Open Data for Business: Tackling machine-learning complexity for data curation

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A recurring challenge in open data is finding efficient ways to map the entities one finds in open data - things like company names, locations, and so on - back to a company's internal representation of those entities. This area of work is known as data curation. Machine-learning tools promise to help solve data curation problems. While the principles are well understood, the engineering details in configuring and deploying ML techniques are the biggest hurdle. Ihab Ilyas explains why leveraging data semantics and domain-specific knowledge is key in delivering the optimizations necessary for truly scalable ML curation solutions.

ABOUT THE SPEAKER

Ihab Ilyas is a professor in the Cheriton School of Computer Science at the University of Waterloo, where his main research is in the area of database systems, with a special interest in data quality and integration, managing uncertain data, rank-aware query processing, and information extraction. Ihab is also a co-founder of Tamr, a startup focusing on large-scale data integration and cleaning. He is a recipient of the Ontario Early Researcher Award (2009), a Cheriton Faculty Fellowship (2013), an NSERC Discovery Accelerator Award (2014), and a Google Faculty Award (2014), and he is an ACM Distinguished Scientist. Ihab is an elected member of the VLDB Endowment board of trustees and an associate editor of the ACM Transactions on Database Systems (TODS). He received his Ph.D. in computer science from Purdue University, West Lafayette.

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