ECML/PKDD 2013 Workshop:
Languages for Data Mining and Machine Learning

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.

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.

Workshop venue: Monday 23 Sept, room R6 at ECMLPKDD in Prague.

Program (tentative)

Informal online proceedings available as a single PDF file.

09:00--09:15	LML'13 Introduction by the organizers
09:15--10:15	Invited talk: Mobility mining query language Website Dino Pedreschi
10:15--10:30	A query language for constraint-based clustering Paper Website Antoine Adam and Hendrik Blockeel
10:30--11:00	Coffee Break
11:00--11:30	Declarative In-Network Sensor Data Analysis Paper George Valkanas, Ixent Galpin, Alasdair J.G. Gray, Alvaro A. A. Fernandes, Norman W. Paton and Dimitrios Gunopulos
11:30--12:00	Mining (Soft-) Skypatterns using Constraint Programming Paper Willy Ugarte Rojas, Patrice Boizumault, Samir Loudni, Bruno Cremilleux and Alban Lepailleur
12:00--12:30	Query Rewriting for Rule Mining in Databases Paper Website Brice Chardin, Emmanuel Coquery, Marie Pailloux and Jean-Marc Petit
12:30--14:00	Lunch Break
14:00--14:30	A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database Paper Jean-Philippe Métivier, Samir Loudni and Thierry Charnois
14:30--15:00	The representation of sequential patterns and their projections within Formal Concept Analysis, Paper Aleksey Buzmakov, Elias Egho, Nicolas Jay, Sergei O. Kuznetsov, Amedeo Napoli and Chedy Raïssi
15:00--15:15	Language of Conclusions and Formal Frame for Data Mining with Association Rules Paper Website Jan Rauch
15:15--15:30	Lower and upper queries for graph-mining Website Amina Kemmar, Yahia Lebbah, Samir Loudni and Mohammed Ouali
15:30--16:00	Coffee Break
16:00--16:30	API design for machine learning software: experiences from the scikit-learn project Paper Website Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Müller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake Vanderplas, Arnaud Joly, Brian Holt and Gaël Varoquaux
16:30--16:45	ParaMiner: A generic pattern mining algorithm for multi-core architectures Paper Website Benjamin Negrevergne, Alexandre Termier, Marie-Christine Rousset and Jean-François Méhaut
16:45--17:00	A declarative query language for statistical inference (extended abstract) Paper Gitte Vanwinckelen and Hendrik Blockeel
17:00--17:30	Discussion session

Old submission information

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 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.

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

• unpublished works (max 15 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 4 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.
Submissions (except oral-only) should follow the LNCS formatting guidelines. For extended abstracts, please add [Extended Abstract] to the title in the submission system; for the oral-only track please add [Oral Only].

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

PC members

• Jean-Marc Petit
• Barry O'Sullivan
• James Cussens
• Jean-Francois Boulicaut
• Kristian Kersting
• Hendrik Blockeel
• Lakhdar Sais
• Sergei O. Kuznetsov
• Dino Pedreschi
• Arnaud Soulet
• Sameer Singh
• Avi Pfeffer
• Elisa Fromont
• Guy Van Den Broeck
• Noah Goodman
• Saso Dzeroski
• Christel Vrain
• Yuri Malitsky
• Nadjib Lazaar
• Mirco Nanni
• Lars Kotthoff
• Salvatore Ruggieri