1. 1 Copyright © 2003 KAIST All Rights Reserved. Using Semantic Caching to Manage Location Dependent Data in Mobile Computing 2003.3.18 CS 744 Database Lab. Se-Kyoung Huh

2. 2 Copyright © 2003 KAIST All Rights Reserved. Contents Background Semantic Cache Modeling LDD Query LDD Semantic Cache Index LDD Query Processing LDD Cache Management Experiment Conclusion

3. 3 Copyright © 2003 KAIST All Rights Reserved. Background Characteristic of mobile computing Large overlapped results for continuous queries Disconnected situation Advantage of caching data for mobile computing Wireless network traffic cost down System performance up

4. 4 Copyright © 2003 KAIST All Rights Reserved. Semantic Cache Semantic Cache vs. Page Cache Advantage of semantic cache for LDD (Location Dependent Data) Strong semantic locality than spatial locality for semantic LDD application Possibility of flexible cache management Use of semantic information in disconnection situations Semantic CachePage Cache GranularityQuery resultDatabase tuples or pages ContentSemantic information + resultresult

5. 5 Copyright © 2003 KAIST All Rights Reserved. Modeling LDD Query Q = “Give me the names of the hotels within 20 miles whose prices are below $100” Qp = (price < 100) ∩ (Lx-20 < xposition <= Lx+20) ∩ (Ly-20 <= yposition < Ly+20) (Lx,Ly) : current user position Assumption : reference point is given Dependent on the current user position

6. 6 Copyright © 2003 KAIST All Rights Reserved. LDD Semantic Cache Index SSRSR SASA SPSP SLSL SCSC S TS S1S1 HotelHname (Lx-5 <=hxpos<=Lx+5)∩ (Ly-5 <=hypos <=Ly+5) 10,202T1 S2S2 Rest. Rname, Type (Lx-10<=rxpos<=Lx+10)∩ (Ly-10 <=rypos <=Ly+10)∩ (6<=sched<=9) -5,155T2 S3S3 HotelHname, Vacancy (Lx-5 <=hxpos<=Lx+5) ∩ (Ly-5<=hypos<=Ly+5) ∩ (Price<100) -5,-208T3 Semantic information TableAttributeBound position Predicate Index for cache result Time Stamp

7. 7 Copyright © 2003 KAIST All Rights Reserved. LDD Query Processing Relationship between query and cache If query is contained by cache Use cache for query processing If query is partly contained by cache Split the query into The query satisfied by cache » by checking through all segment in the cache The query not satisfied by cache Send only the query not satisfied by cache to server Coalesce every partial query result Add new query result into cache Need for decomposition of segments to prevent duplicated cache segment

8. 8 Copyright © 2003 KAIST All Rights Reserved. LDD Cache Management Replacement principle Incorporation of the status of the mobile user The moving direction The distance from cache segment

9. 9 Copyright © 2003 KAIST All Rights Reserved. LDD Cache Management (cont’d) FAR algorithm Divide cache segment In Direction set Segment in the user’s moving direction Out Direction set Segment not in the user’s moving direction Choose the victim among the Out Direction set If Out Direction set is empty Choose the victim the furthest segment in In Direction set

10. 10 Copyright © 2003 KAIST All Rights Reserved. Experiment Page Caching vs. Semantic Caching Database is neither indexed nor clustered Semantic caching is better Due to the highly reduced wireless network traffic Only the required data is transferred

11. 11 Copyright © 2003 KAIST All Rights Reserved. Experiment (cont’d) Page Caching vs. Semantic Caching (cont’d) index on x, column-wise scan clustering Page caching becomes better than one in no index database Due to not necessity of scanning database for finding page

12. 12 Copyright © 2003 KAIST All Rights Reserved. Experiment (cont’d) Page Caching vs. Semantic Caching (cont’d) index on x, column-wise scan clustering Page Caching is sensitive for the organization of database

13. 13 Copyright © 2003 KAIST All Rights Reserved. Experiment (cont’d) Comparison of several replacement policy FAR is better than LRU or MRU

14. 14 Copyright © 2003 KAIST All Rights Reserved. Conclusion Contribution Propose semantic cache concept for mobile computing Weakness Cache replacement policy Always possible for predicting user’s movement direction?