Data Mining Practice Prize 2013
Call For Papers
Fifth Workshop on Data Mining Case Studies and Success Stories
and Fifth Data Mining Practice Prize
an ICDM 2013 workshop
Submit Your Case Study!
Please use the following email address to submit your data mining case study (note that the email address which follows has been encoded as a bitmap). Details on how to submit are provided below.
From its inception the field of data mining has been guided by the need to solve practical problems. Yet a cursory examination of the publications shows that few papers describe a completed implementation or what we will term a “case study”. The small number of case studies is counter-balanced by their prominence. Anecdotally case studies are one of the most discussed topics at data mining conferences. Some of the benefits of good case studies include
1. Inspiration: Case studies provide examples that can inspire data mining researchers to pursue important new technical directions.
2. Innovation: Data mining case studies demonstrate how whole problems were solved - not just part of the problem. Often building the prediction algorithm is only 10% of the problem - the other aspects that comprise a successful deployment are valuable for practitioners to understand.
3. Education: People are more likely to remember stories than facts.
4. Media Coverage: The media is more likely to report on completed data mining applications, than they are on isolated algorithms. We have an opportunity to present positive success stories to the wider community.
5. Public relations: Applications, particularly those that are socially beneficial, will help our perception both within the wider public and other scientific fields.
6. Connections to Other Scientific Fields: Completed systems knit together a range of scientific and engineering disciplines such as signal processing, chemistry, optimization theory, auction theory and so on. Fostering meaningful connections to these fields will benefit data mining academically, and will assist data mining practitioners to learn how to harness these fields to develop successful applications.
The Data Mining Case Studies Workshop and Practice Prize was established in 2005 to showcase the very best in data mining case deployments. Data Mining Case Studies continues into the fifth workshop and prize competition with ICDM-2013. Data Mining Case Studies 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.
Examples of Data Mining Case Studies from previous years have included: (a) a medical application that has save hundreds of lives by mining through hundreds of thousands of patient records to identify patients who have show all the signs for heart disease, yet have not been prescribed heart medication, (b) a system which has uncovered hundreds of millions in sheltered tax evasion rings, (c) a system which has raised revenue by improved cross-selling of computer peripherals and equipment.
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 – the use of established techniques to achieve successful implementations will be given partial allowance.
Unsuccessful data mining systems that describe lessons learned and “war stories” will also be assessed.
The Data Mining Practice Prize
Introduction: The Data Mining Practice Prize will be awarded for the best Data Mining Case Study submission. 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.
Eligibility: All papers submitted to Data Mining Case Studies will be eligible for the Data Mining Practice Prize, with the exception of any persons serving on the Data Mining Practice Prize Committee, in addition to memberes of Sponsoring companies. Eligible authors must consent to allowing the Practice Prize Committee independently validate their claims by contacting third parties and their deployment client for independent verification and analysis.
Award: Winners will receive a variety of honors including:
- Prize money $600 to be divided amongst winners.
- Awards Dinner with organizers and prize winners.
[more information about the Practice Prize]
Most operational industrial and scientific systems that involve data mining to some extent are likely to be acceptable. Systems that are responsible for mission critical systems, medical applications, cash flow, or applications that significantly benefit humanity 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
- Inventory control
- Customer Relationship Management (CRM)
- 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 interfaces
- Text understanding
Notify organizers of intent to submit
Optional Draft submission including client contact information*
Submission including client contact information if it has not already been provided
Notification of acceptance
Camera ready paper submission
Workshop held, Practice Prize winners announced
*We recommend authors submit a draft of their paper by May 26, 2013 so that we can better begin the process of validating claims. Only the chairs will see the paper - it will not be seen by the reviewers.
[more about submission dates]
Submit Your Case Study!
In order to contact the organizers, submit, or for any other correspondence, please use the following email address
(note that the email address which follows has been encoded as a bitmap)
- Please email the organizers as early as possible with your intention to submit.
- If possible, it is recommended that you 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.
- Please provide email the organizers 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.
- 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.
- Please submit your completed article, 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. The official IEEE Microsoft Word template is available from here.
- In addition to the above steps, please also follow the instructions on the IEEE author kit website to submit a pdf version of your paper to the official ICDM Conference site .
Word limits: The maximum submission page length is 10 pages.
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.
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.
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.
Confidentiality: The reviewing process will be confidential.
IEEE International Conference on Data Mining (ICDM), December 8-11, 2013, Dallas, Texas.
(photo credit: visitdallas.com)
[more about this year's venue]
Gabor Melli (co-chair), PhD, VigLink
Brendan Kitts (co-chair), PrecisionDemand
Gregory Piatetsky-Shapiro, PhD, KDNuggets
Ashok Srivastava, PhD, NASA
Claudia Perlich, PhD, Media6Degrees
Alejandro Correa Bahnsen, University of Luxembourg
Ying Li, PhD, Concurix
Wei Ding, PhD, University of Massachusetts
Francoise Soulie-Fogelman, PhD, KXEN
Peter van der Putten, PhD, Leiden University and Pegasystems
Karl Rexer, PhD, Rexer Analytics
David Duling, PhD, SAS
Ed Freeman, DS-IQ
Diane Lye, PhD, Citi
Michael Berthold, PhD, KNIME
Jing Ying Zhang, PhD, Microsoft
Dyng Au, PrecisionDemand
Sarmila Basu, Microsoft
Ankur Teredesai, PhD, University of Washington
Michael Zeller, Zementis
Gang Wu, PhD, Twitter
Dean Abbott, PhD, Abbott Analytics
Parameshvyas Laxminarayan, iProspect
Richard Bolton, PhD, KBM Group
Wesley Brandi, PhD, iPensatori
[biographical information on the organizers]
[more information on our sponsors]