Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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

Authors
Heikki Sjöman, Martin Steinert
Corresponding Author
Heikki Sjöman
Available Online November 2016.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
6th International Workshop of Advanced Manufacturing and Automation
Part of series
Advances in Economics, Business and Management Research
Publication Date
November 2016
ISBN
978-94-6252-243-5
ISSN
2352-5428
DOI
https://doi.org/10.2991/iwama-16.2016.5How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 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  - https://doi.org/10.2991/iwama-16.2016.5
ID  - Sjöman2016/11
ER  -