ECML PKDD 2012 General Call for Workshop Papers

[Grigorios Tsoumakas]

[ please distribute - apologies for multiple postings ]

ECML PKDD 2012 General Call for Workshop Papers

The organizing committee of the ECML PKDD 2012 conference invites you
to submit your latest research to one of the 11 workshops that will be
held on 24th and 28th September. This year, ECML PKDD 2012 will
feature workshops on a variety of hot topics and will also include one
workshop associated with the Discovery Challenge as well as two
workshops that feature challenges. So besides providing you the
opportunity to present and discuss the latest developments and
applications in Machine Learning and Data Mining, these workshops will
enable you to put your best technology to the test.

The deadline for submission is June 29th (details below).

The European Conference on Machine Learning and Principles and
Practice of Knowledge Discovery in Databases (ECML PKDD) will take
place in Bristol, UK from September 24th to 28th, 2012. This event
builds upon a very successful series of 22 ECML and 15 PKDD
conferences, which have been jointly organized for the past 11 years.
ECML PKDD is the prime European scientific event in these fields. It
will feature presentations of contributed papers and invited speakers,
a wide program of workshops and tutorials on the first and last days,
a discovery challenge, and a DINe track with demo, industry, and
‘nectar’ talks.

Workshops website: http://www.ecmlpkdd2012.net/programme/workshops

Arno Knobbe & Carlos Soares
ECML PKDD 2012 Workshop Chairs


** Important Deadlines **

[for all workshops except the Discovery Challenge]

Deadline for submissions: June 29, 2012
Author notification: July 20, 2012
Camera-ready papers due: August 3, 2012
Workshops takes place: September 24 and 28, 2012

Details about the submission process for each workshop can be found at
the corresponding website.


** Monday Workshops (24th September 2012) **

* MUSE: Mining Ubiquitous and Social Environments
Martin Atzmueller and Andreas Hotho
[http://www.kde.cs.uni-kassel.de/ws/muse2012/]

The goal of this workshop is to promote an interdisciplinary forum for
researchers working in the fields of ubiquitous computing, social web,
Web 2.0, and social networks which are interested in utilizing data
mining in a ubiquitous setting. The workshop seeks for contributions
adopting state-of-the-art mining algorithms on ubiquitous social data.
Papers combining aspects of the two fields are especially welcome. In
short, we want to accelerate the process of identifying the power of
advanced data mining operating on data collected in ubiquitous and
social environments, as well as the process of advancing data mining
through lessons learned in analyzing these new data.

* NFMCP: New Frontiers in Mining Complex Patterns
Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco,
Elio Masciari and Zbigniew Ras
[http://www.di.uniba.it/~nfmcp2012/index.htm]

NFMCP aims at bringing together researchers and practitioners of data
mining interested in exploring emerging technologies and applications
where complex patterns in expressive languages are principally
extracted from new prominent data sources like blogs, event or log
data, biological data, spatio-temporal data, social networks, mobility
data, sensor data and streams, and so on. We are interested in
advanced techniques which preserve the informative richness of data
and allow us to efficiently and efficaciously identify complex
information units present in such data.

* Silver: The Silver Lining – learning from unexpected results
Joaquin Vanschoren and Wouter Duivesteijn
[http://datamining.liacs.nl/silver.html]

This workshop is dedicated to the proposition that insight often
begins with unexpected results. Unexpected results chart the
boundaries of our knowledge: they identify errors, reveal false
assumptions, and force us to dig deeper. Unfortunately, this process
is rarely mentioned in the machine learning and data mining discourse.
Indeed, there exists a publication bias that favors (incremental)
successes over novel discoveries of why some ideas, while intuitive
and plausible, do not work. With this workshop, we want to give a
voice to unexpected results that deserve wider dissemination:
thoroughly conducted studies that follow a plausible idea that did not
achieve the aspired results, but instead taught us novel lessons;
studies showing that well-known (successful) methods will not work
under certain conditions, highlighting remaining weaknesses and new
avenues of research; and stories that focus on how a successful method
was discovered after one or several failed attempts.

* IID: Instant Interactive Data Mining
Jilles Vreeken, Nikolaj Tatti, Bart Goethals, Anton Dries, Matthijs
van Leeuwen, Siegfried Nijssen
[http://adrem.ua.ac.be/iid2012/]

At IID’12 we will discuss data mining techniques that allow users to
interactively explore their data, receiving near-instant updates to
every requested refinement. While Instant mining and Stream mining
start from different perspectives and operate under different
constraints, there is a significant overlap in techniques and
developments in either setting can have a significant impact on the
other. Therefore, this workshop aims to bring together researchers
interested in instant and adaptive data mining methods, whether for
use in interactive systems or in the processing of large streams of
evolving data.

* LDSSB: Learning and Discovery in Symbolic Systems Biology
Oliver Ray and Katsumi Inoue
[https://www.cs.bris.ac.uk/~oray/LDSSB12/]

Symbolic Systems Biology is a rapidly emerging field involving the
application of formal logic-based methods to Systems Biology. Recently
a spectrum of such approaches have begun to demonstrate their utility
in modelling and analysing a variety of biological phenomena. Examples
include Boolean logic, classical logic, modal logics, hybrid logic,
rewriting logic, computational logics, constraint programming, formal
methods, process calculi, graphical models, and many more. The primary
aim of this workshop is to explore how machine learning and knowledge
discovery techniques can be used within such formalisms to help learn
and revise biological models. A secondary aim is to investigate how
symbolic methods can be combined with numerical techniques in order to
better handle noise and uncertainty in the real world.


** Friday Workshops (28th September 2012) **

* SDAD: Sentiment Discovery from Affective Data
Mohamed Medhat Gaber, Mihaela Cocea, Stephan Weibelzahl, Ernestina
Menasalvas and Cyril Labbe
[http://gaberm.myweb.port.ac.uk/sdad12/]

The current expansion of social media leads to masses of affective
data related to peoples’ emotions, sentiments and opinions. Knowledge
discovery from such data is an emerging area of research in the past
few years, with a potential number of applications of paramount
importance to business organisations, individual users and
governments. Data mining and machine learning techniques are used to
discover knowledge from various types of affective data such as
ratings, text or browsing data. Although research in this area has
grown considerably in the recent years, knowledge discovery from
affective data is in its infancy state with more open issues and
challenges which often require interdisciplinary approaches. This
workshop aims to bring together researchers in this area to present
their latest work, to discuss the challenges in the field and identify
where our efforts, as a research community, should focus.

* ALRA: Active Learning in Real-world Applications
Laurent Candillier, Max Chevalier and Vincent Lemaire
[http://www.nomao.com/labs/alra]

Machine learning indicates methods and algorithms which allow a model
to learn a behavior thanks to examples. Active learning gathers
methods which select examples used to build a training dataset for the
predictive model. All the strategies aim to use a set of examples as
small as possible and to select the most informative examples. When
designing active learning algorithms for real-world data, some
specific issues are raised. The main ones are scalability and
practicability. Methods must be able to handle high volumes of data,
and the process for labeling new examples by an expert must be
optimized. We encourage papers that describe applications of active
learning in real-world. The industrial context, the main difficulties
met and the original solution developed, shall be described.
Contributions on the associated Nomao challenge (http://www.nomao.com/
labs/challenge), that proposes such a practical application of active
learning, will also be welcome.

* I-Pat: Mining and exploiting interpretable local patterns
Henrik Grosskreutz, Stefan Ruping and Nikos Karacapilidis
[http://www.iais.fraunhofer.de/interpretable-patterns-workshop.html]

Local patterns, like itemsets, correlations, contrast sets or
subgroups, stand out from other data mining tools by their descriptive
nature, which makes them directly interpretable by end users like
clinicians, fraud experts or analysts. In this workshop, we wish to
investigate typical use cases and key requirements for the successful
usage of local pattern mining in applications where next to the
statistical performance of models, the understandability and
interestingness of the models is the key success factor.

* COMMPER: Community Mining and People Recommenders
Panagiotis Papapetrou, Jaakko Hollmen and Luiz Augusto Pizzato
[http://research.ics.tkk.fi/events/commper2012/]

Data mining and knowledge discovery in social networks has advanced
significantly over the past several years, due to the availability of
a large variety of offline and online social network systems. The
focus of COMMPER 2012 is on social networks with special focus on
community mining and people recommenders. Community minding involves
topics such as the analysis of scientific communities and
collaboration networks, including bibliometrics, and the formation of
teams. People recommenders focus on the all topics where recommender
systems are used to enable connections among users, such systems can
be found on all types of social networks such as photo sharing
websites, expert search, mentoring systems and online dating..

* CoLISD: Collective Learning and Inference on Structured Data
Balaraman Ravindran, Kristian Kersting, Sriraam Natarajan, S.
Shivashankar
[http://www.cse.iitm.ac.in/CoLISD/CoLISD.html]

Classical ML techniques assume the data to be iid, but the real world
data is inherently relational and can generally be represented using
graphs or some variants of them. The importance of modelling
structured data is evident from its increasing presence: WWW, social
networks, organizational network, image, protein sequence, relational
data etc. This field has been recently receiving a lot of attention in
the community under different themes depending on the problem
addressed and the nature of solution. Variants include iterative
classification, structured prediction, relational learning, etc. While
there are other issues such as learning the network structure, CoLISD
focuses on the within-network learning and inference tasks with
special emphasis on collective inference.

* ECML/PKDD 2012 Discovery Challenge: Third Challenge on Large Scale
Hierarchical Text Classification
Ion Androutsopoulos, Thierry Artieres, Patrick Gallinari, Eric
Gaussier, Aris Kosmopoulos, George Paliouras, Ioannis Partalas
[http://lshtc.iit.demokritos.gr/]

This year’s discovery challenge hosts the third edition of the
successful PASCAL challenges on large scale hierarchical text
classification. The challenge comprises three tracks and it is based
on two large datasets created from the ODP web directory (DMOZ) and
Wikipedia. The datasets are multi-class, multi-label and hierarchical.
The number of categories ranges between 13,000 and 325,000 roughly and
the number of documents between 380,000 and 2,400,000. The three
tracks are: 1) Standard large-scale hierarchical classification, 2)
Multi-task learning and 3) Refinement-learning.