IDeAL Research Group |
Our research at the Intelligent
Decision and Data Analysis Lab (IDeAL) is driven
by the current unprecedented accumulation of real-time, massive databases. Examples
of these databases include, but are not limited to, real-time sales data from
retail superstores, scientific data, world-wide economic indicators, and data
collected from smart phones, amongst others.
We apply the
end results of our research within a number of diverse domains, including
anthropometry and health care.
Contact:
Please contact us at hviktor{at}uottawa.ca if you are
interested in our research. We are
associated with the TAMALE group here
at the University of Ottawa.
Current projects include the
following:
-
Mining
real-time data streams. We are developing data mining algorithms to build
just-in-time models to facilitate real-time decision making against database streams. We follow an
incremental, any-time learning paradigm, where the models evolve as the data
changes. We favour a “user-in-the-loop” paradigm, where we use active learning
techniques to choose the best training instances for the users.
-
Location-aware
data cube construction and mining. Our goal is to answer the following
question: “Given that we have a very large database with multiple users who
require relevant and up to date information. If the user’s profile, location and
situation are known, what should be done differently?” To this end, we are creating a system
that dynamically computes that specific data cube that is highly relevant, from a particular user’s location,
current needs and perspective. Further, we are implementing a recommender
system that uses this reduced cube to provide the user with personalized,
situation-aware recommendations as she travels.
-
We
are also collaborating with the National Research Council of Canada (NRC) on a proteomics
initiative. In this work,
we are investigating the use of data
mining and computational intelligence techniques for rational drug design.
-
Fourthly,
we have started working on green database management and data mining, where our
aim is to develop sustainable algorithms that guarantee fast
answers while minimizing power consumption.
The following past projects has been completed successfully.
-
Relational database mining refers to the problem setting where data
resides in multiple tables (or relations)
as contained in a relational database. Consider a database containing Terabytes
or Petabytes of data. In this case, the evaluation of a hypothesis may involve
hundreds of thousands of tuples spread over multiple tables, leading to
computationally expensive multiple joins, which cannot assume the use of main
memory. Furthermore, the current state-of-the art, involve object-relational
databases which contain also multimedia
content such as 2D images or 3D objects. We have developed the so-called IDeAL2 utility-based
environment to directly mine data as contained in
object-relational databases, focusing on techniques for classification and
clustering.
-
Finding
clothes that fit. In the apparel
industry, an important challenge is to produce garments that fit various
populations well. However, repeated studies of customers’ levels of
satisfaction indicate that this is often not the case. The following questions
come to mind. What, then, are the typical
body profiles of a population? Are there significant differences between
populations, and if so, which body measurements need special care when
e.g. designing garments for Italian females? Within a population, would it be
possible to identify the measurements that are of importance for different
sizes and genders? Furthermore, assume that we have access to an accurate
anthropometric database. Would there, then, be a way to guide the data mining process to discover only those body measurements
that are of the most interest for
apparel designers? To this end, we are investigating new approaches to
explore a database, containing anthropometric
measurements and 3-D body scans, of samples of the North American, Italian
and Dutch populations.
-
Preserving
software dependent data over a very long time. The rapid changes in technology
in general, and in Internet-related technologies in particular, make the
long-term preservation of e-data an important challenge. Our objective was to better understand the intrinsic subtleties when
preserving e-data over 50 years or more.
To this end, our research aimed to creating an environment to study the
long-term preservation of e-data. We focused our attention on preserving
multimedia and relational data, which were dependent on software components,
for future use. The end result of this research resulting in the IDeaL long-term experimental environment, containing a
persistent data webhouse, together with archiving and
indexing, retrieval and trend analysis modules for handling the evolving
e-data.
-
Managing and
exploring Cultural Heritage repositories.
We studied the efficient management and exploration of very large
repositories of 2D images and 3D objects for the modelling and reconstitution
of complex heritage sites, and
applied our methodology to a variety of real cases.
Collaborators and Sponsors:
-
IBM Canada
-
National Research Council of Canada (NRC)
-
University of Bari, Italy
-
Telfer School of
Management at the University of Ottawa
-
Canada Foundation for Innovation (CFI)
-
Ontario Innovation Trust (OIT)
-
Ontario Research Network for E-Commerce (ORNEC)
-
National Science and Engineering Research Council
(NSERC) of Canada