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Metric Indexing for Efficient Data Access in the Internet of Things

 
: Beecks, Christian; Grass, Alexander; Devasya, Shreekantha

:
Postprint urn:nbn:de:0011-n-5827148 (433 KByte PDF)
MD5 Fingerprint: da69876fffba392e04d2f0dffb9badaa
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Erstellt am: 26.3.2020


Abe, N. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE International Conference on Big Data 2018. Proceedings : Dec 10-Dec 13, 2018, Seattle, WA, USA
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-5035-6
ISBN: 978-1-5386-5034-9
ISBN: 978-1-5386-5036-3
S.5132-5136
International Conference on Big Data <2018, Seattle/Wash.>
European Commission EC
H2020; 723145; COMPOSITION
European Commission EC
H2020; 723650; MONSOON
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FIT ()

Abstract
Data are a central phenomenon in our digital information age. They impact the way we live, work, and play and provide unprecedented opportunities to simplify our daily life and behavior. They implicate enormous potential and impact society, economy, and science. Due to the advancement of cyber-physical systems and Internet of Things technologies, it is expected that the majority of real-time data will be generated from devices interconnected within the Internet of Things by the year 2025. In this paper, we tackle the problem of managing Internet of Things data in an efficient way. To this end, we introduce the metric approach for storing and querying Internet of Things data and investigate the ability of pivot-based tables for indexing and searching this type of data. Along with the introduction of two real-world, large-scale Internet of Things datasets from the EU projects COMPOSITION and MONSOON (under grant no. 723145 and 723650), we show that the metric approach facilitates efficient data access in the Internet of Things.

: http://publica.fraunhofer.de/dokumente/N-582714.html