Building Scalable Machine Learning Solutions for Data Curation

Speaker:        Professor Ihab Ilyas
                Cheriton School of Computer Science
                University of Waterloo

Title:          "Building Scalable Machine Learning Solutions for Data

Date:           Thursday, 23rd November 2017

Time:           4:30pm - 5:30pm

Venue:          Room 1505 (near lift 25/26), HKUST


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. In this talk I discuss
why leveraging data semantics and domain-specific knowledge is key in
delivering the optimizations necessary for truly scalable ML curation
solutions. The talk focuses on two main problems: (1) entity
consolidation, which is arguably the most difficult data curation
challenge because it is notoriously complex and hard to scale; and (2)
using probabilistic inference to suggest data repair for identified errors
and anomalies using our new system called HoloCLean. Both problems have
been challenging researchers and practitioners for decades due to the
fundamentally combinatorial explosion in the space of solutions and the
lack of ground truth. There's a large body of work on this problem by both
academia and industry. Techniques have included human curation,
rules-based systems, and automatic discovery of clusters using predefined
thresholds on record similarity Unfortunately, none of these techniques
alone has been able to provide sufficient accuracy and scalability. The
talk aims at providing deeper insight into the entity consolidation and
data repair problems and discusses how machine learning, human expertise,
and problem semantics collectively can deliver a scalable, high-accuracy


Ihab Ilyas is a professor in the Cheriton School of Computer Science at
the University of Waterloo, where his main research focuses on the areas
of big data and database systems, with 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, elected
SIGMOD vice chair, and an associate editor of the ACM Transactions of
Database Systems (TODS). He holds a PhD in computer science from Purdue
University, West Lafayette.