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Using Semantic Caching to Manage Location Dependent Data in Mobile Computing

Using Semantic Caching to Manage Location Dependent Data in Mobile Computing. 2003.3.18 CS 744 Database Lab. Se-Kyoung Huh. Contents. Background Semantic Cache Modeling LDD Query LDD Semantic Cache Index LDD Query Processing LDD Cache Management Experiment Conclusion. Background.

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Using Semantic Caching to Manage Location Dependent Data in Mobile Computing

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  1. Using Semantic Caching to Manage Location Dependent Data in Mobile Computing 2003.3.18 CS 744 Database Lab. Se-Kyoung Huh

  2. Contents • Background • Semantic Cache • Modeling LDD Query • LDD Semantic Cache Index • LDD Query Processing • LDD Cache Management • Experiment • Conclusion

  3. 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. 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

  5. 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. LDD Semantic Cache Index Semantic information Index for cache result Table Attribute Predicate Bound position Time Stamp

  7. 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. LDD Cache Management • Replacement principle • Incorporation of the status of the mobile user • The moving direction • The distance from cache segment

  9. 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. 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. 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. 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. Experiment (cont’d) • Comparison of several replacement policy • FAR is better than LRU or MRU

  14. Conclusion • Contribution • Propose semantic cache concept for mobile computing • Weakness • Cache replacement policy • Always possible for predicting user’s movement direction?

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