License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.9.7.24
URN: urn:nbn:de:0030-drops-116349
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/11634/
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Bagnall, Anthony ; Cole, Richard L. ; Palpanas, Themis ; Zoumpatianos, Kostas
Weitere Beteiligte (Hrsg. etc.): Anthony Bagnall and Richard L. Cole and Themis Palpanas and Konstantinos Zoumpatianos

Data Series Management (Dagstuhl Seminar 19282)

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dagrep_v009_i007_p024_19282.pdf (3 MB)

Abstract

We now witness a very strong interest by users across different domains on data series (a.k.a. time series) management. It is not unusual for industrial applications that produce data series to involve numbers of sequences (or subsequences) in the
order of billions (i.e., multiple TBs). As a result, analysts are unable to handle the vast amounts of data series that they have
to manage and process. The goal of this seminar is to enable researchers and practitioners to exchange ideas and foster
collaborations in the topic of data series management and identify the corresponding open research directions. The main
questions answered are the following: i) What are the data series management needs across various domains and what
are the shortcomings of current systems, ii) How can we use machine learning to optimize our current data systems, and how can these systems help in machine learning pipelines? iii) How can visual analytics assist the process of analyzing big data series collections? The seminar focuses on the following key topics related to data series management:
1)Data series storage and access paterns, 2) Query optimization, 3) Machine learning and data mining for data serie, 4) Visualization for data series exploration, 5) Applications in multiple domains.

BibTeX - Entry

@Article{bagnall_et_al:DR:2019:11634,
  author =	{Anthony Bagnall and Richard L. Cole and Themis Palpanas and Kostas Zoumpatianos},
  title =	{{Data Series Management (Dagstuhl Seminar 19282)}},
  pages =	{24--39},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{9},
  number =	{7},
  editor =	{Anthony Bagnall and Richard L. Cole and Themis Palpanas and Konstantinos Zoumpatianos},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2019/11634},
  URN =		{urn:nbn:de:0030-drops-116349},
  doi =		{10.4230/DagRep.9.7.24},
  annote =	{Keywords: data series; time series; sequences; management; indexing; analytics; machine learning; mining; visualization}
}

Keywords: data series; time series; sequences; management; indexing; analytics; machine learning; mining; visualization
Collection: Dagstuhl Reports, Volume 9, Issue 7
Issue Date: 2019
Date of publication: 18.12.2019

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