Workshop on Data Engineering for Inductive Learning

[Peter Turney]

CALL FOR PARTICIPATION: Workshop on Data Engineering for Inductive Learning
---------------------------------------------------------------------------

IJCAI-95, Montreal (Canada), August 19/20/21, 1995


Objective
---------

In inductive learning, algorithms are applied to data. It is
well-understood that attention to both elements is critical -- unless
instances are represented so as to make its generalization methods
appropriate, no inductive learner can succeed. In applied work, it is
not uncommon for practitioners to spend the bulk of their time
exploring and transforming data in efforts to enable the use of
existing induction techniques.

Despite widespread acceptance of these facts, however, research reports
normally give data work short shrift. In fact, a report devoted mainly
to the data in an induction problem rather than to the algorithms that
process it might well be difficult to publish in mainstream machine
learning and neural network venues. 

Our goal in this workshop is to counterbalance the predominant focus on
algorithms by providing a forum in which data takes center stage.
Specifically, we invite discussion of issues relevant to data
engineering, which we define as the transformation of raw data into a
form useful as input to algorithms for inductive learning. Data
engineering is a concern in industrial and commercial applications of
machine learning, neural networks, genetic algorithms, and traditional
statistics. Among others, papers of the following kind are welcome:

1. Detailed case studies of data engineering in real-world applications 
   of inductive learning.

2. Descriptions of data engineering techniques or methods that have
   proven useful across a number of applications.

3. Studies of the data requirements of important inductive learning
   algorithms, the specifications to which data must be engineered for
   these algorithms to function.

4. Reports on software tools and environments for data engineering,
   including papers on "interactive induction" algorithms.

5. Empirical studies documenting the effect of data engineering on the
   success of induced models.

6. Surveys of data engineering practice in related fields: statistics,
   pattern recognition, etc. (but not problem-solving or 
   theorem-proving).

7. Papers on constructive induction, feature selection and related
   techniques.

8. Papers on (re)formulating a problem, to make it suitable
   for inductive learning techniques. For example, a paper
   on reformulating the problem of information filtering as
   learning to classify.

This workshop will enable an overview of current work in data
engineering. Since the problem of data engineering has received
relatively little published attention, it is difficult to anticipate
the work that will be presented at this workshop. We expect that the
workshop will make it possible to see common trends, shared problems,
and clever solutions that we cannot guess at, given our current,
limited view of data engineering. We have allowed ample time for
discussion of each paper (10 minutes), to foster an atmosphere
that will encourage data engineers to share their stories and to seek
common elements. We aim to leave the workshop with a vision of the
research directions that might bring science into data engineering.


Participation
-------------

During the workshop, we anticipate approximately 14 presentations. Each
paper will be given 25 minutes, 15 minutes for presentation and 10
minutes for discussion. There will be at most 30 participants in the
workshop. If you wish to participate in the workshop, you may either
submit a paper or a description of work that you have done (are doing,
plan to do) that is relevant to the workshop. Papers should be at most
10 pages long. The first page should include the title, the author's
name(s) and affiliation(s), a complete mailing address, phone number,
fax number, e-mail, an abstract of at most 300 words, and up to five
keywords. For those who do not choose to submit a paper, a description
of relevant work should be at most 1 page long and should include
complete address information. Workshop participants are required to
register for the main IJCAI-95 conference.

All submissions (papers or descriptions of relevant work) will be
reviewed by at least two members of the organizing committee. Please
send your submissions to the contact address below. Submissions should
be PostScript files, sent by e-mail. Accepted submissions will be
available before the workshop through ftp. Workshop participants will
also be given copies of the papers on the day of the workshop.

In selecting the papers, the committee will aim for breadth of coverage
of the topics listed above. Ideally, each of the eight kinds of papers
listed above would have at least one representative in the workshop.
A paper with new ideas on data engineering will be preferred to a high-
quality paper on a familiar idea.

The workshop organizers plan to publish revised versions of selected
papers from the workshop. The papers would be published either as a
book or as a special issue of a journal.

The exact date for the workshop has not yet been decided by IJCAI.
The workshop is one day in duration and will be held on one of
August 19, 20, or 21.


Schedule
--------

Deadline for submissions:		March 31, 1995
Notification of acceptance:		April 21, 1995
Submissions available by ftp:		April 28, 1995
Actual Workshop:			August 19/20/21, 1995


Organizing Committee
--------------------

Peter Turney, National Research Council (Canada)
Cullen Schaffer, CUNY/Hunter College (USA)
Rob Holte, University of Ottawa (Canada)


Contact Address
---------------

Dr. Peter Turney
Knowledge Systems Laboratory
Institute for Information Technology
National Research Council Canada
Ottawa, Ontario, Canada
K1A 0R6
(613) 993-8564 (office)
(613) 952-7151 (fax)
peter at ai.iit.nrc.ca