A New Big Data Processing Framework for the Online Roadshow
The Online Roadshow, a new type of web application, is a digital marketing approach that aims to maximize contactless business engagement. It leverages web computing to conduct interactive game sessions via the internet. As a result, massive amounts of personal data are generated during the engageme...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/7/3/123 |
| _version_ | 1797581241644482560 |
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| author | Kang-Ren Leow Meng-Chew Leow Lee-Yeng Ong |
| author_facet | Kang-Ren Leow Meng-Chew Leow Lee-Yeng Ong |
| author_sort | Kang-Ren Leow |
| collection | DOAJ |
| description | The Online Roadshow, a new type of web application, is a digital marketing approach that aims to maximize contactless business engagement. It leverages web computing to conduct interactive game sessions via the internet. As a result, massive amounts of personal data are generated during the engagement process between the audience and the Online Roadshow (e.g., gameplay data and clickstream information). The high volume of data collected is valuable for more effective market segmentation in strategic business planning through data-driven processes such as web personalization and trend evaluation. However, the data storage and processing techniques used in conventional data analytic approaches are typically overloaded in such a computing environment. Hence, this paper proposed a new big data processing framework to improve the processing, handling, and storing of these large amounts of data. The proposed framework aims to provide a better dual-mode solution for processing the generated data for the Online Roadshow engagement process in both historical and real-time scenarios. Multiple functional modules, such as the Application Controller, the Message Broker, the Data Processing Module, and the Data Storage Module, were reformulated to provide a more efficient solution that matches the new needs of the Online Roadshow data analytics procedures. Some tests were conducted to compare the performance of the proposed frameworks against existing similar frameworks and verify the performance of the proposed framework in fulfilling the data processing requirements of the Online Roadshow. The experimental results evidenced multiple advantages of the proposed framework for Online Roadshow compared to similar existing big data processing frameworks. |
| first_indexed | 2024-03-10T23:02:35Z |
| format | Article |
| id | doaj.art-87d78eeb390e4945901c9006c0c1f7b2 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T23:02:35Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-87d78eeb390e4945901c9006c0c1f7b22023-11-19T09:34:04ZengMDPI AGBig Data and Cognitive Computing2504-22892023-06-017312310.3390/bdcc7030123A New Big Data Processing Framework for the Online RoadshowKang-Ren Leow0Meng-Chew Leow1Lee-Yeng Ong2Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, MalaysiaThe Online Roadshow, a new type of web application, is a digital marketing approach that aims to maximize contactless business engagement. It leverages web computing to conduct interactive game sessions via the internet. As a result, massive amounts of personal data are generated during the engagement process between the audience and the Online Roadshow (e.g., gameplay data and clickstream information). The high volume of data collected is valuable for more effective market segmentation in strategic business planning through data-driven processes such as web personalization and trend evaluation. However, the data storage and processing techniques used in conventional data analytic approaches are typically overloaded in such a computing environment. Hence, this paper proposed a new big data processing framework to improve the processing, handling, and storing of these large amounts of data. The proposed framework aims to provide a better dual-mode solution for processing the generated data for the Online Roadshow engagement process in both historical and real-time scenarios. Multiple functional modules, such as the Application Controller, the Message Broker, the Data Processing Module, and the Data Storage Module, were reformulated to provide a more efficient solution that matches the new needs of the Online Roadshow data analytics procedures. Some tests were conducted to compare the performance of the proposed frameworks against existing similar frameworks and verify the performance of the proposed framework in fulfilling the data processing requirements of the Online Roadshow. The experimental results evidenced multiple advantages of the proposed framework for Online Roadshow compared to similar existing big data processing frameworks.https://www.mdpi.com/2504-2289/7/3/123Online Roadshowbig data processing frameworkApache SparkApache Kafka |
| spellingShingle | Kang-Ren Leow Meng-Chew Leow Lee-Yeng Ong A New Big Data Processing Framework for the Online Roadshow Big Data and Cognitive Computing Online Roadshow big data processing framework Apache Spark Apache Kafka |
| title | A New Big Data Processing Framework for the Online Roadshow |
| title_full | A New Big Data Processing Framework for the Online Roadshow |
| title_fullStr | A New Big Data Processing Framework for the Online Roadshow |
| title_full_unstemmed | A New Big Data Processing Framework for the Online Roadshow |
| title_short | A New Big Data Processing Framework for the Online Roadshow |
| title_sort | new big data processing framework for the online roadshow |
| topic | Online Roadshow big data processing framework Apache Spark Apache Kafka |
| url | https://www.mdpi.com/2504-2289/7/3/123 |
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