Enhui Huang: Optimization for Active Learning-based Interactive Database Exploration

At 3pm on Feb 22, 2019, in the Thomas Flowers room of Inria Saclay,
Enhui Huang (CEDAR) will present:

TITLE: Optimization for Active Learning-based Interactive Database

ABSTRACT: There is an increasing gap between fast growth of data and
limited human ability to comprehend data. Consequently, there has been a
growing demand of data management tools that can bridge this gap and help
the user retrieve high value content from data more effectively. In this
work, we aim to build interactive data exploration as a new database
service, using an approach called “explore-by-example”. In particular, we
cast the explore-by-example problem in a principled “active learning”
framework, and bring the properties of important classes of database
queries to bear on the design of new algorithms and optimizations for
active learning-based database exploration. These new techniques allow the
database system to overcome a fundamental limitation of traditional active
learning, i.e., the slow convergence problem. Evaluation results using
real-world datasets and user interest patterns show that our new system
significantly outperforms state-of-the-art active learning techniques and
data exploration systems in accuracy while achieving desired eciency for
interactive performance.


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