Applying Sequential Pattern Mining to Portable RFID System Data Heikki Sj"man, Martin Steinert

DOI

10.2991/iwama-16.2016.5How to use a DOI?

Keywords

RFID; data mining; sequential patterns; intelligent manufaturing; industry 4.0; data warehouses

Abstract

This paper presents how data mining can be applied to RFID proximity tracking data captured in a production setting. The WINEPI algorithm is explained and used for mining sequential patterns from transaction data produced by portable RF transceivers that can be attached, for example, to the personnel and machines of a production facility. The contribution of this paper is the additional mindset of how data can be produced for a data warehouse with dedicated sensors in order to prototype the data warehouse itself - and how we can use this created knowledge as a help when designing intelligent manufacturing systems.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Heikki Sjöman
AU  - Martin Steinert
PY  - 2016/11
DA  - 2016/11
TI  - Applying Sequential Pattern Mining to Portable RFID System Data Heikki Sj"man, Martin Steinert
BT  - Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SP  - 25
EP  - 29
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.5
DO  - 10.2991/iwama-16.2016.5
ID  - Sjöman2016/11
ER  -