-------- Original-Nachricht -------- Betreff: [wkwi] CFP: ECIS 2014 - Track "Decision Support and Big Data" Datum: Sat, 2 Nov 2013 10:01:03 +0100 (CET) Von: Guido Schryen guido.schryen@wiwi.uni-regensburg.de Antwort an: postmaster@idefix.buva.sowi.uni-bamberg.de Organisation: Universität Regensburg

Liebe Kolleg(inn)en,

die Öffnung des ECIS 2014-Einreichungssystems nehme ich zum Anlass, Sie auf unseren Track aufmerksam zu machen. Über Einreichungen aus Ihren Reihen würden wir uns freuen.

Mit freundlichen Grüßen,

Guido Schryen

**************************************************************************** CALL FOR PAPERS

22nd European Conference on Information Systems (ECIS 2014)

Track: Decision Support and Big Data (http://ecis2014.eu/wp-content/uploads/2013/07/8.-Decision-Support-and-Big-D ata1.pdf)

June 9-11, 2014, Tel Aviv, Israel (http://ecis2014.eu/)

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TRACK CHAIRS

Tina Comes, University of Agder, Norway,martina.comes@uia.no Guido Schryen*, University of Regensburg, Germany,guido.schryen@ur.de Stefan Voß, University of Hamburg, Germany,stefan.voss@uni-hamburg.de

*Corresponding track chair

DESCRIPTION

Intelligent decision support is known to be crucial in many business contexts, and it will both gain momentum and become increasingly challenging through the availability of very large data pools (big data). Sensor networks, social network activities, RFID deployment, Internet search histories and retail transactions are just a few examples of sources that are likely to generate Exabytes or even Zettabytes of data. The data are characterized by high levels of volume, velocity, variety and variability, and traditional analytics and techniques may easily fall short of storing, analyzing and processing these data and, even more so, using them in an intelligent way for decision making.

Key advantages of successfully managing big data and using them for decision making and business analytics include the improvement of overall efficiency, the improvement of speed and accuracy of decision making, the ability to forecast, the identification of business opportunities and a greater understanding of citizens’ and customers’ needs. Turning big data into business and society value will thus become one of the major challenges in the IS discipline. The close link between data and decisions shall avoid the processing of irrelevant or redundant information and thus help reduce information overload while ensuring that all relevant information is processed.

This track looks for new and innovative methodologies, techniques, theories, and systems that allow for exploiting big data pools to support decision makers. We invite both quantitative and analytical contributions.

TOPICS OF INTEREST

Possible topics include, but are not limited to:

* Data mining and machine learning in decision support contexts,

* Decision making, including optimization models and methodologies

* Uncertainty and risk management in decision support

* Iterative, sequential and interdependent decisions

* Social media analysis and crowd sourcing

* Collaborative and participatory decision making including social media applications

* Retail and customer analysis

* Logistics and supply chain analysis and supply chain risk management

* Internet data analysis

* Data quality

* Business value of big data

* Video analytics

* Geospatial analytics

* Cloud analytics and intelligence

SPONSORSHIP

Authors of exceptional papers will be invited for submitting an expanded version to a fast-track special issue in the Springer journal Annals of Information Systems.

IMPORTANT DATES

Paper Submission begins: 1 November, 2013 Submission Deadline Date: 8 December, 2013 Notification of Acceptance: March 3, 2014 Final version of accepted papers due: March 30, 2014 Early Bird Registration closes: April 16, 2014

ASSOCIATE EDITORS

Jörn Altmann, Seoul National University, Korea

Christer Carlsson, Abo Akademi University, Finland

Haluk Demirkan, University of Washington – Tacoma, USA

Benjamin Fabian, Humboldt-Universität zu Berlin, Germany

Andreas Fink, Helmut-Schmidt-University, Germany

Ole-Christoffer Granmo, University of Agder, Norway

Richard F. Hartl, University of Vienna, Austria

Laetitia Jourdan, INRIA, France

Mathias Klier, University of Regensburg, Germany

Nathalie Kliewer, Freie Universität Berlin, Germany

Leo Kroon, Rotterdam School of Management, The Netherlands

Janny M.Y Leung, The Chinese University of Hong Kong, China

Dirk C. Mattfeld, Technische Universität Braunschweig

Lars Mönch, Fernuniversität Hagen, Germany

Dirk Neumann, University of Freiburg, Germany

Leysia Palen, University of Colorado, Boulder, USA

Fethi A. Rabhi, University of New South Wales, Australia

Manjeet Rege, University of St. Thomas, USA

Franz Rothlauf, Johannes Gutenberg University of Mainz, Germany

Vicente Salas Fumás, Universidad de Zaragoza, Zaragoza, Spain

Michael Scholz, Universität Passau, German

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Prof. Dr. Guido Schryen Institut für Wirtschaftsinformatik Universität Regensburg Universitätsstraße 31 93053 Regensburg Web:www.winfor.uni-regensburg.de