Transcript `Location, Location, Location` to `Context, Context, Context`

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!!