[Geo4All] CfP: 1st Int. Workshop on Big Geo Data Quality and Privacy (BIGQP) @ EDBT/ICDT 2017

[Dimitris Kotzinos]

Dear all,

I am co-organizing a workshop at the coming EDBT conference and I think
that it is of interest to the Geo4All community. You are all very
welcome to submit relevant work and participate to it.
If there are any questions on the workshop please do not hesitate to
contact me.

Best regards,
Dimitris

-- 
Dimitris Kotzinos
Professor
Head MIDI team
Lab. ETIS (ENSEA/UCP/CNRS UMR 8051)
& Dept. Sciences Informatiques, Université de Cergy-Pontoise
2 av. Adolphe Chauvin
Site Saint Martin, bureau A561
95000 Pontoise
France
phone: +33 13425 2855
e-mail: Dimitrios.Kotzinos at u-cergy.fr


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BIGQP'17 1st Call for Papers
===============================================================================
1st International Workshop on Big Geo Data Quality and Privacy (BIGQP 2017)
March 21, 2017 - Venice, Italy
http://www-etis.ensea.fr/BigGeoQ-UP/BIGQP2017

Co-located with EDBT/ICDT 2017 Joint Conference
March 21-24, 2017 - Venice, Italy
http://edbticdt2017.unive.it/
===============================================================================

WORKSHOP DESCRIPTION
Big Geo Data are becoming a significant part of the data production that
occurs
today at a global scale. They are to a big extent crowdsourced by users
who do
not follow a well-documented ("scientific") method that ensures data
quality,
either because they do not know or do not care about the issue. This kind of
data usually contain references to locations, i.e., Points of Interest
(POIs),
and become accessible in general social media (e.g., Facebook, Google+)
or in
specialized platforms (e.g., Open Street Maps, Yelp). Location information
could be either extracted by personal assistants (e.g., Google Now) or
social
platforms (e.g., Facebook, Twitter) in terms of places visited, trajectories
pursuit or mentioned by the users, along with their social posts.
Information
extraction techniques enable us to analyze a wealth of geospatial and
temporal
information available in social posts such as spatial objects and the
way they
are spatially, temporally and/or semantically related (e.g., north,
in-between,
during, same-as). Spatial objects may refer to precise and/or imprecise
geographical objects (e.g., POIs, toponyms, and vernacular names), as
well as
to implicit spatial objects identified by means of textual descriptions (for
instance, the following user post could identify a part of a certain road at
the point of publication: "traffic jam between POI 'A' to POI 'B'"). The
quality of crowdsourced geo data might vary depending on the origin (machine
vs human generated), the level of detail of the extraction techniques,
as well
as the obfuscation techniques used by the persons themselves or the social
media platforms to protect their privacy. Another aspect of quality is
associated with the credibility of the extracted information with respect to
one's location or time of publication (e.g., user post mentioning an
event just
after it has happened although the user's and event's locations are
spatially
unrelated).

The quality (e.g., precision, accuracy, consistency) of geospatial
information
can be improved when personal data are integrated from several data sources
(social networks, geographical authorities). On the other hand, the
combination
of such personal data might reveal sensitive information regarding users'
location and might put users' location privacy (also known as geoprivacy) at
risk. As a matter of fact, location information is inextricably linked to
personal safety. Unrestricted access to information about an individual's
location could potentially lead to harmful encounters, for example
stalking or
physical attacks. Moreover, location constrains our access to spatiotemporal
resources, like meetings, medical facilities, our homes, or even crime
scenes.
Hence, it can be used to infer other personal sensitive information,
such as an
individual's political views, state of health, or personal preferences.
Understanding the different aspects of geographic/geometric/geospatial
quality
involved in crowdsourced geo data and evaluate the privacy risks
introduced by
enhancing their quality in personal, social and urban applications is a
challenging topic.

The BIGQP workshop aims to be a premier venue in gathering computer
science and
geoscience researchers who are contributing to and are interested in
both Data
Quality and Privacy of Big Geo Data. Hence, it is a unique opportunity
to find
in a single place up-to-date scientific works on both subjects that have
so far
only partially been addressed by different research communities such as Data
Quality Management, Distributed and Mobile Systems, and Big Data Privacy.

Topics of interest include, but are not limited to:
* Quality of online location data
* Extraction of spatial relations in Big Data
* Extraction of spatial objects from textual Big Data
* Quality metrics of Big Geo Data
* Geo entities resolution and linking
* Geo data inconsistency detection and repairing
* Geo-analytics in data quality and user privacy
* Human mobility patterns in crowdsourced Geo data
* User privacy and personal location information
* Data Quality-based Privacy models
* Privacy masking and anonymization
* Tools and Applications

PROCEEDINGS AND PAPER SUBMISSION
Interested authors may submit papers of 4 pages or 8 pages. All papers
should
be formatted according to the ACM SIG Proceedings double-column template
(http://www.acm.org/sigs/publications/proceedings-templates) and be
submitted
to the workshop's EasyChair page at
https://easychair.org/conferences/?conf=bigqp2017. All workshop papers
will be
published online at CEUR (http://ceur-ws.org/).

IMPORTANT DATES
Paper submission:             November 14, 2016
Notification of acceptance:   December 20, 2016
Camera-ready version:         January 14, 2016
Workshop:                     March 21, 2017

WORKSHOP CO-CHAIRS
Dimitris Kotzinos, ETIS Lab - University of Cergy Pontoise, France
Vassilis Christophides, INRIA, France
Charalampos Nikolaou, University of Oxford, UK
Yannis Theodoridis, University of Piraeus, Greece

PROGRAM COMMITTEE
Natalia Andrienko, Fraunhofer IAIS, Germany
Maria Antonia Brovelli, Politechnico di Milano, Italy
Aris Gkoulalas-Divanis, IBM Research, USA
Bernardo Cuenca Grau, University of Oxford, UK
Krzysztof Janowicz, STKO Lab, Department of Geography, UCSB, USA
Vana Kalogeraki, Athens University of Economics and Business, Greece
Egor V. Kostylev, University of Oxford, UK
Ioannis Krontiris, European Research Center of Huawei in Munich, Germany
Dino Pedreschi, University of Pisa, Italy
Nikos Pelekis, University of Piraeus, Greece
Dieter Pfoser, Dept. of Geography and Geoinformation Science, George
Mason University, USA
Chiara Renso, CNR, Italy
Dimitris Sacharidis, ATHENA R.C., Greece
Maribel Yasmina Santos, University of Minho, Portugal
Yucel Saygin, Sabanci University, Turkey
Manolis Terrovitis, ATHENA R.C., Greece
Guillaume Touya, IGN, France
Katerina Tzompanaki, University of Cergy Pontoise, France
Dan Vodislav, University of Cergy Pontoise, France
George Vouros, University of Piraeus, Greece
Monica Wachowicz, University of New Brunswick, Canada