Eamonn Keogh, UC Riverside – August 2018

Time Series Snippets: A New Analytics Primitive with applications to IoT Edge Computing

While most of today’s always-connected tech devices take advantage of cloud computing, many Internet of Things (IoT) developers increasingly understand the benefits of doing more analytics on the devices themselves, a philosophy known as edge computing. By performing analytic tasks directly on the sensor, edge computing can drastically reduce the bandwidth, cloud processing, and cloud storage needed.

However, even if taken to the extreme, edge computing will occasionally have to report some summary data to a central server. Thus, a common type of IoT analytical query is essentially “Send me some representative/typical data.” This query might be issued by a human attempting to understand an unexpected event at a manufacturing plant, or it might be issued by an algorithm as a subroutine in some higher-level analytics. In either case, the problem of finding representative time series subsequences has not been solved despite the ubiquity of time series in almost all human endeavors, and especially in IoT domains.

In this proposal Prof Eamonn Keogh argues for a new definition of representative patterns called time series snippets. In many domains, time series snippets allow an extreme form of data reduction; instead of transmitting many megabytes of data from each sensor per hour, it may be possible to transmit just a few kilobytes of data, containing a handful of snippets with their metadata. Moreover, the proposal will attempt to show that many downstream analytic tasks, including classification, anomaly detection and monitoring, can greatly benefit by reasoning about the snippets, rather than working with the raw data.