posted by user: bofoghi || 2654 views || tracked by 3 users: [display]

IEEE MPPES 2013 : IEEE Workshop on Mining Performance Patterns in Elite Sports 2013

FacebookTwitterLinkedInGoogle

Link: http://vu.edu.au/icdm
 
When Dec 8, 2013 - Dec 8, 2013
Where Dallas, Texas, USA
Submission Deadline Aug 18, 2013
Notification Due Sep 4, 2013
Final Version Due Oct 15, 2013
Categories    data mining   sport   machine learning
 

Call For Papers

MPPES
IEEE ICDM Workshop

SUBMISSION DEADLINE EXTENDED TO AUGUST 18, 2013

Mining Performance Patterns in Elite Sports (MPPES) is the IEEE ICDM workshop on using advanced data analytics techniques for decision making in the elite sports domain. Sports performance analysis, as a means to create and analyse a valid record of athlete performances by using systematic observations, has gained importance in the last decade. It has been facilitated by advances in Information Technology and Digital Photography. In particular, machine Learning and Data Mining are currently used to analyse a variety of sports data. Mining elite sports performance patterns in data arising from international competitions such as World Championships and Olympic Games is now at the centre of attention for many researchers at the intersection of the two domains of Information Technology and Sports Science.

Most well-established data mining and machine learning techniques have been applied in modelling and mining sports data. From these, unsupervised learning (e.g., clustering), supervised learning (e.g., classification), relationship estimation (e.g., regression analysis), and rule mining (i.e., sequential patterns and association rules) techniques have widely been applied to a number of decision problems in the elite sports domain. However, such ad hoc and non-systematic single problem-oriented efforts have never been fully integrated in to generic problem type-centred solutions and structures.
The emerging field of Elite Sports Data Mining, therefore, still lacks a covering framework of techniques and guidelines for the different decision problems in this domain. On the other hand, although elite sports data inherit some unique characteristics (e.g., outlier performances often result in long-standing world records and are thus very interesting), specific data mining techniques and solutions have never been adapted in such a way to become sports domain-compatible. The latter issue is realized especially in the two main steps of sports data mining, namely, data staging (collection and pre-processing) and analysis.

The elite sports domain involves a number of rules, regulations, tactics, strategies, performance measures, conditions, abilities, and performance evaluation criteria related to each specific competition. These aspects, as well as decision problem types (e.g., performance pattern discovery and performance prediction), require careful consideration when applying any data staging techniques. Sports decision problems and problem types also play an important role in deciding what data mining or machine learning technique is suitable to be utilized in the analysis step. Understanding of what criteria to consider when selecting or adapting a specific data analytics technique for specific decision problem types in the elite sports is therefore an important step towards optimized utilization or development of efficient and effective techniques well-suited in this domain.

)))Topics
This workshop intends to provide a forum for researchers in the different fields of Machine Learning, Statistics, Probabilistic Reasoning, Data Mining, and Sport Science to discuss related topics regarding the applications, current challenges, and possibly the future of using advanced data analytics techniques in knowledge discovery and knowledge generation in elite sports. Topics of interest include but are not limited to:
• Overview of data mining in elite sports
• Frameworks and challenges to bring together data mining and sport science
• Application of existing data mining and machine learning techniques in sports data analysis
• Challenges/benefits of using major and current data mining and machine learning techniques in sports, i.e.,:
- Supervised learners
- Unsupervised learners
- Probabilistic learners/reasoners
- Rule miners
- Statistical relationship estimators
• Adapting data mining and machine learning techniques for sports data analysis
• Selecting specific data mining techniques for problem types in sports
• Outlier modelling in sports data
• Data staging (collection and pre-processing) for sports data mining
• Real-time data mining-based decision support tools in sports
• Visualization of sports performance patterns
• Video and image mining in sports
• Text mining (and its applications) in elite sports
• Preparation and interpretation of sports data mining results

)))Paper Submissions
We invite regular paper submissions, work-in-progress, demos, and position papers. All papers must follow the IEEE ICDM format and be submitted through the ICDM Workshop Submission Site.
Regular papers can be up to 8 pages in length; short papers, such as position and work-in-progress papers, no more than 6 pages; two additional pages can be purchased for $125 per page. All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to workshop topics, originality, significance, and clarity.

)))Important Dates
- Paper Submission: August 3, 2013
- Notification to Authors: September 24, 2013
- Workshop: December 08, 2013


)))Workshop Organizers
Bahadorreza Ofoghi, National ICT Australia and the University of Melbourne, Australia
John Zeleznikow, Victoria University, Australia
Clare MacMahon, Swinburne University, Australia
Dan Dwyer, Deakin University, Australia

)))Contact
For further questions regarding submissions and participation in the workshop, please contact one of the organizers below (include IEEE ICDM Workshop: MPPES in the subject line):

Dr. Bahadorreza Ofoghi: bahadorreza.ofoghi@nicta.com.au
Prof. John Zeleznikow: john.zeleznikow@vu.edu.au

Related Resources

ICDM 2024   IEEE International Conference on Data Mining
ECAI 2024   27th European Conference on Artificial Intelligence
AIKE 2024   7th IEEE International Conference on Artificial Intelligence and Knowledge Engineering
ACM-Ei/Scopus-CCISS 2024   2024 International Conference on Computing, Information Science and System (CCISS 2024)
BDCAT 2024   IEEE/ACM Int’l Conf. on Big Data Computing, Applications, and Technologies
IEEE COINS 2024   IEEE COINS 2024 - London, UK - July 29-31 - Hybrid (In-Person & Virtual)
UCC 2024   The IEEE/ACM International Conference on Utility and Cloud Computing
AIM@EPIA 2024   Artificial Intelligence in Medicine
HiPC 2024   31st IEEE International Conference on High Performance Computing, Data, and Analytics
CVIV 2024   2024 6th International Conference on Advances in Computer Vision, Image and Virtualization (CVIV 2024) -EI Compendex