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DTSTART;TZID=America/Toronto:20161118T120000
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SUMMARY:Operational-Log Analysis for Big Data Systems: Challenges and Solutions
DESCRIPTION:Friday November 18\, 2016 at 12:00 p.m. Dr. Andriy Miranskyy\, Assistant Professor at the Department of Computer Science\, Ryerson University\, will be presenting “Operational-Log Analysis for Big Data Systems: Challenges and Solutions”. \nSpeaker: Dr. Andriy Miranskyy\nAssistant Professor\, Department of Computer Science\, Ryerson University \nDay & Time: Friday\, November 18\, 2016\n12:00 p.m. – 1:00 p.m. \nLocation: George Vari Centre for Computing and Engineering\nRyerson University\nRoom: ENG 288\n245 Church Street\, Toronto\, Ontario M5B 2K3\nMap – http://www.ryerson.ca/maps – Look for ENG \nRegistration: Registration is free\, but space is limited. Please register via this link: http://tinyurl.com/systemsEvent \nOrganizers: IEEE Toronto Systems Chapter\, Alexei Botchkarev albot@ieee.org\nIEEE Toronto WIE\, Magnetics\, Measurement/Instrumentation-Robotics and Computer Science Department of Ryerson University\nIEEE Toronto WIE Chair: Maryam Davoudpour maryam.davoudpour@ieee.org \nAbstract: Big data systems (BDSs) are complex\, consisting of multiple interacting hardware software components\, such as distributed compute nodes\, networking\, databases\, middleware\, business intelligence layer\, and high availability infrastructure. Any of these components can fail. Finding the failures’ root causes is extremely laborious. Analysis of BDS-generated logs can speed up this process. The logs can also help improve testing processes\, detect security breaches\, customize operational profiles\, and aid with any other tasks requiring runtime-data analysis. \nHowever\, practical challenges hamper log analysis tools’ adoption. The logs emitted by a BDS can be thought of as big data themselves. When working with large logs\, practitioners face seven main issues: scarce storage\, unscalable log analysis\, inaccurate capture and replay of logs\, inadequate log-processing tools\, incorrect log classification\, a variety of log formats\, and inadequate privacy of sensitive data. This talk describes the challenges and practical solutions faced while building and institutionalizing dynamic analysis tools in the industry. \nBiography: Andriy Miranskyy is an assistant professor at the Department of Computer Science\, Ryerson University. His research interests are in the area of mitigating risk in software engineering\, focusing on software quality assurance\, program comprehension\, software requirements\, project risk management\, Big Data\, and Green IT. Andriy received his Ph.D. in Applied Mathematics at the University of Western Ontario. He has 17 years of software engineering experience in information management and pharmaceutical industries. Prior to joining Ryerson\, Andriy worked as a software engineer in the IBM Information Management division at the IBM Toronto Software Laboratory; currently\, he is the Faculty Fellow of the IBM Centre for Advanced Studies. He has served as Guest Editor for a special edition of IEEE Software as well as organizer\, committee member\, and reviewer for several software engineering workshops and conferences.
URL:https://www.ieeetoronto.ca/event/operational-log-analysis-for-big-data-systems-challenges-and-solutions/
LOCATION:Room: ENG 288\, 245 Church Street\, Toronto\, Ontario M5B 2K3
CATEGORIES:Instrumentation & Measurement,Magnetics,Systems,Women in Engineering
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