Fri, Feb 17, 2017 @ 02:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Modeling and Recognition of Events from Temporal Sensor Data for Energy Applications
Ph.D. candidate: Om Prasad Patri
Friday, February 17, 2017
2:30PM, EEB 248
The ubiquitous nature of sensors and smart devices collecting more and more data from industrial and engineering equipment (such as pumps and compressors in oilfields or smart meters in energy grids) has led to new challenges in faster processing of temporal data to identify critical happenings (events) and respond to them. We deal with two primary challenges in processing events from temporal sensor data: (i) how to comprehensively model events and related happenings (event modeling), and (ii) how to automatically recognize event patterns from raw multi-sensor data (event recognition).
The event modeling problem is to build a comprehensive event model enabling complex event analysis across diverse underlying systems, people, entities, actions and happenings. We propose the Process-oriented Event Model for event processing that attempts a comprehensive representation of these processes, particularly those seen in modern energy industries and sensor data processing applications. This model brings together, in a unified framework, the different types of entities that are expected to be present at different stages of an event processing workflow and a formal specification of relationships between them.
Using event models in practice requires detailed domain knowledge about a variety of events based on raw data. We propose to learn this domain knowledge automatically by using recent advances in time series classification and shape mining, which provide methods of identifying discriminative patterns or subsequences (called shapelets). These methods show great potential for real sensor data as they don\'t make assumptions about the nature, source, structure, distribution, or stationarity of input time series, provide visual intuition, and perform fast event classification. By combining shape extraction and feature selection, we extend this temporal shape mining paradigm for processing data from multiple sensors. We present evaluation results to illustrate the performance of our approaches on real-world sensor data.
Om Prasad Patri is a CS PhD candidate at USC advised by Prof. Viktor K. Prasanna. His interests are broadly in the areas of data science, artificial intelligence, cybersecurity and event-based systems. For his dissertation, he has worked on modeling and processing information in event-based systems and pattern mining from multivariate time series sensor data. At USC, he has been a part of the Center for Smart Interactive Oil Field Technologies (CiSoft) and the USC Data Science Lab. He has also worked on internships at NEC Labs America and Cylance Inc. He holds a Bachelors in Computer Science (2011) from the Indian Institute of Technology Guwahati and a Masters in Computer Science (2014) from USC. He is a member of the IEEE, ACM, ISSIP and SPE.
Defense Committee: Viktor K. Prasanna (chair), Iraj Ershaghi, Dennis McLeod
Audiences: Everyone Is Invited
Contact: Lizsl De Leon