1. The Keogh Lab 1 Presented by Abdullah Mueen

2. Overview of our work Our Goal: Extract information from raw, noisy, massive, unstructured data. We develop algorithms for – Classification – Clustering – Rule finding – Motif discovery – Discord discovery – Shapelet discovery – Linkage discovery We work closely with the domain experts. – For collecting new data. – To verify our results. 2

3. Case 1: Motif Discovery 3 Beet Leafhopper ( Circulifer tenellus) plant membrane Stylet voltage source input resistor V 050100150200 0 1010 2020 to insect conductive glue voltage reading to soil near plant Exact Discovery of Time Series Motifs. Abdullah Mueen, Eamonn Keogh, Qiang Zhu, Sydney Cash, Brandon Westover. SDM 2009. MK motif discovery

4. false nettles stinging nettles Case 2: Shapelet Discovery 4 false nettles Shapelet stinging nettles Time Series Shapelets: A New Primitive for Data Mining. Lexiang Ye and Eamonn Keogh. SIGKDD 2009

5. Case 3: Linkage Discovery 5 CK-1 0.6291 CK-1 0.9033 CK-1 Distance Measure 0.6 0.7 0.8 0.9 CK-1 Distance Single Linkage Dendrogram Print House 1Print House 2 A Compression Based Distance Measure for Texture. Bilson Campana and Eamonn Keogh. SDM 2010 text a hand-press book character matrix text ornamentstext

6. Lab Members Dr. Eamonn Keogh Dr. Gustavo Batista Abdullah Mueen Qiang Zhu Bilson Campana Thanawin Art R. Bing Hu Yuan Hao Jesin Zakaria 6

7. Motif in Online Data Maintain motif in streaming data without introducing latency. 7

8. Motion Motif 8 Find repeated motion in motion capture data which is a 32 dimensional time series.