  RFID technology has gained popularity to address localization problem in the
manufacturing shopfloor due to its affordability and easiness in deployment.
This technology is used to track the manufacturing object location to increase
the production efficiency. However, the data used for localization task is not
easy to analyze because it is generated from the non-stationary environment. It
also continuously arrive over time and yields the large-volume of data.
Therefore, an advanced big data analytic is required to overcome this problem.
We propose a distributed big data analytic framework based on PANFIS (Scalable
PANFIS), where PANFIS is an evolving algorithm which has capability to learn
data stream in the single pass mode. Scalable PANFIS can learn big data stream
by processing many chunks/partitions of data stream. Scalable PANFIS is also
equipped with rule structure merging to eliminate the redundancy among rules.
Scalable PANFIS is validated by measuring its performance against single PANFIS
and other Spark scalable machine learning algorithms. The result shows that
Scalable PANFIS performs running time more than 20 times faster than single
PANFIS. The rule merging process in Scalable PANFIS shows that there is no
significant reduction of accuracy in classification task with 96.67 percent of
accuracy in comparison with single PANFIS of 98.71 percent. Scalable PANFIS
also generally outperforms some Spark MLib machine learnings to classify RFID
data with the comparable speed in running time.
