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