ECML PKDD 2012: Last 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.
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