Fwd: [SIG-IRList] Mining Urban Data 2 - Workshop at ICML

[Glen Newton]

---------- Forwarded message ----------
From: Ioannis Katakis <[hidden email]>
Date: Sat, Apr 11, 2015 at 2:33 PM
Subject: [SIG-IRList] Mining Urban Data 2 - Workshop at ICML
To: [hidden email]


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Mining Urban Data 2 - ICML 2015 Workshop - Deadline: May 1st, 2015
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Call for Papers - MUD2 - 2nd International Workshop on Mining Urban
Data - Emerging Learning Paradigms and Applications for Smart Cities

URL: http://www.insight-ict.eu/mud2

[We apologize if you receive multiple copies of this email]

 * Important Dates
Please, refer to http://www.insight-ict.eu/mud2 for up-to-date information.
- Paper submission: May 1, 2015
- Notification: May 10, 2015
- Camera-ready: June 15, 2015
- Workshop day: July 10 or 11, 2015 (TBA)

 * Smart Cities and Urban Data

We are gradually moving towards a smart city era. Many innovative
applications arise daily utilizing massive urban data streams.
Technologies that apply machine learning algorithms to urban data will
have significant impact in many aspects of the citizens' everyday
life. Examples of such applications include managing disastrous
events, understanding the city's sentiment and opinion, tracking
health issues, monitoring crucial environmental factors as well as
improving energy efficiency and optimizing traffic. Unfortunately,
urban data have some characteristics that hinder the state of the art
in  machine learning algorithms. Such are diversity, privacy,
distributed and partitioned data, lack of labels, noise, complimentary
of multiple sources and requirement for online learning. Many smart
city applications require to tackle all these problems at once. This
workshop aims at discussing a set of new Machine Learning applications
and paradigms emerging from the smart city environment.

We especially welcome contributions based on data that can be reused
by the community, and we plan to make a list of these data sets
available on the workshop website. For researchers who would like to
try their hand on smart city data, data sets from the city of Dublin
are already listed on the workshop website.

A non-exhaustive list of challenges posed by smart cities data and
welcomed at the workshop include:

- Online learning - data generated from sensors can only
partially/temporarily be stored. Thus, a major requirement is to
process and analyse them as they arrive from the sources. Algorithms
should be on-line and adaptive.

- Large Scale Learning - the massive volume of data demands
distributed / parallel processing technologies. Other issues include
the complexity of the data coming from different sources with
different spatial and temporal references or granularity.

- Learning in Mobile Environment - special techniques are required for
storing and learning in mobile environments.

- Heterogeneous Data and Information Fusion - in many smart-city
applications, different types of information (GPS, weather, Twitter,
traffic data) should be analysed and combined in order to draw
conclusions.

- Learning with Social media - the main issue in mining micro-blogging
data (e.g. Twitter) is that the text is very short, cursorily written
and in different languages.

- Event Detection - a very interesting research issue that arises from
such data is the identification of real world events (e.g. "traffic
jam", "accident", "flood", "concert").

- Learning with Uncertain/Noisy Data - data generated by a smart city
are typically very noisy. Uncertainty management procedures as well as
crowdsourcing techniques might be required in order to aid the data
models disambiguate the information.

- Learning without Labels - with the size of the data sets and the
associated area, labeling the full data set can be prohibitively
expensive. Therefore, learning must typically be done with originally
no or extremely few labels. Semi-supervised or active learning
approaches could therefore be very interesting for such applications.

- Computer vision - CCTV cameras are a rich source of information.
They can be used to count pedestrians, detect accidents, security etc.

 * Applications

MUD will focus on presenting novel approaches that target some of the
following applications: a) Traffic Management, b) Public Transport
Adjustment, c) Accident Prevention, d) Resource Allocation, e) Energy
Efficiency, f) Sentiment Analysis, g) Environment.

 * Invited Speakers
 To be announced

 * Organizers

 - Ioannis Katakis, National & Kapodistrian University of Athens
 - Francois Schnitzler, The Technion
 - Thomas Liebig, TU Dortmund University
 - Gennady Andrienko, Fraunhofer IAIS and City University London
 - Dimitrios Gunopulos, National & Kapodistrian University of Athens
 - Shie Mannor, The Technion
 - Katharina Morik, TU Dortmund University

 The workshop is organized by the consortium of the EU Project
INSIGHT. For more information please visit www.insight-ict.eu

 * Submission

Submission is done through EasyChair:
https://easychair.org/conferences/?conf=mud2

Submissions should follow the ICML 2015 format
(http://icml.cc/2015/?page_id=151) and can be up to eight pages long,
excluding references. Additional appendices are allowed. Papers do not
need to be anonymized.
All submissions will be reviewed by at least two independent
reviewers. The conference proceedings will be published as CEUR
Workshop Proceedings (CEUR-WS.org) and will be indexed by DBLP.
Accepted papers will be either presented as a talk or poster. We
welcome submissions on novel research work as well as extended
abstracts on work recently published or under review in another
conference or journal. With respect to other (previous or future)
publications, authors can choose to have an extended abstract (1-2
pages) included in the proceedings instead of the actual reviewed
paper.

Please find more information at the website: www.insight-ict.eu/mud2/
ICML 2015 web site: http://icml.cc/2015/

If you have any question about the workshop, you can send an email to
[hidden email]

Ioannis Katakis,
Francois Schnitzler,
Thomas Liebig,
Gennady Andrienko,
Dimitrios Gunopulos,
Shie Mannor,
Katharina Morik



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