Enabling a Semantic Sensor Knowledge Approach for Quality Control Support in Cleanrooms

With the explosion of big data technologies (BD), the pos-sibility to integrate those tools into daily company operations is more affordable and straightforward. On the other hand, Knowledge-based approaches such as graphs and different semantic approaches, although those have not been so popular in the industry in the past years, nowa-days, thanks to the high availability of heterogeneous data inside of the company context, those tools are being used more to enhance or enrich data and processes, and make more informed decisions about the busi-ness. The SEMT platform is presented; this system combines a Big Data recollection approach from a legacy/manual sensor environment to per-form a knowledge enhancement process to support the semi-conductor development and production operations inside a cleanroom.