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Call for Papers - Data Mining Case Studies

Gabor Melli melli at sfu.ca
Mon Apr 11 00:11:37 EST 2005

KDD 2005 Workshop - KDD Success Stories and Practice

                    CALL FOR PAPERS

  The First Annual Workshop on Knowledge Discovery and
             Data Mining Success Stories 
        and introducing The KDD Practice Prize


>From its inception Knowledge Discovery and Data Mining
(KDD) has been
guided by the need to solve practical problems. This
is reflected in
the innovative creation of the Industrial Track and
KDD Cup at the
annual ACM SIGKDD conference. Yet because of length
confidentiality, and other restrictions, few articles
a completed implementation or what we will term a
"case study". The
small number of case studies are made up for by their
Anecdotally, case studies are one of the most
discussed topics at KDD.
It is only human to favor the telling of stories.
Stories can capture
the imagination and inspire researchers to do great
things. The
benefits of good case studies include:

1. Education: Case studies help to build
2. Inspiration: Case studies inspire future KDD
3. Public Relations: Applications that are socially
beneficial, and
   even those that are just interesting, help to raise
awareness of 
   the positive role that data mining can play in
science and society.
4. Problem Solving: Case studies demonstrate how whole
problems can 
   be solved. Often 90% of the effort is spent solving
   prediction algorithm related problems. 
5. Connections to Other Scientific Fields: Completed
KDD systems often
   exploit methods and principles from a wide range of
   areas. Fostering connections to these fields will
benefit KDD 
   academically, and will assist KDD practitioners to
learn how to
   harness these fields to develop successful


It is our pleasure to announce the establishment of
the first in a
series of workshops that will focus on successful KDD
The workshop series will be entitled "KDD Success
Stories". These
workshops will highlight KDD implementations that have
been responsible
for a significant and measurable improvement in
business operations,
or an equally important scientific discovery, or some
other benefit
to humanity. KDD Success Stories organizing committee
members will
reserve the right to contact the deployment site and
validate the
various facts of the implementation.

Some examples of KDD Success Stories include: (a) a
description of
how KDD techniques were able to identify a gene
involved in cancer,
(b) Description of a system that autonomously hedges
funds in on-
line auctions, (c) Description of a deployed Customer
Management system that significantly increased the
of the deployment organization.

KDD Success Stories will allow papers greater latitude
in (a) range
of topics - authors may touch upon areas such as
operations research, inventory control, and so on, (b)
page length -
longer submissions are allowed, (c) scope - more
complete context,
problem and solution descriptions will be encouraged,
(d) prior
publication - if the paper was published in part
elsewhere, it may
still be considered if the new article is
substantially more
detailed, (e) novelty - often successful KDD
practitioners utilize
well established techniques to achieve successful
and allowance for this will be given.

Unsuccessful KDD systems and "war stories" may also be


The KDD Practice Prize will be awarded each year to
the best
submitted KDD Success Story paper. The prize will be
for work that has had a significant and quantitative
in the application in which it was applied, or has
benefited humanity. Detailed rules and regulations can
be found
on the KDD Success Stories web site,

Eligibility: All papers submitted to KDD Success
Stories will
be eligible for the KDD Practice Prize, with the
exception of
the KDD Practice Prize Committee. Eligible authors
consent to
allowing the Practice Prize Committee to contact third
and their deployment client in order to independently
their claims.

Award: The names of winners and runners up will be
printed in
SIGKDD Explorations. Authors of the winning Practice
ation will also be asked to write a summary of their
paper in
SIGKDD Explorations. A plaque will be awarded to the
authors on
the winning paper. We will forgo a public awards
ceremony until
the workshop builds a following. In this first year, a
awards ceremony will be held at the conference with
the winners
where the awards will be presented and photos taken.

Justification: The KDD Practice Award will provide an
for companies to overcome confidentiality restrictions
and enter
high quality and descriptive papers to the KDD
Practice workshop.
It will also reward significant practical achievements
by KDD
practitioners who may not otherwise have a forum for
sional reward. 


Most operational industrial and scientific systems
that involve
data mining to some extent are likely to be acceptable
for the
Success Stories workshop and Practice Prize. Systems
that are
responsible for mission critical decisions or
cash-flow will be
particularly good candidates. If you are unsure as to
the suita-
bility of your paper, please contact the organizers at
at the kddsuccessstories.com website. Topics include
but are not 
limited to:

 - Genomics
 - Inventory control
 - Customer Relationship Management (CRM)
 - ShopBots
 - Recommendation systems
 - Auction trading systems
 - Clinical patient monitoring
 - Seismic Data interpretation
 - Survival analysis for medical procedures
 - Climate analysis
 - Correlates of genes with disease
 - Dangerous Drug interactions
 - Law enforcement applications
 - Search Engine Marketing
 - Food spoilage elimination
 - Price optimization
 - Data visualization in mission-critical user
 - Text understanding

 - Deadline for submissions	June 1, 2005
 - Notification of acceptance	July 9, 2005
 - Camera ready paper submission	August 1, 2005
 - Practice Prize announcement	August 21, 2005

 - Prize money: The ACM is sponsoring the following
prize purse:
 - $500 for first place winner
 - $300 for second place winner
 - $200 for third place winner

AWARD: The cash prize and plaques will be presented to
the winners in
a private award ceremony at the conclusion of the
Success Stories
workshop. Winners will also have their names printed
Explorations, and be invited to write a summary of
their article.

Email the following to submissions at the website
by June 1, 2005
 1. Article not more than 20 pages double spaced
 2. Author names, addresses, affiliations, phone
numbers and email
 3. Three persons who use the system in their day to
day activities,
    or are responsible for the system, and who may be
contact to 
    validate the claims made in the paper. Ideally
these individuals 
    belong to a different company than the authors.
Also, ideally 
    these individuals are not personal acquaintances
or friends of 
    the authors. 
 4. Nature of relationship of each contact to the
system and authors.
 5. Any information of relevance to contacting
deployment users.

 1. Word limits: Word limits will be relaxed for
submission to KDD
 Success Stories, so that participants may explain
their problem and
 solution in as much detail as necessary to both
captivate the reader
 and explain the solution. The maximum submission page
length will
 be 20 double-spaced pages. Despite the longer page
length, articles
 will be critically assessed for relevancy, and
authors risk rejection
 if their articles do not keep the reader's interest.
In addition, the
 PC will look for ways to cut the article, and so any
 made by the PC for cutting the article will need to
be followed to
 prior to inclusion in the workshop program.

 2. Commercial product mentions: KDD Practice is not a
sales venue.
 References to commercial products will be carefully
scrutinized by
 our Program Committee for applicability. Where
possible the underlying
 techniques should be described. The purpose of
Practice KDD is to
 illustrate real applications with descriptions that
are concise and
 complete. Commercial software if introduced, should
be named briefly
 and then described at a technical level (eg. don't
mention that
 "SAS Neural Nets(TM) increased our forecast accuracy
by 20%" - instead
 say that you used 'SAS PROC Neural Net(TM)' which
implemented a 3-
 layer sigmoidal backpropagation model with 10 inputs,
4 hidden and
 1 output node, and this net increased forecast
accuracy by 20%".
 Any papers violating these ethics will be deemed
 If in doubt please contact the organizers prior to
submission. We
 will allow a single product mention along the lines
described above,
 and this should be sufficient for establishing
commercial credibility.

 3. Valid contact information for the company that
deployed the KDD
 system must be supplied to the Program Committee. The
 Committee should be afforded the right to contact
individuals that
 were the beneficiaries of the KDD system and ask them
 about the implementation. In particular, the claims
made in the
 Practice KDD submission will need to be verified.
Failure to provide
 factual or complete descriptions of results obtained
with the system,
 that are discovered through this fact checking
process, will result
 in forfeiture of KDD Prize and dismissal from the
conference. The
 KDD Prize Committee will endeavor to be discrete in
its contacts,
 so please inform us of any information we need to
know before
 contacting the KDD system users.

 4. Copyright: Authors will agree to allow the display
of their
 articles on the web. Authors should also agree to
allow their articles
 to be published in book form. If authors wish to opt
out of website or
 book publication, please contact the Workshop

 5. Confidentiality: The reviewing process will be

 - Brendan Kitts (co-chair), iProspect
 - Gabor Melli (co-chair), Simon Fraser University
 - Gregory Piatetsky-Shapiro, PhD., KDNuggets
 - Richard Bolton, PhD., KnowledgeBase Marketing, Inc.
 - Diane Lye, PhD., Amazon
 - Chris Johnson, University of Technology Sydney
 - David Freed, PhD., Nevada Scientific
 - John Elder, PhD., Elder Research
 - Kevin Hetherington, MITRE Corp.
 - Parameshvyas Laxminarayan, iProspect
 - Tom Osborn, PhD., Verism Inc.
 - Ed Freeman, Federal Home Loan Bank of Seattle
 - Martin Vrieze, Harborfreight 
 - Martin Ester, PhD., Simon Fraser University

 - http://www.kddsuccessstories.com


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