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DESCRIPTION:Anomaly detection/prediction usually relies on wide domain know
 ledge to build up the tools to automatically detect/predict abnormal event
 s or behaviors of an IoT system. An IoT system may consist of machines wit
 h different capabilities\, functionalities and ages. Abnormal events or be
 haviors are usually rare events. It is time-consuming and high-cost to bui
 ld up the domain knowhow of the IoT systems and collect enough data points
  of the anomaly. In this lecture\, I first identify the issues and challen
 ges. Then I illustrate a general environment for anomaly detection/perditi
 on. Then I will illustrate the technologies and solutions for anomaly dete
 ction/prediction\, and show some prototypes and their applications.\n\nSpe
 aker(s): Phone Lin\, \n\nVirtual: https://events.vtools.ieee.org/m/358912
LOCATION:Virtual: https://events.vtools.ieee.org/m/358912
ORGANIZER:l5zhao@torontomu.ca
SEQUENCE:3
SUMMARY:Data-Driven Anomaly Detection & Prediction for IoT
URL;VALUE=URI:https://events.vtools.ieee.org/m/358912
X-ALT-DESC:Description: <br /><div class="page" title="Page 1">\n<div class
 ="layoutArea">\n<div class="column">\n<p>Anomaly detection/prediction usua
 lly relies on wide domain knowledge to build up the tools to automatically
  detect/predict abnormal events or behaviors of an IoT system. An IoT syst
 em may consist of machines with different capabilities\, functionalities a
 nd ages. Abnormal events or behaviors are usually rare events. It is time-
 consuming and high-cost to build up the domain knowhow of the IoT systems 
 and collect enough data points of the anomaly. In this lecture\, I first i
 dentify the issues and challenges. Then I illustrate a general environment
  for anomaly detection/perdition. Then I will illustrate the technologies 
 and solutions for anomaly detection/prediction\, and show some prototypes 
 and their applications.</p>\n</div>\n</div>\n</div>
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