1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology ACM SIGMOD1 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

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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.

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 3 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. Motion modeling segmentation Subsequence similarity Result analysis

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 4 Methods  Using a finite state automaton to simulate the motion model.  V→ segments→ stream→ records→ DB  V i :  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 表示組成

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 L min and L max. EX : L min = 3, L max = 8; ─ Online subsequence similarity DEFINITION 2. (Online Subsequence Similarity)

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 6 Methods( cons. ) ─ Motion prediction  Offline clustering ─ Stream and patient similarity are important for many application. ─ Stream similarity : for each query subsequence from R 1, the most similar γ. N 2 retrieved subsequences from R 2 will be used to define the distance between R 1 and R 2. EX : γ=10%, at least 0.1×N 2 with the same state order from R 2. else will be removed. DEFINITION 3. (Stream Distance) Offline clustering Stream similarity Patient similarity

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 7 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. Offline clustering Stream similarity Patient similarity Motion modeling segmentation Subsequence similarity Result analysis

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 8 Experiences  Direction : ─ Evaluating the subsequence matching approach and its applications. ─ Comparing the weighted L 1 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.

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 9 Experiences (cons.)  To evaluate the similarity measure

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 10 Experiences (cons.)  There is a tradeoff between the number of predictions and the prediction accuracy.

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 11 Experiences (cons.)  Comparing with other distance function  Evaluating query subsequence generation

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. ACM SIGMOD 12 Experiences (cons.)  After clustering, the result of prediction

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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.

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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.