Efficiently Integrating Heterogeneous Data: Collaboration, Automation, and
Relaxation
Robert McCann
Database and Information Systems Lab
University of Illinois at Urbana-Champaign

Data integration systems provide a one-stop interface to multiple disparate
data sources.  Despite a lot of interest from both academia and industry over
the last decade, these systems are still built and maintained in a manual,
labor-intensive, and error-prone process.  As a result, deployment has been
limited.  In this talk I will describe my work in the AIDA project at
Illinois.  Namely, I present three approaches to reduce integration costs:
collaboration, automation, and relaxation.  First I discuss the application
of mass collaboration techniques to perform critical integration tasks at
little cost to the system builder (reminiscent of open-source software
efforts).  I then discuss the use of automatic techniques (e.g. anomaly
detection, machine learning) to reduce the high cost of system maintenance.
Lastly, I describe initial work on relaxing one fundamental cause of
integration costs - rigid structural requirements - resulting in IR-style
best-effort integration scenarios.  These techniques can help significantly
reduce integration costs, promoting system deployment and offering solutions
to build integration systems where not previously possible.