=Paper=
{{Paper
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|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1458/C02_keynote_Seidl.pdf
|volume=Vol-1458
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1458/C02_keynote_Seidl.pdf</pdf>
<pre>
      Fast Multimedia Stream Data Mining

                                 Thomas Seidl

                              RWTH Aachen,
                  Templergraben 55, 52056 Aachen, Germany
                     seidl@informatik.rwth-aachen.de
                 http://dme.rwth-aachen.de/de/team/seidl



    Abstract. In our days, huge and still increasing amounts of data are
    collected from scientific experiments, sensor and communication net-
    works, technical processes, business operations and many other domains.
    Database and data mining techniques aim at efficiently analyzing these
    large and complex data to support new insights and decision making
    based on the extraction of regular or irregular patterns hidden in the
    data. Current research trends in data analytics are driven by the high
    volume, velocity, and variety of Big Data.
    The talk discusses some challenges in the field. In recent developments for
    dynamic stream data mining, anytime algorithms play an important role.
    Novel hierarchical, statistical indexing structures including BayesTree
    and ClusTree allow for obtaining high quality results at any time while
    adapting themselves to varying stream velocities. Particular challenges
    occur when supervised and unsupervised mining tasks are faced with
    multimodal streams of complex multimedia objects.




Copyright c 2015 by the papers authors. Copying permitted only for private and
academic purposes. In: R. Bergmann, S. Görg, G. Müller (Eds.): Proceedings of
the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB. Trier, Germany, 7.-9.
October 2015, published at http://ceur-ws.org




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</pre>