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LML 2013 : Workshop on Languages for Data Mining and Machine Learning


When Sep 23, 2013 - Sep 23, 2013
Where Prague
Submission Deadline Jun 28, 2013
Notification Due Jul 19, 2013
Final Version Due Aug 2, 2013
Categories    computer science   machine learning   artificial intelligence

Call For Papers

Research in Data Mining and Machine Learning has progressed significantly in the last decades, through the development of advanced algorithms and techniques. In the past few years there has been a growing attention to the development of languages for use in data mining and machine learning. Such languages provide common buildings blocks and abstractions, and can provide an alternative interface to advanced algorithms and systems that can greatly increase the utility of such systems.

Languages in this workshop can range form query languages and modeling languages to domain specific languages and integration with existing programming languages. Examples include Alchemy, Chrism and ProbLog (probabilistic modeling and inference); Factorie (probabilistic modeling and factor graphs); Dyna (declarative weighted deduction); Learning-based Java (learning based programming); MSQL, Mine Rule, SIQL, SPQL, DMX (data mining query languages); and more.

The workshop aims to bring together researchers and stimulate discussions on languages for data mining and machine learning. Its main motivation is the believe that designing generic and declarative modeling languages for data mining and machine learning, together with efficient solving techniques, is an attractive direction that can boost scientific progress.

The workshop promotes work that goes beyond one particular subfield of machine learning or data mining. It promotes cross-fertilisation between computer language oriented research, across data mining and machine learning. The workshop encourages submissions inspired from other scientific disciplines such as logic in knowledge representation, query languages in databases, modeling languages in constraint solving and other formalisms such as mathematical programming.

Accepted formats

Our main goal is to stimulate discussion, collaboration and the sharing of experiences. In that respect, we would have three submission types:

- Unpublished works (max 8 pages, double submissions allowed). We allow a submitted or under review paper to also be submitted to the workshop. In this way, we offer authors reviews and (if accepted) discussion on their work among workshop participants.
- Extended abstracts and vision statements (max 2 pages). Short papers and vision statements are meant to be thought provoking and stimulate discussion.
- Recently published works (special oral-only track, no page limits). Part of the program will be for a short oral-only presentation of recently published work. The aim is to share experiences and lessons learned.

The submission system can be reached through this link:

List of topics

The following is a non-exclusive list of topics that fit the scope:

- Language constructs and abstractions for expressing mining/learning tasks
- Query languages for data mining
- Modeling languages for data mining/machine learning
- Declarative approaches to data mining/machine learning
- Language integration of databases and data mining systems
- Frameworks for supporting higher-level ML/DM languages
- Scalable and/or distributed computation of ML/DM language constructs
- Compilation/transformation/interpretation of high-level languages to existing ML/DM algorithms
- Domain specific languages for classes of mining and learning problems
- Logic as a language for data mining/machine learning
- Constraint-based languages for data mining/machine learning
- High-level (declarative) languages for data mining and machine learning problems
- Probabilistic programming langauges for data mining and machine learning
- Domain-specific languages for ML/DM applications
- Integration of logic and/or statistics in languages, for use in data mining/machine learning
- Generic languages and their integration with mining/learning techniques
- Learning-based programming languages

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