Attributes Reduction in Big Data
Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced coll...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/14/4901 |
| _version_ | 1797562235299561472 |
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| author | Waleed Albattah Rehan Ullah Khan Khalil Khan |
| author_facet | Waleed Albattah Rehan Ullah Khan Khalil Khan |
| author_sort | Waleed Albattah |
| collection | DOAJ |
| description | Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics. |
| first_indexed | 2024-03-10T18:25:18Z |
| format | Article |
| id | doaj.art-0c25f8846cf84fcbb09209efd98045be |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:25:18Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0c25f8846cf84fcbb09209efd98045be2023-11-20T07:02:19ZengMDPI AGApplied Sciences2076-34172020-07-011014490110.3390/app10144901Attributes Reduction in Big DataWaleed Albattah0Rehan Ullah Khan1Khalil Khan2Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, PakistanProcessing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics.https://www.mdpi.com/2076-3417/10/14/4901attributes samplingcontent-based filteringSupport Vector Machinesmachine learning |
| spellingShingle | Waleed Albattah Rehan Ullah Khan Khalil Khan Attributes Reduction in Big Data Applied Sciences attributes sampling content-based filtering Support Vector Machines machine learning |
| title | Attributes Reduction in Big Data |
| title_full | Attributes Reduction in Big Data |
| title_fullStr | Attributes Reduction in Big Data |
| title_full_unstemmed | Attributes Reduction in Big Data |
| title_short | Attributes Reduction in Big Data |
| title_sort | attributes reduction in big data |
| topic | attributes sampling content-based filtering Support Vector Machines machine learning |
| url | https://www.mdpi.com/2076-3417/10/14/4901 |
| work_keys_str_mv | AT waleedalbattah attributesreductioninbigdata AT rehanullahkhan attributesreductioninbigdata AT khalilkhan attributesreductioninbigdata |