Continuous Data Cleaning
Fei Chiang
Department of Computing and Software
McMaster University
http://www.cas.mcmaster.ca/~fchiang/
Date and Location: Wednesday, April 2nd,
2014, 2:00pm - 3:00pm @ SB 204.
Abstract
In declarative data cleaning, data semantics are encoded as
constraints and errors arise when the data violates the constraints.
Various forms of statistical and logical inference can be used to
reason about and repair inconsistencies (errors) in the data.
Recently, unified approaches that repair both errors in data and
errors in semantics (the constraints) have been proposed. However,
both data-only approaches and unified approaches are by and large
static in that they apply cleaning to a single snapshot of the data
and constraints.
In this talk, I will present a continuous data cleaning framework
that can be applied to dynamic data and constraint environments. Our
approach permits both the data and its semantics to evolve and
suggests repairs based on the accumulated evidence to date.
Importantly, our approach uses not only the data and constraints as
evidence, but also considers the past repairs chosen and applied by
a user (user repair preferences). I will then describe details of a
repair classifier that predicts the type of repair needed to resolve
an inconsistency and learns from past user repair preferences to
recommend more accurate repairs in the future.
Biography
Fei Chiang is an Assistant Professor in the Department of Computing
and Software at McMaster University. Her research interests are
broadly in the area of data management, with a focus on data
quality, data cleaning, data privacy, and information extraction.
She received her M. Math from the University of Waterloo, and B.Sc
and PhD degrees from the University of Toronto, all in Computer
Science. She has worked at IBM Global Services, in the
Autonomic Computing Group at the IBM Toronto Lab, and in the Data
Management, Exploration and Mining Group at Microsoft Research.