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Digital
Library of the European Council for Modelling
and Simulation |
Title: |
Building Adaptive Data Mining Models on Streaming Data in Real-Time,
an Outlook on Challenges, Approaches and Ongoing Research |
Authors: |
Frederic Theodor Stahl |
Published in: |
(2018). ECMS 2018
Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens
Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0005 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) 32nd European Conference on Modelling and
Simulation, Wilhelmshaven, Germany, May 22nd
– May 265h, 2018 |
Citation
format: |
Frederic
Theodor Stahl (2018). Building Adaptive Data Mining Models on Streaming Data
in Real-Time, an Outlook on Challenges, Approaches and Ongoing Research, ECMS 2018 Proceedings Edited by: Lars Nolle,
Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European
Council for Modeling and Simulation. doi:
10.7148/2018-0008 |
DOI: |
https://doi.org/10.7148/2018-0008 |
Abstract: |
Advances in hardware and software, in
the past two decades have enabled the capturing, recording and processing of
potentially large and infinite streaming data. As a consequence the field of
research in Data Stream Mining has emerged building Data Mining models,
workflows and algorithms enabling the efficient and effective analysis of
such streaming data at a large scale. Application areas of Data Stream Mining
techniques include real-time telecommunication data, telemetric data from
large industrial plants, credit card transactions, social media data, Smart
Cities, IoT, etc. Some applications allow the data to be processed modelled
and analysed in batches by traditional Data Mining approaches. However,
others require the model building and analytics to take place in real-time as
soon as new data becomes available i.e. to accommodate infinite streams and
fast changing concepts in the data. This talk discusses challenges, barriers,
opportunities and recent research on Micro-Cluster based Data Stream Mining
models to overcome these barriers. |
Full
text: |