Impact of Different Data Management Frameworks on Common Data Management Tasks in Information System (R Language Perspective)
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| Title: | Impact of Different Data Management Frameworks on Common Data Management Tasks in Information System (R Language Perspective) |
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| Authors: | Masood H. Siddiqui, Aanchal A. Awasthi, Anant Prakash Awasthi, Niraj Kumar Singh |
| Source: | International Journal of Intelligent Systems and Applications in Engineering; Vol. 12 No. 21s (2024); 1711-1720 |
| Publisher Information: | International Journal of Intelligent Systems and Applications in Engineering, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Memory Management in R, Performance in R, Native R, Tidyverse, Data.Table |
| Description: | To maximize data processing and analysis, effective data management is essential. It ensures that data is efficiently processed, readily accessible, secure, and well-organized. This enhances data integrity, reduces the amount of redundancy, and it makes decision-making more prompt. In an era where data is a valued asset that drives innovation and strategic decision-making, effective data management techniques are essential. The two essential data management activities for improving data processing are joining and sorting. By combining datasets based on common characteristics, joining makes thorough analysis easier. Sorting data well enhances search and retrieval. When combined, these processes enhance the accuracy and speed of data processing, simplifying workflows and enabling sound decision-making. Database management systems depend on joining and sorting to enable the creation of value, the extraction of significant insights, and the identification of trends from massive datasets. The performance of native R, tidyverse, and data.table when merging data in R varies. Large datasets may cause Native R to lag, despite its versatility. Known for its readability, Tidyverse strikes a balance between performance and simplicity. Because of its exceptional speed, Data.table is a very effective option for large-scale data joins. The decision is based on the complexity and amount of the dataset. The best option for maximum performance, particularly for complex and large-scale jobs, is Data.table. Native R and Tidyverse work well with smaller, more manageable datasets when code readability is crucial. Every method addresses particular requirements in R data analysis. Similarly, when it comes to sorting data in R, Native R, tidyverse, and data.table behave differently. While Native R provides a standard method, it might not be as effective with larger datasets. Although readability is given priority in Tidyverse's user-friendly syntax, it may not be as fast as more efficient options. Once more, Data.table runs faster and uses less memory when sorting large amounts of data than the competition. The decision is based on the needs of the analysis: data.table for best performance, especially with large datasets and computationally intensive tasks; tidyverse for readability; and Native R for simplicity. Hence, in order to sum up, effective data management is essential for businesses to fully utilize their data and make wise decisions. Optimizing data processing and analysis requires careful consideration of joining, sorting, and tool selection. |
| Document Type: | Article |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 2147-6799 |
| Access URL: | https://www.ijisae.org/index.php/IJISAE/article/view/5709 |
| Rights: | CC BY SA |
| Accession Number: | edsair.issn21476799..d1e5b09c44d9d149c461fefb9f76c5d3 |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | To maximize data processing and analysis, effective data management is essential. It ensures that data is efficiently processed, readily accessible, secure, and well-organized. This enhances data integrity, reduces the amount of redundancy, and it makes decision-making more prompt. In an era where data is a valued asset that drives innovation and strategic decision-making, effective data management techniques are essential. The two essential data management activities for improving data processing are joining and sorting. By combining datasets based on common characteristics, joining makes thorough analysis easier. Sorting data well enhances search and retrieval. When combined, these processes enhance the accuracy and speed of data processing, simplifying workflows and enabling sound decision-making. Database management systems depend on joining and sorting to enable the creation of value, the extraction of significant insights, and the identification of trends from massive datasets. The performance of native R, tidyverse, and data.table when merging data in R varies. Large datasets may cause Native R to lag, despite its versatility. Known for its readability, Tidyverse strikes a balance between performance and simplicity. Because of its exceptional speed, Data.table is a very effective option for large-scale data joins. The decision is based on the complexity and amount of the dataset. The best option for maximum performance, particularly for complex and large-scale jobs, is Data.table. Native R and Tidyverse work well with smaller, more manageable datasets when code readability is crucial. Every method addresses particular requirements in R data analysis. Similarly, when it comes to sorting data in R, Native R, tidyverse, and data.table behave differently. While Native R provides a standard method, it might not be as effective with larger datasets. Although readability is given priority in Tidyverse's user-friendly syntax, it may not be as fast as more efficient options. Once more, Data.table runs faster and uses less memory when sorting large amounts of data than the competition. The decision is based on the needs of the analysis: data.table for best performance, especially with large datasets and computationally intensive tasks; tidyverse for readability; and Native R for simplicity. Hence, in order to sum up, effective data management is essential for businesses to fully utilize their data and make wise decisions. Optimizing data processing and analysis requires careful consideration of joining, sorting, and tool selection. |
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| ISSN: | 21476799 |