{"id":4785,"date":"2020-10-16T11:36:58","date_gmt":"2020-10-16T15:36:58","guid":{"rendered":"https:\/\/carleton.ca\/cuids\/?post_type=cu-events&#038;p=4785"},"modified":"2020-10-16T11:36:58","modified_gmt":"2020-10-16T15:36:58","slug":"timely-processing-of-big-data-challenges-and-answers","status":"publish","type":"cu-events","link":"https:\/\/carleton.ca\/cuids\/cu-events\/timely-processing-of-big-data-challenges-and-answers\/","title":{"rendered":"Timely Processing of Big Data: Challenges and Answers"},"content":{"rendered":"<p><img decoding=\"async\" loading=\"lazy\" class=\"size-medium wp-image-4616 aligncenter\" src=\"https:\/\/carleton.ca\/cuids\/wp-content\/uploads\/Institute-for-Data-Science-color-small-240x72.png\" alt=\"\" width=\"240\" height=\"72\" srcset=\"https:\/\/carleton.ca\/cuids\/wp-content\/uploads\/Institute-for-Data-Science-color-small-240x72.png 240w, https:\/\/carleton.ca\/cuids\/wp-content\/uploads\/Institute-for-Data-Science-color-small-160x48.png 160w, https:\/\/carleton.ca\/cuids\/wp-content\/uploads\/Institute-for-Data-Science-color-small-400x119.png 400w, https:\/\/carleton.ca\/cuids\/wp-content\/uploads\/Institute-for-Data-Science-color-small-360x107.png 360w, https:\/\/carleton.ca\/cuids\/wp-content\/uploads\/Institute-for-Data-Science-color-small.png 587w\" sizes=\"(max-width: 240px) 100vw, 240px\" \/><\/p>\n<p style=\"text-align: center;\"><em>Data Science Distinguished Speaker Seminar Series\u00a0\u00a0<\/em><\/p>\n<p><strong>Speaker<\/strong>: Shikharesh Majumdar<\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p>The availability of information rich data from various sources that include enterprise systems as well as sensor-based smart facilities is fueling\u00a0research on effectively harnessing the power of Big Data. Data analytics that concerns the extraction of\u00a0knowledge from such large volumes of raw data is of great deal of interest to both\u00a0researchers and system users. However, data analytics needs to be performed efficiently\u00a0so that the results are available to the users in a cost effective and timely manner. The\u00a0challenges of Big Data processing often result from its large volume and the speed at\u00a0which data is generated dynamically from enterprise computing applications, social\u00a0networks and sensor based systems for example.<\/p>\n<p>Parallel processing techniques, cloud-based platforms (e.g. MapReduce\/Hadoop, Spark\u00a0and Storm) used for deploying these techniques as well the management of resources on\u00a0such platforms are crucial for effectively utilizing the power of Big Data. Latency\u00a0sensitive real-time applications used for analyzing noisy streaming\u00a0 data from multiple\u00a0sensors deployed in a cyber-physical system often add to the challenges of Big Data\u00a0processing. Selected research results from the domains of enterprise computing, multiple\u00a0object tracking (video analytics) and smart phone based remote health monitoring will be\u00a0presented.<\/p>\n<p><strong>About the Speaker<\/strong><\/p>\n<p>Shikharesh Majumdar is a Chancellor\u2019s Professor and the Director of the Real Time and Distributed Systems Research Centre at the Department of Systems and Computer Engineering in Carleton University in Ottawa, Canada. He is a member of the board for Carleton University Institute for Data Science and a member of the faculty team involved with Carleton University\u2019s Canada-India Centre for Excellence. He holds a Ph.D. degree in Computational Science from University of Saskatchewan, Saskatoon, Canada. His research interests are in the areas of cloud and grid computing (with the current focus on Big Data processing), smart systems, operating systems and performance evaluation. Dr. Majumdar actively collaborates with the industrial sector and has performed his sabbatical research at Nortel and Cistech. He has been the area editor for the Simulation Modelling Practice and Theory journal published by Elsevier (2008-2017). Dr. Majumdar is a Fellow of the Institution of Engineering and Technology (FIET), a member of ACM, a senior member of IEEE and a Professional Engineer (Ontario, Canada). He was a Distinguished Visitor for the IEEE Computer Society from 1998 to 2001.<\/p>\n<p><strong>Seminar Moderator:<\/strong><br \/>\n<a href=\"https:\/\/orcid.org\/0000-0003-1847-2738\"><strong>Dr. Tracey P. Lauriault<\/strong><\/a>, Associate Professor, Critical Media and Big Data, School of Journalism and Communication, Carleton University<\/p>\n<p><\/p>\n<p style=\"text-align: center;\"><em>Zoom webinar information for the Data Science Distinguished Speaker Seminar Series is sent to our mailing list (sign up <a href=\"https:\/\/carleton.ca\/cuids\/contact\/\">here<\/a>). If you are not on our mailing list and would like to attend this virtual seminar, please e-mail <a href=\"&#x6d;&#x61;&#105;&#108;t&#x6f;&#x3a;&#x63;&#117;&#105;d&#x73;&#x40;&#x63;&#97;&#114;l&#x65;&#x74;&#x6f;&#110;&#46;c&#x61;\">&#x63;&#x75;&#x69;&#x64;&#x73;&#x40;&#x63;&#x61;&#114;&#108;&#101;&#116;&#111;&#110;&#46;ca<\/a>.<\/em><\/p>\n","protected":false},"template":"","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_links_to":"","_links_to_target":""},"daevent-type":[16],"event-audience":[],"event-featured":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.2 - 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