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PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260408T131825Z
UID:Seminar-DMML-541@lxserverA.csc.liv.ac.uk.csc.liv.ac.uk
ORGANIZER:CN=Danushka Bollegala:MAILTO:Danushka.Bollegala@liverpool.ac.uk
DTSTART:20190125T100000
DTEND:20190125T110000
SUMMARY:Data Mining and Machine Learning Series
DESCRIPTION:Bakhtiar Amen: Directed Acyclic Graph Model for Scalable Contextual Anomaly Event Stream Detection\n\nThe age of big digital data has emerged, the Internet of Things (IoT) and Internet of Everything (IoE) objects are creating high volumes of streams at high velocity rate. Thus, learning and detecting anomaly from continuous event stream poses a unique challenging task before it can be dismissed or disregarded. Specifically, retrieving contextual information from event stream is one of the primarily concern in many real-time applications including in network traffics, weather broadcast, stock exchange, healthcare sensors, engineering machines and online e-commerce.  In this talk, I will demonstrates the anomaly detection concept for the big event stream scenario and present novel contextual detection method and algorithm to retrieve deeper information by using Direct Acyclic Graph (DAG) Model.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=541
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