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