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Probabilistic Similarity Search for Uncertain Time Series

Probabilistic Similarity Search for Uncertain Time Series. Presented by CAO Chen 21 st Feb, 2011. Outline. Introduction Background Time Series Similarity Search Motivation & Contribution Uncertain Time Series Query Uncertainty Approximation Step-wise Refinement Evaluation

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Probabilistic Similarity Search for Uncertain Time Series

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  1. Probabilistic Similarity Search for Uncertain Time Series Presented by CAO Chen 21st Feb, 2011

  2. Outline • Introduction • Background • Time Series • Similarity Search • Motivation & Contribution • Uncertain Time Series Query • Uncertainty Approximation • Step-wise Refinement • Evaluation • Related Literature Review • Q & A CAO Chen, DB Group, CSE, HKUST

  3. Background – Time Series CAO Chen, DB Group, CSE, HKUST

  4. Background – Time Series (cont’d) • Source of Time Series Data • Traffic measurements • Uncorrelated • Location tracking of moving objects • Measuring environmental parameter(temperature) • Correlated CAO Chen, DB Group, CSE, HKUST

  5. Background – Similarity Search • Similarity Search • Pattern Matching • Shape Matching CAO Chen, DB Group, CSE, HKUST

  6. Background – Similarity Search (cont’d) • Range Query • Return all tuples that fits between an upper and lower boundary. • We don’t know how many it will return • Slower than top-k because no upper bound to prune • Sequence Matching • Whole matching: Sequences with same length • Subsequence Matching CAO Chen, DB Group, CSE, HKUST

  7. Motivation & Contribution • Uncertainty • Moving objects • Object identification • Sensor network monitoring CAO Chen, DB Group, CSE, HKUST

  8. Motivation & Contribution (cont’d) • Contribution • (Firstly) Formalize the notion of uncertain time series • Two novel types of probabilistic range queries over uncertain time series • Pruning strategy based on approximating representation of uncertainty • Explicitly evaluate the refinement(processing) time cost CAO Chen, DB Group, CSE, HKUST

  9. Outline • Introduction • Background • Time Series • Similarity Search • Motivation & Contribution • Uncertain Time Series Query • Uncertainty Approximation • Step-wise Refinement • Evaluation • Related Literature Review • Q & A CAO Chen, DB Group, CSE, HKUST

  10. Probabilistic Queries Over Uncertain TS • Definition of Uncertain Time Series CAO Chen, DB Group, CSE, HKUST

  11. Probabilistic Queries Over Uncertain TS (cont’d) • Definition of Uncertain Lp-Distance CAO Chen, DB Group, CSE, HKUST

  12. Probabilistic Queries Over Uncertain TS (cont’d) • Definition of Probabilistic Range Queries CAO Chen, DB Group, CSE, HKUST

  13. Challenge in Processing Range Queries with Uncertainty • Naïve Solution • Computing all distance observations • CPU-bound vs. I/O bound • Long time series and high sample rates (large n), • Naïve Solution • Number of computing the distance CAO Chen, DB Group, CSE, HKUST

  14. Outline • Introduction • Background • Time Series • Similarity Search • Motivation & Contribution • Uncertain Time Series Query • Uncertainty Approximation • Step-wise Refinement • Evaluation • Related Literature Review • Q & A CAO Chen, DB Group, CSE, HKUST

  15. Approximate Representation CAO Chen, DB Group, CSE, HKUST

  16. Approximate Representation (cont’d) • Two Levels of Appr. Representation • Different in whether existing multiple(K) groups of sample observation in one time slot CAO Chen, DB Group, CSE, HKUST Only one group at each time slot By K-means clustering

  17. Distance Approximations CAO Chen, DB Group, CSE, HKUST

  18. Distance Approximations (cont’d) CAO Chen, DB Group, CSE, HKUST

  19. Distance Approximations (cont’d) • Lemma 1 • Lemma 2 CAO Chen, DB Group, CSE, HKUST

  20. Probabilistic Bounded Range Queries (PBRQ) CAO Chen, DB Group, CSE, HKUST True Hit True Drop

  21. Outline • Introduction • Background • Time Series • Similarity Search • Motivation & Contribution • Uncertain Time Series Query • Uncertainty Approximation • Step-wise Refinement • Evaluation • Related Literature Review • Q & A CAO Chen, DB Group, CSE, HKUST

  22. Step-Wise Refinement • When to refine? • Time series that could not be filtered or determined simply by comparing the interval of lower and upper bound • Refinement Goal • To identify an uncertain time series as true hit or true drop • Condition to increase the lower bound • Increase of the number of qualified distance CAO Chen, DB Group, CSE, HKUST

  23. Step-Wise Refinement (cont’d) • Refinement heuristics CAO Chen, DB Group, CSE, HKUST

  24. Outline • Introduction • Background • Time Series • Similarity Search • Motivation & Contribution • Uncertain Time Series Query • Uncertainty Approximation • Step-wise Refinement • Evaluation • Related Literature Review • Q & A CAO Chen, DB Group, CSE, HKUST

  25. Evaluation • Benchmark • UCI Time Series Data Mining Archive • CBF, GUN/POINT, CONTROL CHART, OSU LEAF • Uncertainty • Generating samples uniformly distributed around the given exact values • Evaluation • Overall Speed-Up • Refinement Speed-Up CAO Chen, DB Group, CSE, HKUST

  26. Evaluation (cont’d) • Speed-up for Probabilistic Bounded Range Query (PBRQ) CAO Chen, DB Group, CSE, HKUST

  27. Evaluation (cont’d) • Speed-up for Probabilistic Rank Range Query (PRRQ) CAO Chen, DB Group, CSE, HKUST

  28. Evaluation (cont’d) • Speed-up w.r.t. scalability CAO Chen, DB Group, CSE, HKUST

  29. Evaluation (cont’d) • Refinement • S-S: using proposed strategy • R-R: randomly processing for both steps • Logarithm value of required calculations CAO Chen, DB Group, CSE, HKUST

  30. Outline • Introduction • Background • Time Series • Similarity Search • Motivation & Contribution • Uncertain Time Series Query • Uncertainty Approximation • Step-wise Refinement • Evaluation • Related Literature Review • Q & A CAO Chen, DB Group, CSE, HKUST

  31. Q & A • Thank You CAO Chen, DB Group, CSE, HKUST

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