An Electronic Commerce Big Data Analytics Architecture and Platform
The COVID-19 pandemic significantly increased e-commerce growth, adding more than 218 billion US dollars to the United States e-commerce sales. With this significant growth, various operational challenges have appeared, including logistic difficulties and customer satisfaction. Businesses that striv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/19/10962 |
| _version_ | 1797576170469851136 |
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| author | Amr Munshi Ahmad Alhindi Thamir M. Qadah Amjad Alqurashi |
| author_facet | Amr Munshi Ahmad Alhindi Thamir M. Qadah Amjad Alqurashi |
| author_sort | Amr Munshi |
| collection | DOAJ |
| description | The COVID-19 pandemic significantly increased e-commerce growth, adding more than 218 billion US dollars to the United States e-commerce sales. With this significant growth, various operational challenges have appeared, including logistic difficulties and customer satisfaction. Businesses that strive to take advantage of increased e-commerce growth must understand data and rely on e-commerce analytics. The large scale of e-commerce data requires sophisticated information technology techniques and cyber-infrastructure to leverage and analyze. This study presents a big e-commerce data platform to address several challenges in e-commerce. The presented platform’s design is based on a distributed system architecture that supports e-commerce analytics applications using historical and real-time data and features a continuous feedback loop to observe the decision-making and evaluation processes to achieve the desired objectives. The platform was validated using two analytical applications. The first application was to identify the periods in which customers prefer to place orders, while the second was used to verify the big e-commerce data platform. The resulting insights and findings promote informed e-commerce decisions. Furthermore, viewing and acting on insight results and findings promote informed decisions that potentially benefit the e-commerce industry. The proposed platform can perform numerous e-commerce applications that potentially benefit the e-commerce industry. |
| first_indexed | 2024-03-10T21:48:28Z |
| format | Article |
| id | doaj.art-40442de655df4ea6a55b5b2261fbbc5e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:48:28Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-40442de655df4ea6a55b5b2261fbbc5e2023-11-19T14:06:39ZengMDPI AGApplied Sciences2076-34172023-10-0113191096210.3390/app131910962An Electronic Commerce Big Data Analytics Architecture and PlatformAmr Munshi0Ahmad Alhindi1Thamir M. Qadah2Amjad Alqurashi3College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi ArabiaCollege of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi ArabiaCollege of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi ArabiaSalla Research and Innovation, Makkah 24225, Saudi ArabiaThe COVID-19 pandemic significantly increased e-commerce growth, adding more than 218 billion US dollars to the United States e-commerce sales. With this significant growth, various operational challenges have appeared, including logistic difficulties and customer satisfaction. Businesses that strive to take advantage of increased e-commerce growth must understand data and rely on e-commerce analytics. The large scale of e-commerce data requires sophisticated information technology techniques and cyber-infrastructure to leverage and analyze. This study presents a big e-commerce data platform to address several challenges in e-commerce. The presented platform’s design is based on a distributed system architecture that supports e-commerce analytics applications using historical and real-time data and features a continuous feedback loop to observe the decision-making and evaluation processes to achieve the desired objectives. The platform was validated using two analytical applications. The first application was to identify the periods in which customers prefer to place orders, while the second was used to verify the big e-commerce data platform. The resulting insights and findings promote informed e-commerce decisions. Furthermore, viewing and acting on insight results and findings promote informed decisions that potentially benefit the e-commerce industry. The proposed platform can perform numerous e-commerce applications that potentially benefit the e-commerce industry.https://www.mdpi.com/2076-3417/13/19/10962big datadata lakedata warehousee-commerce |
| spellingShingle | Amr Munshi Ahmad Alhindi Thamir M. Qadah Amjad Alqurashi An Electronic Commerce Big Data Analytics Architecture and Platform Applied Sciences big data data lake data warehouse e-commerce |
| title | An Electronic Commerce Big Data Analytics Architecture and Platform |
| title_full | An Electronic Commerce Big Data Analytics Architecture and Platform |
| title_fullStr | An Electronic Commerce Big Data Analytics Architecture and Platform |
| title_full_unstemmed | An Electronic Commerce Big Data Analytics Architecture and Platform |
| title_short | An Electronic Commerce Big Data Analytics Architecture and Platform |
| title_sort | electronic commerce big data analytics architecture and platform |
| topic | big data data lake data warehouse e-commerce |
| url | https://www.mdpi.com/2076-3417/13/19/10962 |
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