Applying Sequential Pattern Mining to Portable RFID System Data Heikki Sj"man, Martin Steinert
Heikki Sjöman, Martin Steinert
Available Online November 2016.
- https://doi.org/10.2991/iwama-16.2016.5How to use a DOI?
- RFID; data mining; sequential patterns; intelligent manufaturing; industry 4.0; data warehouses
- 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.
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 -