  With the advent of automation, many manufacturing industries have
transitioned to data-centric methodologies, giving rise to an unprecedented
influx of data during the manufacturing process. This data has become
instrumental in analyzing the quality of manufacturing process and equipment.
Engineers and data analysts, in particular, require extensive time-series data
for seasonal cycle analysis. However, due to computational resource
constraints, they are often limited to querying short-term data multiple times
or resorting to the use of summarized data in which key patterns may be
overlooked. This study proposes a novel solution to overcome these limitations;
the advanced resolution-based pixel preemption data filtering (AR-PPF)
algorithm. This technology allows for efficient visualization of time-series
charts over long periods while significantly reducing the time required to
retrieve data. We also demonstrates how this approach not only enhances the
efficiency of data analysis but also ensures that key feature is not lost,
thereby providing a more accurate and comprehensive understanding of the data.
