1. Accessing to Spatial Data in Mobile Environment Presented By Jekkin Shah

2. Framework Web based navigation system Spatial data visualization

3. 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

4. Architecture and Software Components Client server model Centralized server Client side processing Visualization GPS Server Files Databases Data Servers LocalDistant

5. Approach / Techniques LOCATION ! Location based query restriction Data caching –Semantic inclusion of queries –Location based caching

6. Query Data Management Response Data management parts RestrictionValidation Inclusion / CacheCommunication

7. Data Model Object Oriented Model Object Entity = (o,v,g,t) o = unique object identifier v = (v 1,…, v n ) set of alphanumeric attribute values t = time parameter g = geographical position ( modeled by point, line or region )

8. Data model ( cont. ) Typical query : select o from l 1,…, l n where C Query q = (l, L, C) l = layer to which selected objects belong l € L L = {l 1,…, l n } : set of layers C = selection condition over set L

9. q = (l, L, C) Restriction C r ( location based ) q = (l, L, C ^ C r ) Query Restriction

10. Query Restriction ( cont. ) Data presentation conditions change with time q r new = (l, L, C ^ C r new ) Restriction and query history maintained (C r 1,…, C r n ), (q r 1,…, q r n ) at time t i, (0 ≤ i ≤ n) Query re-execution is implicit ( similar to continuous persistent queries )

11. 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 t i < t` become invalid - remove the invalid entries for the memory - reuse them for further queries ( after validation of objects )

12. 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)

13. 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

14. Semantic caching (cont.) n C r n+1 (I) => ( v C r i )(I) i = 1 Q n+1 Q1Q1 Q2Q2 Q3Q3

15. Data Validation Validation at Layer level Validation at object level 3 subsets Present : present in both I s and I c Obsolete : present in I c but deleted from I s New : present in I s but not yet in I c

16. Data Validation Validation at client side –Validate whole local instance I c against an answer of query executed on server ( a set I s ) –Leave obsolete objects as it is Validation at server side –Compare I c with I s delete obsolete Add new Update modified objects

17. 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) ClientServer q V

18. Data Transfer ClientServer q T O V ClientServer q + T V+O Local validation at client side Validation at server side

19. Data Replacement Time based : Replacement of the oldest answer (LRU) Location based : Replacement of the farthest answer Explicit or Implicit replacement

20. 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 )

21. Conclusion and further directions Semantic caching uses restriction to visible zone Analysis 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

22. The end