Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review
With the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/11/4/112 |
| _version_ | 1827728622186659840 |
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| author | Victor Hugo Silva-Blancas José Manuel Álvarez-Alvarado Ana Marcela Herrera-Navarro Juvenal Rodríguez-Reséndiz |
| author_facet | Victor Hugo Silva-Blancas José Manuel Álvarez-Alvarado Ana Marcela Herrera-Navarro Juvenal Rodríguez-Reséndiz |
| author_sort | Victor Hugo Silva-Blancas |
| collection | DOAJ |
| description | With the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI) for decision making. One of the least used strategies is drill-down analysis (DD), belonging to the decision trees subcategory, which because of not having AI resources has lost interest among researchers. However, its easy implementation makes it a suitable tool for database processing systems. This research has developed a systematic review to understand the prospective of DD analysis on scientific literature in order to establish a knowledge platform and establish if it is convenient to drive it to integration with superior methodologies, as it would be those based on ANN, and produce a better diagnosis in future works. A total of 80 scientific articles were reviewed from 1997 to 2023, showing a high frequency in 2021 and experimental as the predominant methodology. From a total of 100 problems solved, 42% were using the experimental methodology, 34% descriptive, 17% comparative, and just 7% post facto. We detected 14 unsolved problems, from which 50% fall in the experimental area. At the same time, by study type, methodologies included correlation studies, processes, decision trees, plain queries, granularity, and labeling. It was observed that just one work focuses on mathematics, which reduces new knowledge production expectations. Additionally, just one work manifested ANN usage. |
| first_indexed | 2024-03-10T23:32:39Z |
| format | Article |
| id | doaj.art-6c7df1802ccb487cb0a4462c1475f651 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-10T23:32:39Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-6c7df1802ccb487cb0a4462c1475f6512023-11-19T03:13:35ZengMDPI AGTechnologies2227-70802023-08-0111411210.3390/technologies11040112Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic ReviewVictor Hugo Silva-Blancas0José Manuel Álvarez-Alvarado1Ana Marcela Herrera-Navarro2Juvenal Rodríguez-Reséndiz3Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoFacultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoWith the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI) for decision making. One of the least used strategies is drill-down analysis (DD), belonging to the decision trees subcategory, which because of not having AI resources has lost interest among researchers. However, its easy implementation makes it a suitable tool for database processing systems. This research has developed a systematic review to understand the prospective of DD analysis on scientific literature in order to establish a knowledge platform and establish if it is convenient to drive it to integration with superior methodologies, as it would be those based on ANN, and produce a better diagnosis in future works. A total of 80 scientific articles were reviewed from 1997 to 2023, showing a high frequency in 2021 and experimental as the predominant methodology. From a total of 100 problems solved, 42% were using the experimental methodology, 34% descriptive, 17% comparative, and just 7% post facto. We detected 14 unsolved problems, from which 50% fall in the experimental area. At the same time, by study type, methodologies included correlation studies, processes, decision trees, plain queries, granularity, and labeling. It was observed that just one work focuses on mathematics, which reduces new knowledge production expectations. Additionally, just one work manifested ANN usage.https://www.mdpi.com/2227-7080/11/4/112data experimentaldata miningdata sciencedata warehousedrill-down |
| spellingShingle | Victor Hugo Silva-Blancas José Manuel Álvarez-Alvarado Ana Marcela Herrera-Navarro Juvenal Rodríguez-Reséndiz Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review Technologies data experimental data mining data science data warehouse drill-down |
| title | Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review |
| title_full | Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review |
| title_fullStr | Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review |
| title_full_unstemmed | Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review |
| title_short | Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review |
| title_sort | tendency on the application of drill down analysis in scientific studies a systematic review |
| topic | data experimental data mining data science data warehouse drill-down |
| url | https://www.mdpi.com/2227-7080/11/4/112 |
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