Past Event! Note: this event has already taken place.
Log Modeling and Analytics: Advancements in Application Execution Management
November 14, 2016 at 1:30 PM to 3:00 PM
| Location: | 5345 Herzberg Laboratories |
| Audience: | Anyone |
| Contact Email: | cuids@carleton.ca |
ABSTRACT
Monitoring and management of large scale data and applications have always been complex tasks, especially because the data is unstructured and often logged in applications in a syntactic manner. This makes it quite limited, requires manual interpretation and hence makes the process of monitoring and management slow, cumbersome and hard. This talk will present an overview of our proposed solution of data modeling in correlation with analytical techniques in order to improve monitoring and management process for large-scale data and applications. We carry out semantic (i.e., highly structured, formalized and expressive) modeling of data, execution workflow and logs, and then build, customize and use Data Analytics techniques, in correlation with the semantically enriched data, to process the data that helps in automating the monitoring and management process. There have been several related efforts but such approaches still could not achieve the goal effectively mainly because (1) there is no consideration of correlation between the modeling of execution data, execution workflow, logs and (2) having data analytical solutions handle unstructured, ambiguous and raw data. We have designed and developed our unique hybrid approach of partially using data modeling and description, as well as customized data analysis and social network analysis techniques to be able to automatically interpret and process the highly structured information from data and logs generated during the execution. In this way, our approach combines the best characteristics of data modeling and data analytics and helps in improving the automated monitoring and management of data and applications at large-scale. The impact, usefulness and effectiveness of our solution have been demonstrated by applying it on industrial case-studies and scenarios.
ABOUT THE SPEAKER
M. Omair Shafiq is an Assistant Professor at the School of Information Technology, Carleton University. He completed his PhD in Computer Science from the University of Calgary in 2015. His research interests include Data Modeling, Big Data Analytics, Services Computing, Machine Learning and Cloud Computing. He received NSERC Postdoctoral Fellowship Award and Mitacs Elevate Postdoctoral Fellowship Award in 2015-2016 competition, NSERC Vanier CGS Scholarship in 2012, Alberta-Innovates Technology Futures (AITF) Scholarships for PhD and Master studies in 2011 and 2010, J.B. Hyne Research Innovation Award from University of Calgary in 2012, Departmental Research Award from University of Calgary in 2009 and 2010 and Teaching Excellence Award from University of Calgary in 2011. He has published over 50 peer-reviewed publications in journals, book chapters, conferences and workshops, served in technical program committee of over 30 conferences and workshops, co-organized more than 8 conference and workshops.
This seminar is free and open to all. Complimentary coffee, tea and light snacks will be provided beginning at 1:15 p.m. We hope you can join us!
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