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Aggregate Query Processing in Cache-Aware Wireless Sensor Networks

Aggregate Query Processing in Cache-Aware Wireless Sensor Networks. Khaled Ammar University of Alberta. Agenda. Introduction Previous Work Contribution Selection Process Hot Area Conclusion Future Work References. Introduction.

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Aggregate Query Processing in Cache-Aware Wireless Sensor Networks

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  1. Aggregate Query Processing in Cache-Aware Wireless Sensor Networks Khaled Ammar University of Alberta

  2. Agenda • Introduction • Previous Work • Contribution • Selection Process • Hot Area • Conclusion • Future Work • References

  3. Introduction • Wireless Sensor Network (WSN) is important to enable users query the physical world. • Energy consumption is the main challenge. • Spatial queries query sensor information with in a defined area. • Multi user and Multiple queries are expected.

  4. Previous work C Q [CACHE-10] M.A. Nascimento, R. Alencar, and A. Brayner. Optimizing query processing in cache-aware wireless sensor networks. Proc. of SSDBM Journal, pages 60-77, 2010.

  5. Previous work R Q Q1’ Q2’

  6. Previous work Q1’ Q2’ Ѳ1 Ѳ2

  7. Challenges for Aggregate functions • None of cached data could be considered as Relevant queries. C Q

  8. Agenda • Introduction • Previous Work • Contribution • Conclusion • Future Work • References

  9. Contribution • Customize Selection Process criteria • Special Handling for the Hot Area

  10. Customize Selection Process criteria • In the previous approach [CACHE-10]: • All queries assumed to be row data queries. • Aggregation extension: (Native Approach) • Cached queries should be fully bounded • The Requested and the cached query should be the same Aggregate function [CACHE-10] M.A. Nascimento, R. Alencar, and A. Brayner. Optimizing query processing in cache-aware wireless sensor networks. Proc. of SSDBM Journal, pages 60-77, 2010.

  11. Customize Selection Process criteria • Proposed: • Cached queries should be fully bounded: • Average  Sum and Count • Sum + Count  Average • Histogram  Count, Average, Sum, Max, Min • Accept cached queries not fully bounded if: • Queries match • Aggregate function = Max or Min • Query answer belongs to the queried area

  12. Customize Selection Process criteriaPerformance Evaluation

  13. Customize Selection Process criteriaPerformance Evaluation

  14. Customize Selection Process criteriaPerformance Evaluation

  15. Customize Selection Process criteriaPerformance Evaluation

  16. Special Handling for the Hot Area • Definition: Hot Area is an area in the monitored field with high frequent queries. • Any monitored field, usually have a specific group of areas with high importance. • Examples: Gates, Server rooms, • Searching for a Hot area is out of our scope.

  17. Special Handling for the Hot Area • Which query is more useful for others

  18. Conclusion • Existing Cache-Aware WSN can save about 5% of the queries cost. • Proposed new rules for relevant query increase the percentage to about 15% • Histogram was shown to be very helpful to all other aggregates. • Relaxing the condition of bounded queries is more important than relaxing the condition of queries matching .

  19. Thanks

  20. Histogram for Exact queries • Histogram provides approximate answers only • Recently, we proposed HIU [HIU-11]: • Cheaper than TAG, use around 1/3 of TAG’s cost. • Can compute exact answers as well as approximate. • It has an extension to answer a Median query [RBM-11] [HIU-11] Khaled Ammar and Mario A. Nascimento. Histogram and other aggregate queries in wireless sensor networks. Proc. of SSDBM Journal, page (to appear), 2011. [RBM-11] K. Ammar, M.A. Nascimento, and J. Niedermayer. An adaptive refinement-based algorithm for median queries in wireless sensor networks. In Proc. of MobiDE, page (to appear), 2011.  Back

  21. Special Handling for the Hot Area • Cost of Histogram vs. Row data [TAG02]

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