AR-PPF: Advanced Resolution-Based Pixel Preemption Data Filtering for Efficient Time-Series Data Analysis
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914850936979456 |
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| author | Kim, Taewoong Choi, Kukjin Kim, Sungjun |
| author_facet | Kim, Taewoong Choi, Kukjin Kim, Sungjun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19575 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AR-PPF: Advanced Resolution-Based Pixel Preemption Data Filtering for Efficient Time-Series Data Analysis Kim, Taewoong Choi, Kukjin Kim, Sungjun Human-Computer Interaction Databases Performance 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. |
| title | AR-PPF: Advanced Resolution-Based Pixel Preemption Data Filtering for Efficient Time-Series Data Analysis |
| topic | Human-Computer Interaction Databases Performance |
| url | https://arxiv.org/abs/2406.19575 |