-------- Weitergeleitete Nachricht -------- Betreff: [WI] 3rd CfP: Special Session on Big Data Analytics & Stream Data Mining @ ISMIS 2017 Datum: Tue, 10 Jan 2017 22:06:50 +0100 Von: Martin Atzmueller atzmueller@cs.uni-kassel.de Antwort an: Martin Atzmueller atzmueller@cs.uni-kassel.de An: kdml@cs.uni-kassel.de, ak-kd-list@lists.uni-karlsruhe.de, fg-db@informatik.uni-rostock.de, wi@aifb.uni-karlsruhe.de
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Call for Papers Special Session on Big Data Analytics and Stream Data Mining (BDASD2017)
A Special Session co-located with the 23rd International Symposium on Methodologies for Intelligent Systems (ISMIS 2017) Warsaw, Poland, June 26-29, 2017 *********************************************************************
Objectives ========== In the world of today, modern information systems are able to collect very large data with inherent and increasing complex structure and dimensionality. Furthermore, new data sources often provide various heterogeneous representations and also time changing characteristics with respect to the data. This is particular visible in the rapidly developing field of Big Data Analytics. Although machine learning and data mining researchers had already studied mining massive and complex data, there are significant differences between earlier efforts and the current trends opening up new problems and challenges. Indeed, Big Data Analytics opens up new research problems which were only considered within a limited range. Applications of Big Data Analytics may also influence human behavior and society in a significantly higher degree than before – which also requires new types of research. Furthermore, new Big Data challenges are particularly relevant in emerging applications where data are continuously generated at a high rate in the form of data streams, whose characteristics may also change with time (concept drifting data). Compared to static, standard environments, processing data streams implies new computational challenges and requirements for algorithms and their ability to adapt to such dynamic and complex contexts.
In order to address these new research challenges concerning both the analysis of Big Data and mining data streams, respectively, we organize a special session – BDASD - co-located with the ISMIS conference. We aim to gather researchers from all over the world coming from different communities being interested in the aforementioned issues, as well as to present algorithmic foundations and application aspects of analyzing these new types of data.
Topics of interest ================== Suggested topics include (but are not limited to) the following: * Learning from high-dimensional datasets * Mining non-standard data representations * Large-scale link and graph mining * Scaling up learning algorithms * Distributed data mining approaches * Knowledge discovery from ubiquitous environments * Analysis of data from sensors and social media * Online learning algorithms. * Detection and adaptation to concept drift * Evaluation issues of models learned from evolving data streams * Classification and clustering in data streams * Privacy in big and stream data analytics * Societal aspects of applying Big Data * Applications, especially in scientific data analysis, computational social science, medicine, text processing, web mining, image or multimedia analysis, sensor networks, industrial contexts, bio-informatics, energy management, and related domains.
Special Session Organizers ========================== Martin Atzmueller, University of Kassel, Germany Jerzy Stefanowski, Poznan University of Technology, Poland
Important Dates =============== Paper submission due: January 22, 2017 Notification of review results: March 14, 2017 Camera ready papers due: April 3, 2017
Proceedings =========== The accepted papers will be published within the ISMIS main conference proceedings (Springer LNAI Series).
Paper submission ================ Authors are invited to submit their manuscripts using the Springer LNCS/LNAI style, with a maximum of 10 pages. Detailed instructions are provided on the conference homepage.
Paper should be submitted in PDF format via the ISMIS 2017 Online Submission System