Application of Event Stream Processing: Data Mining the 2017 Boston Marathon

Presented by

Michael Onwi and Devin Pearson, Kennesaw State University

About this talk

The ability to analyze high-velocity data in motion is increasingly of interest to organizations and civic leaders, enabling access to real time information for decision makers through event stream processing. This is imperative not only in sensor driven industries like health care and manufacturing, but also for large scale public events with issues related to public safety, transportation and overall experience for tourists and residents. The Center for Statistics and Analytical Research at Kennesaw State University used SAS® Event Stream Processing to analyze tweets from the 2017 Boston Marathon and determine topics, patterns and trends to provide insight for future large scale events. Attributes of interest from each tweet such as contents, timestamps and locations were identified and captured. The output was then filtered by US time zones. Two counter windows, global and EST (where the Marathon was held), were used to count the number of incoming tweets in a 10 second sliding window. The information was then displayed on a dashboard. The results were analyzed over the timeline of the marathon to correlate Twitter activity to the event itself. In addition, key topics were analyzed to determine propensity for co-presence, such as pain+Advil.

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