Subsequence Matching on Structured Time Series Data

1. Subsequence Matching on Structured Time Series Data Advisor : Dr. Hsu Presenter : Jia-Hao Yang Author :Huanmei Wu, Betty Salzberg, Gregory C Sharp, Steve B Jiang, Hiroki Shirato, David Kaeli ACM SIGMOD

2. motivation • Although many method about time series、subsequence matching have been proposed. Few less attention pay to the internal structure within the data. ACM SIGMOD

3. Motion modeling segmentation Subsequence similarity Result analysis Objective • This paper using subsequence similarity matching : • To predict tumor motion in real-time. (online) • To find a correlation between moving pattern and patient conditions. (offline) • To provide a general solution for all problem domain. ACM SIGMOD

4. Methods • Using a finite state automaton tosimulatethe motion model. • V→ segments→ stream→ records→ DB • Vi : • Online subsequence matching • Dynamic query subsequence generation • For real-time applications, query subsequences must be an accurate and last condition. • DEFINITION 1. (Subsequence Stability) • S is stable if 組成 表示 組成 組成 ACM SIGMOD

5. Methods (cons.) • The more stable, the shorter the query subsequence will be. and the length of the query subsequence is between the user specified Lmin and Lmax. • EX : Lmin = 3 , Lmax = 8; • Online subsequence similarity • DEFINITION 2. (Online Subsequence Similarity) ACM SIGMOD

6. Stream similarity Offline clustering Patient similarity Methods( cons. ) • Motion prediction • Offline clustering • Stream and patient similarity are important for many application. • Stream similarity : • for each query subsequence from R1 , the most similar γ.N2 retrieved subsequences from R2 will be used to define the distance between R1 and R2 . • EX :γ=10% , at least 0.1×N2 with the same state order from R2. else will be removed. • DEFINITION 3. (Stream Distance) ACM SIGMOD

7. Stream similarity Offline clustering Patient similarity Motion modeling segmentation Subsequence similarity Result analysis Methods( cons. ) • Patient similarity : • It based on the stream similarity. The distance is the average distance between two streams. • DEFINITION 4. (Patient Distance) • Generalization of the method • In addition to respiratory motion, there are many other applications which can be simulated and analyzed using the above framework. ACM SIGMOD

8. Experiences • Direction : • Evaluating the subsequence matching approach and its applications. • Comparing the weighted L1 distance function to the weighted Euclidean distance. • Evaluating online query subsequence generation mechanism by comparing with fixed length query subsequence. • Showing that how the result of offline analysis can help for online prediction. ACM SIGMOD

9. Experiences (cons.) • To evaluate the similarity measure ACM SIGMOD

10. Experiences (cons.) • There is a tradeoff between the number of predictions and the prediction accuracy. ACM SIGMOD

11. Experiences (cons.) • Comparing with other distance function • Evaluating query subsequence generation ACM SIGMOD

12. Experiences (cons.) • After clustering , the result of prediction ACM SIGMOD

13. Conclusion • In this paper, we introduced a solution for tumor respiratory motion analysis, clustering and online prediction and it can be generalized into a framework, which can be used in whole problems. • the approach have considered the internal structure of a time series data. ACM SIGMOD

14. Opinion • Advantage : provide a generation solution • Future work : • in automatic dynamic parameter tuning, improving noise detection, finding better motion model in cardiac, including indexing in the search algorithm. ACM SIGMOD