21 19 17 15 13 11 9 7 5 3 0 1 Query Resolution Time (ms) 4,000 Linkcells Geographical Space Relational Space Location-Aware Linkcell Method • Transforms a mobile user’s position into a linkcell name. • Initiates a relational database search sequence at a point in the ‘repository’ corresponding to the mobile user’s geo-position. • Permits large numbers of locations to be remain unexamined as proximity portal candidates. • Requires an appropriate linkcell ‘size’ to give superior performance. Query Resolution Time (ms) Optimal Linkcell Size 5,000 4,000 3,000 2,000 1,000 0 0 100 200 300 400 Repository Size (000's) Conventional Query Resolution Optimized Linkcells Fixed 'Unmanaged' Linkcells 500 Proximity Query Resolution Time Resultset Completion Times Single Category 100,000-Location Repository Query Resolution Time (ms) 1,600 1,200 800 400 0 0.000 0.002 0.004 Linkcell Size 0.006 0.008 Optimal Linkcell Size “Solve” …. P (S) = 1 – (1 – n N/CS . . . (A) /N) TC . . . for Linkcell “Names” 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. Alternative Data Management Methods for Location-Based Services 1. Conventional (Enumerative) Methods where C, U, D are ok but not R. 2. Linkcell-Based Methods where R is ok but C, U, and D are burdened MUS {C0, C1} Context-Aware (Location-Aware) Special Case (the “Locationalized Business Directory” Case) Generalize to {C0, C1, C2, …. CN} Prototypical Context-Aware System Context Server “contextualizes” Proto-Contexts Three Types of Proto-Context 1. Non-Locationalized Proto-Contexts use conventional CRUD methods 2. Locationalized, Categorized Proto-Contexts (e.g., Locationalized Business Directories) use ‘standard’ Linkcell-Based CRUD methods 3. Locationalized, Uncategorized Proto-Contexts (e.g., Specialized, Locationalized Business Directories) use ??? (inherits the Proximity Portal Problem) Linkcell Method Reformulation Linkcell Construct . . . from: . . . to: Linkcell Optimization . . . from: P (S) = 1– (1 – nTC/N)N/CS . . . to: P (S) = 1– (1 – S2/4A)N Data Management of Proto-Contexts for Context-Aware Services: A Summary 1. Non-Locationalized Proto-Contexts use conventional CRUD methods 2. Locationalized, Categorized Proto-Contexts use ‘standard’ Linkcell-Based CRUD methods 3. Locationalized, Uncategorized Proto-Contexts use reformulated Linkcell-Based CRUD methods Applying Location-Aware LinkcellBased Data Management to ContextAware Mobile Business Services Jim Wyse www.busi.mun.ca/jwyse Thank you!!