1. A Research Sampler shasha@cs.nyu.edu http://cs.nyu.edu/cs/faculty/shasha/in dex.html

2. Philosophy Research should be fun -- good puzzles, interesting algorithms. Research should be useful -- work with real users whenever possible. Implementation should be fast (I use a very powerful programming environment that I expect my students to learn)

3. Thesis Philosophy Ideal thesis should have an interesting algorithm with analysis, an implementation, and users. Of the 15 theses I have supervised, 13 follow this model. The other two were pure systems theses.

4. Current Research Topics Time series analysis: finding correlation/bursts. Query by humming. AQuery: Database for ordered data (like time series) Computational biology: data analysis, visualization, proteomics

5. Online Pattern Discovery Sensor-less: Pairs-trading in stock trading (find highly correlated pairs in n log n time) Sensor-full: Gamma Ray Detection in astrophysics (burst detection over a large number of window sizes in almost linear time) Dennis Shasha (joint work with Yunyue Zhu, Xiaojian Zhao, Zhihua Wang, Tyler Neylon, Xin Zhang and Prof Richard Cole)

6. Goal of this work Time series are important in so many applications – biology, medicine, finance, music, physics, … A few fundamental operations occur all the time: burst detection, correlation, pattern matching. Extend functionality for music and science.

7. StatStream (VLDB,2002): Example Stock prices streams –The New York Stock Exchange (NYSE) –50,000 securities (streams); 100,000 ticks (trade and quote) Pairs Trading, a.k.a. Correlation Trading Query:“which pairs of stocks were correlated with a value of over 0.9 for the last three hours?”

8. StatStream (VLDB,2002): Example XYZ and ABC have been correlated with a correlation of 0.95 for the last three hours. Now XYZ and ABC become less correlated as XYZ goes up and ABC goes down. They should converge back later. I will sell XYZ and buy ABC …

9. Online Detection of High Correlation Given tens of thousands of high speed time series data streams, to detect high-value correlation, including synchronized and time-lagged, over sliding windows in real time. Real time –high update frequency of the data stream –fixed response time, online

10. Online Detection of High Correlation Correlated!

11. StatStream: Algorithm Naive algorithm –N : number of streams –w : size of sliding window –space O(N) and time O(N 2 w) VS space O(N 2 ) and time O(N 2 ). Suppose that the streams are updated every second. –With a Pentium 4 PC, the exact computing method can only monitor 700 streams with a delay of 2 minutes. Our Approach –Use Discrete Fourier Transform to approximate correlation –Use grid structure to filter out unlikely pairs –Our approach can monitor 10,000 streams with a delay of 2 minutes.

12. Empirical Study : Speed Our algorithm is parallelizable.

13. Sketches : Random Projection Correlation between time series of the returns of stock –Since most stock price time series are close to random walks, their return time series are close to white noise –DFT/DWT can’t capture approximate white noise series because there is no clear trend (too many frequency components). Solution : Sketches (a form of random landmark) –Sketches pool: matrix of random variables drawn from stable distribution –Sketches : The random projection of all time series to lower dimensions by multiplication with the same matrix –The Euclidean distance (correlation) between time series is approximated by the distance between their sketches with a probabilistic guarantee.

14. Burst Detection

15. Burst Detection: Applications Discovering intervals with unusually large numbers of events. –In astrophysics, the sky is constantly observed for high- energy particles. When a particular astrophysical event happens, a shower of high-energy particles arrives in addition to the background noise. Might last milliseconds or days… –In telecommunications, if the number of packages lost within a certain time period exceeds some threshold, it might indicate some network anomaly. Exact duration is unknown. –In finance, stocks with unusual high trading volumes should attract the notice of traders (or perhaps regulators).

16. Bursts across different window sizes in Gamma Rays Challenge : to discover not only the time of the burst, but also the duration of the burst.

17. Elastic Burst Detection: Problem Statement Problem: Given a time series of positive numbers x 1, x 2,..., x n, and a threshold function f(w), w=1,2,...,n, find the subsequences of any size such that their sums are above the thresholds: –all 0<w<n, 0<m<n-w, such that x m + x m+1 +…+ x m+w-1 ≥ f(w) Brute force search : O(n^2) time Our shifted wavelet tree (SWT): O(n+k) time. –k is the size of the output, i.e. the number of windows with bursts

18. Burst Detection: Data Structure and Algorithm –Define threshold for node for size 2 k to be threshold for window of size 1+ 2 k-1

19. Empirical Study : Stock Price Spread Burst

20. Elastic Burst in two dimensions Population Distribution in the US

21. Summary Able to detect bursts of many different durations in essentially linear time. Can be used both for time series and for spatial searching. Can specify thresholds either with absolute numbers or with probability of hit. Algorithm is simple to implement and has low constants (code is available). Ok, it’s embarrassingly simple.

22. With a Little Help From My Warped Correlation Karen’s hummingMatch: Dennis’s humming Match: “What would you do if I sang out of tune?" Yunyue’s humming Match:

23. Related Work in Query by Humming Traditional method: String Matching [Ghias et. al. 95, McNab et.al. 97,Uitdenbgerd and Zobel 99] –Music represented by string of pitch directions: U, D, S (degenerated interval) –Hum query is segmented to discrete notes, then string of pitch directions –Edit Distance between hum query and music score Problem –Very hard to segment the hum query –Partial solution: users are asked to hum articulately New Method : matching directly from audio [Mazzoni and Dannenberg 00] Problem –slowed down by DTW

24. Time Series Representation of Query An example hum query Note segmentation is hard! Segment this!

25. How to deal with poor hum queries? No absolute pitch –Solution: the average pitch is subtracted Incorrect tempo –Solution: Uniform Time Warping Inaccurate pitch intervals –Solution: return the k-nearest neighbors Local timing variations –Solution: Dynamic Time Warping

26. Dynamic Time Warping Euclidean distance: sum of point-by-point distance DTW distance: allowing stretching or squeezing the time axis locally

27. Dynamic Time Warping

28. AQuery A Database System for Order Dennis Shasha joint work with Alberto Lerner {lerner,shasha}@cs.nyu.edu

29. Idea Whatever can be done on a table can be done on an ordered table (arrable). Not vice-versa. AQuery – query language on arrables Expresses many queries easily Elegant new optimizations.

30. And Streams? AQuery has no special facilities for streaming data, but it is expressive enough. Idea for streaming data is to split the tables into tables that are indexed with old data and a buffer table with recent data. Optimizer works over both transparently.

31. Computational Biology Collaborations with several groups at NYU (plant and worm), Duke, Yale. Growth area: biologists need us, but we have a lot to learn. Big issues: control experimental space, evaluate data, infer an active (rather than just paper) model – combinatorial design. Visualization.

32. Sungear Design Generalizes Venn diagrams to more than three Visual outline is an ellipse having anchors on borders and vessels in the interior. Each vessel points to associated anchors. Linked views to hierarchies, lists, and graphs, so can simultaneously update data depending on user queries (selection events).

33. Venn Diagram: great for three factors

35. Sungear Principle “Sungear is stupid” Doesn’t care which kind of data it is representing, though there is built-in support for genes (because of links to GO and to cytoscape). Basic Sungear representation could be used to describe anything from yachting gear to demographics.

36. Summary Hard problems with practical motivation. Fun algorithms – not afraid of heuristics. Fast, maintainable, portable applications.