Correlating burst events on streaming stock market data

1. Correlating burst events on streaming stock market data Presenter : Shu-Ya Li Authors : Michail Vlachos, Kun-Lung Wu, Shyh-Kwei Chen, Philip S. Yu DMKD, 2008

2. Outline • Motivation • Objective • Methodology • Burst detection • Index structure • Experiments and Results • Conclusion • Personal Comments

3. Motivation • People need to make decisions about financial. • ‘Burstiness’ suggests more events of importance are happening within the same time frame. • The identification of bursts can provide useful insights about an imminent change in the monitoring quantity.

4. Objectives • The effective burst detection. • to do the right thing. • The efficient memory-based index. • to do the thing right.

5. Methodology - Overview Burst detection Index structure BD q∩b Bs = {b1, . . . , bk} Q = {q1, . . . ql}

6. Before Methodology … • Assuming a Gaussian data distribution • τ=μ+3σ 150cm<身高<170cm 身高>200cm Outliers, Noise… τ τ

7. τ Methodology - Burst detection • If si> τ, then time i is marked as a burst. • In this work we use an exponential model to describe the shape of the distribution τ 假設μ=10 P = 0.0004 x = 78.24 P = 0.9 x = 1.05 τ Burst x

8. Methodology - Index structure • Building a CEI-Overlap index • Burst intervals → Containment-encoded-intervals (CEI’s) • Insert a burst interval

9. Methodology - Index structure • Identification of overlapping burst regions

10. Experiments • Meaningfulness of results

11. Experiments • The B+ tree insertion time is linear to the number of objects, while the CEI-index exhibits constant insertion time.

12. Experiments

13. Conclusion • We have presented a complete framework for efficient correlation of bursts. • The effectiveness of our scheme is attributed to • the effective burst detection • the efficient memory-based index.

14. Personal Comments • Advantage • Many examples • Drawback • … • Application • Outlier detection