AR-PPF: Advanced Resolution-Based Pixel Preemption Data Filtering for Efficient Time-Series Data Analysis

TL;DR

AR-PPF is a novel data filtering algorithm that enables efficient visualization and analysis of long-term manufacturing time-series data, reducing retrieval time while preserving key features for better insights.

Contribution

The paper introduces AR-PPF, a new resolution-based pixel preemption filtering method that improves long-term data visualization efficiency without losing critical information.

Findings

Significantly reduces data retrieval time for long-term time-series analysis.

Maintains key data features during filtering, ensuring accurate insights.

Enhances overall efficiency of manufacturing data analysis processes.

Abstract

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.