[Computational-biology] CFP Data Mining Case Studies Workshop Nov 27, 2005

Gabor Melli (KDD-2004) gmelli_kdd2004 at predictionworks.com
Sun Jul 17 16:14:15 EST 2005


                      Call For Papers

     The First Annual Workshop on Data Mining Case Studies  
     and introducing The Data Mining Practice Prize
           http://www.dataminingcasestudies.com


         New Orleans, Louisiana, November 27, 2005
                      In conjunction with
   ICDM'05: The Fifth IEEE Conference on Data Mining 2005



SYNOPSIS

Data Mining Case Studies, located at IEEE's Conference on Data 
Mining, showcases data mining success stories. The workshop will 
award the Data Mining Practice Prize to the best data mining 
implementation of the year, and bestow a variety of honors to 
winners and runners up including $1000 in prize money and a short 
article in the journal SIGKDD Explorations. 


MOTIVATION

>From its inception the field of Data Mining has been guided by the need
to solve practical problems. This is reflected in the establishment of
the Industrial Track at the annual Association for Computing Machinery
KDD conference, and popular, practical tutorials at IEEE's Data Mining 
Conference. Yet because of client confidentiality, few articles describe 
real-world success stories. The small number of success stories are made 
up for by their prominence. Anecdotally, case studies are one of the most 
discussed topics at data mining conferences. 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 understanding.

 2. Inspiration: Case studies inspire future data mining research.

 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 non-
    prediction algorithm related problems. 

 5. Connections to Other Scientific Fields: Completed data
    mining systems often exploit methods and principles from
    a wide range of scientific areas. Fostering connections
    to these fields will benefit data mining academically,
    and will assist practitioners to learn how to harness
    these fields to develop successful applications.


THE WORKSHOP

It is our pleasure to announce the establishment of the first in
a series of workshops that will focus on successful data mining 
implementations. The workshop series will be entitled "Data
Mining Case Studies". These workshops will highlight data mining 
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. 
Data Mining Case Studies organizing committee members will reserve 
the right to contact the deployment site and validate the various 
facts of the implementation.

Some examples of Data Mining Case Studies include: (a) a description 
of how Data mining 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 
Relationship Management system that significantly increased the 
profitability of the deployment organization.

Data Mining Case Studies will allow papers greater latitude in (a) 
range of topics - authors may touch upon areas such as optimization, 
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 data mining practitioners 
utilize well established techniques to achieve successful
implementations and allowance for this will be given.

Unsuccessful data mining systems and "war stories" may also be described.


THE DATA MINING PRACTICE PRIZE

The Data Mining Practice Prize will be awarded each year to the best 
Data Mining Case Study paper. The prize will be awarded for work
that has had a significant and quantitative impact in the application
in which it was applied, or has significantly benefited humanity.
Detailed rules and regulations can be found on the Data Mining Case
Studies web site http://www.dataminingcasestudies.com.

Eligibility: All papers submitted to Data Mining Case Studies will be 
eligible for the Data Mining Practice Prize, with the exception of
the Data Mining Practice Prize Committee. Eligible authors consent to
allowing the Practice Prize Committee to contact third parties and
their deployment client in order to independently validate their claims.

Award: Winners and runners up can expect an impressive array of honors 
including (a) Plaque, (b) Prize money comprising $500 for first place, 
$300 for second place, $200 for third place, (c) Article summaries 
about each of the deployments, to be published in the journal SIGKDD 
Explorations, which will also announce the results of the competition
and the prize winners, (d) Awards Dinner with organizers and prize winners.

Sponsors: We wish to thank our Gold Sponsor, Elder Research Inc., for 
their generous donation of prize money, incidental costs, time and
support.


TOPICS

Most operational industrial and scientific systems that involve data 
mining to some extent are likely to be acceptable for Data Mining Case 
Studies. Systems that are responsible for mission critical decisions or 
cash-flow will be particularly good candidates. If you are unsure as to 
the suitability of your paper, please contact the organizers with your 
topic at the email address at the bottom of the page.
                           
Topics include but are not limited to

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


DATES

Notify organizers of intention to submit	Now
Draft submission (Optional - see note)		September 1, 2005
Final submission				October 1, 2005
Notification of acceptance			October 15, 2005
Camera ready paper				October 22, 2005

Note: We recommend authors submit a draft of their paper by 
September 1, so that we can begin the process of validating claims. 
The paper will not be reviewed - only the chairs will see the paper. 
If you cannot provide the article by this date, you may still 
submit by the final submission deadline


SUBMISSION INSTRUCTIONS

In order to contact the organizers, submit, or for any other
correspondence, please use the email address at the bottom of
the page.
 
1. Please email the organizers as early as possible with your 
   intention to submit. You may email your intent any time before 
   October 1, 2005.

2. If possible, please provide an optional draft of the article by 
   the draft submission date.  This draft will only be viewed by the 
   Chairs - it will not be given to the reviewers or affect the prize 
   competition. 

3. In addition, please provide us with 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. Provide your author names, addresses, affiliations, phone numbers 
   and email. Also note the nature of relationship of each contact 
   to the system and authors. Finally, provide any information of 
   relevance to contacting deployment users.

5. Please submit your completed article, not more than 20 pages in 
   IEEE Proceedings format to the email address above.Due to editing 
   requirements for the Workshop Proceedings, we strongly encourage 
   documents to be submitted in Microsoft Word format. A Microsoft 
   Word Formatting Template can be downloaded from 
   ftp://pubftp.computer.org/press/outgoing/proceedings/instruct.doc


GUIDELINES

1. Word limits: Word limits will be relaxed for submission to Data Mining 
   Case Studies, 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 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 recommendations 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: Data Mining Case Studies 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 Data 
   Mining Case Studies 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 inadmissible. 
   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 data mining 
   system must be supplied to the Program Committee. The Program Committee 
   should be afforded the right to contact individuals that were the 
   beneficiaries of the data mining system and ask them questions about the 
   implementation. In particular, the claims made in the paper 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 prize 
   and dismissal from the conference. The Prize Committee will endeavor to 
   be discrete in its contacts, so please inform us of any information we 
   need to know before contacting the 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 organizers.

5. Confidentiality: The reviewing process will be confidential. 


HOST CONFERENCE

IEEE International Conference on Data Mining (ICDM 2005) in New Orleans
http://www.cacs.louisiana.edu/~icdm05
 

ORGANIZING COMMITTEE

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
Simeon J. Simoff, PhD., University of Technology Sydney
Karl Rexer, PhD., Rexer Analytics
David Freed, PhD., Exa Corp.
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
 Kristen Stevensen, iProspect


FURTHER INFORMATION

http://www.dataminingcasestudies.com
Email us at submissions at data mining case studies.com without the spaces.





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