8.10.15 Abdullah

Big Data Analytics and Machine Intelligence Seminar #9:

Prof. Mueen Abdullah, University of New Mexico
August 10, 2015, 10:00 am, NASA Langley, Pearl Young Theater, Bldg 2102
Live Seminar

Time series patterns are waveforms with properties useful for summarization, classification and anomaly detection. Time series patterns are interpretable to domain experts and amenable to several other knowledge discovery tasks. In this talk, I will present three types of time series patterns: Motifs, Shapelets, and Discords. Motifs are repeating patterns that repeat in seemingly random time series data; Shapelets are small segments of long time series characterizing their sources; Discords are anomalous waveforms in long time series that do not repeat anywhere else. I will show efficient unsupervised algorithms to discover these patterns and present cases in mining sensor-readouts from robots, humans and social media. Cases include activity classification using accelerometer data, correlated clusters in twitter and anomalies in astronomical and physiological data.

Prof. Abdullah is an Assistant Professor in Computer Science at the University of New Mexico. His primary interest is in temporal data mining with a focus on two unique types of signals: electrical sensors and social networks. Prof. Abdullah’s work focuses on pattern-based mining algorithms such as similarity search, correlation join, classification, clustering, and rule/association mining for both archived and streaming time series data. He is also interested in the application of these algorithms to other semi-structured data types such as audio or XML files. He has authored publications in the journals Data Mining and Knowledge Discovery and Knowledge and Information Systems, and has been published in the IEEE International Conference on Data Mining (ICDM), the Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (KDD), and the Society for Industrial and Applied Mathematics International Conference on Data Mining (SDM). He received the award for best paper at the KDD 2012 conference, along with the runner-up award in the Doctoral Dissertation Contest. He holds a PhD in Computer Science from the University of California.