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What and Who
Title:Data Series Management: The Road to Big Sequence Analytics
Speaker:Themis Palpanas
coming from:Paris Descartes University (France)
Speakers Bio:Themis Palpanas is a professor of computer science at the Paris

Descartes University (France), where he is a director of the Data
Intensive and Knowledge Oriented Systems (diNo) group. He received
the BS degree from the National Technical University of Athens,
Greece, and the MSc and PhD degrees from the University of Toronto,
Canada. He has previously held positions at the University of Trento
and the IBM T.J. Watson Research Center. He has also worked for the
University of California, Riverside, and visited Microsoft Research
and the IBM Almaden Research Center. His research solutions have been
implemented in world-leading commercial data management products
and he is the author of nine US patents. He is the recipient of
three Best Paper awards (including ICDE and PERCOM), and the IBM
Shared University Research (SUR) Award in 2012, which represents
a recognition of research excellence at worldwide level. He has been
a member of the IBM Academy of Technology Study on Event Processing,
and is a founding member of the Event Processing Technical Society.
He has served as General Chair for VLDB 2013, the top international
conference on databases, and he is serving as Associate Editor for

Event Type:Talk
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
We use this to send out email in the morning.
Level:Expert Audience
Date, Time and Location
Date:Monday, 6 June 2016
Duration:60 Minutes
Building:E1 4
There is an increasingly pressing need, by several applications in
diverse domains, for developing techniques able to index and mine very
large collections of sequences, or data series. Examples of such
applications come from social media analytics and internet service
providers, as well as from a multitude of scientific domains. It is not
unusual for these applications to involve numbers of data series in the
order of hundreds of millions to billions, which are often times not
analyzed in their full detail due to their sheer size.
In this talk, we describe recent efforts in designing techniques for
indexing and mining truly massive collections of data series that will
enable scientists to easily analyze their data. We show that the main
bottleneck in mining such massive datasets is the time taken to build
the index, and we thus introduce solutions to this problem. Furthermore,
we discuss novel techniques that adaptively create data series indexes,
allowing users to correctly answer queries before the indexing task is
finished. We also show how our methods allow mining on datasets that
would otherwise be completely untenable, including the first published
experiments using one billion data series.
Finally, we present our vision for the future in big sequence management
research, including the promising directions in terms of distributed
Name(s):Petra Schaaf
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Attachments, File(s):

Petra Schaaf/AG5/MPII/DE, 05/31/2016 10:12 AM
Last modified:
halma/MPII/DE, 11/07/2018 04:52 PM
  • Petra Schaaf, 05/31/2016 10:17 AM -- Created document.