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Accessing to Spatial Data in Mobile Environment
Presented By
Jekkin Shah
Framework
Web based navigation system
Spatial data visualization
Problem definition
• What is the data management principle for data processing in an embedded navigation system ?
• Issues– Diversity of data sources – Data exchange between the embedded
system and the data sources
Architecture and Software Components
• Client server model
• Centralized server
• Client side processing
Visualization
GPSServer
Server
Files
Databases
Data Servers
Local Distant
Approach / Techniques
LOCATION !
Location based query restriction
Data caching – Semantic inclusion of queries– Location based caching
Query Data Management Response
Data management parts
Restriction Validation
Inclusion / Cache Communication
Data Model
Object Oriented Model
Object Entity = (o,v,g,t)
o = unique object identifier
v = (v1,…, vn) set of alphanumeric attribute values
t = time parameter
g = geographical position
( modeled by point, line or region )
Data model ( cont. )
Typical query :
select o from l1,…, ln where C
• Query q = (l, L, C)• l = layer to which selected objects belong• l € L• L = {l1,…, ln } : set of layers • C = selection condition over set L
• q = (l, L, C)• Restriction Cr
( location based )• q = (l, L, C ^ Cr )
Query Restriction
Query Restriction ( cont. )
Data presentation conditions change with time
qrnew = (l, L, C ^ Cr
new )
• Restriction and query history maintained
(Cr1,…, Cr
n) , (qr1,…, qr
n) at time ti , (0 ≤ i ≤ n)
• Query re-execution is implicit ( similar to continuous persistent queries )
Query Restriction ( cont. )
• What happens when one of the layers of L is modified at the server ?
• q = (l, L, C)• at time t` , L becomes L`• all queries for which ti < t` become invalid
- remove the invalid entries for the memory- reuse them for further queries
( after validation of objects )
Semantic caching of queries
IDEA ???
Try to construct the result of the new query from the results of the previous queries
• The selection condition C can be considered as a description of its results
q = (l, L, C) , q(I)
For a new query q
3 scenarios :
• Not all objects of the answer q(I) are available at the client
• Client owns all objects of the answer, but result cannot be calculated locally
• All objects locally present to calculate the correct answer
Semantic caching (cont.)
n
Crn+1(I) => ( v Cr
i )(I)
i = 1
Qn+1
Q1
Q2
Q3
Data Validation
• Validation at Layer level
• Validation at object level
• 3 subsets
• Present : present in both Is and Ic
• Obsolete : present in Ic but deleted from Is
• New : present in Is but not yet in Ic
Data Validation
• Validation at client side– Validate whole local instance Ic against an
answer of query executed on server ( a set Is )
– Leave obsolete objects as it is
• Validation at server side– Compare Ic with Is
• delete obsolete• Add new • Update modified objects
Data Transfer
• 3 cases of transfer :– whole object
V = (o,t,v,g)
– object identifier and modification time
T = (o,t)
– object identifier O = (o)
Client Server
q
V
Data Transfer
Client Server
qTOV
Client Server
q + T
V+O
Local validation at client side
Validation at server side
Data Replacement
• Time based :
Replacement of the oldest answer (LRU)
• Location based :
Replacement of the farthest answer
Explicit or Implicit replacement
Simulation results
• Restriction with cache (unlimited) 25% of objects transferred to the client Data transfer reduced to around 75%
( as compared to restriction without cache )
Conclusion and further directions
• Semantic caching uses restriction to visible zoneAnalysis of more general selection conditions
• Caching can be extended to multiscale data.
Scale parameters include resources available, communication speed, map revolution
• Inclusion of spatial indexes
The end