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.6.4.57
URN: urn:nbn:de:0030-drops-66912
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2016/6691/
Go back to Dagstuhl Reports

Acar, Avrim ; Anandkumar, Animashree ; Mullin, Lenore ; Rusitschka, Sebnem ; Tresp, Volker
Weitere Beteiligte (Hrsg. etc.): Evrim Acar and Animashree Anandkumar and Lenore Mullin and Sebnem Rusitschka and Volker Tresp

Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152)

pdf-format:
dagrep_v006_i004_p057_s16152.pdf (2 MB)

Abstract

This report documents the program and the outcomes of Dagstuhl Perspectives Workshop 16152 "Tensor Computing for Internet of Things". In an interactive three-day workshop industrial and academic researchers exchanged their multidisciplinary perspectives through impulse talks, panel discussions, and break-out sessions. Internet of Things (IoT) or Cyber-physical systems (CPS) bring out interesting new challenges to tensor computing, such as the need for real-time analytics and control in interconnected dynamic networks, e.g. electricity, transportation, manufacturing. On the other hand, IoT/CPS have characteristics that make tensor methods applicable to extract information very efficiently. During our discussions we identified an action plan to have a structured approach that will enable the multidisciplinary community of domain and control experts, data scientists, and distributed, embedded software developers to share knowledge and best practices, compare and exchange tensor models depending on data types and applications in distinct IoT/CPS scenarios.

BibTeX - Entry

@Article{acar_et_al:DR:2016:6691,
  author =	{Avrim Acar and Animashree Anandkumar and Lenore Mullin and Sebnem Rusitschka and Volker Tresp},
  title =	{{Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152)}},
  pages =	{57--79},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{6},
  number =	{4},
  editor =	{Evrim Acar and Animashree Anandkumar and Lenore Mullin and Sebnem Rusitschka and Volker Tresp},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2016/6691},
  URN =		{urn:nbn:de:0030-drops-66912},
  doi =		{10.4230/DagRep.6.4.57},
  annote =	{Keywords: Tensor Methods, Multi-way Data Analysis, Multi-linear Algebra, Tensor Software, Distributed \& Parallel Computing,  Big Data Computing & Analytics, }
}

Keywords: Tensor Methods, Multi-way Data Analysis, Multi-linear Algebra, Tensor Software, Distributed \& Parallel Computing, Big Data Computing & Analytics,
Freie Schlagwörter (englisch): Cyber-physical Systems, Intelligent Autonomous Systems, Applications in Smart Grid, Mobility, Smart City
Collection: Dagstuhl Reports, Volume 6, Issue 4
Issue Date: 2016
Date of publication: 12.10.2016

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI