A New Big Data Processing Framework for the Online Roadshow

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kang-Ren Leow, M. Leow, Lee-Yeng Ong
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Abstract

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.
一种新的在线路演大数据处理框架
在线路演是一种新型的网络应用程序,是一种旨在最大限度地提高非接触式商业参与度的数字营销方法。它利用网络计算通过互联网进行交互式游戏会话。因此,在观众和在线路演之间的互动过程中,会产生大量的个人数据(例如游戏数据和点击流信息)。通过网络个性化和趋势评估等数据驱动流程,收集的大量数据有助于在战略业务规划中更有效地细分市场。然而,在这样的计算环境中,传统数据分析方法中使用的数据存储和处理技术通常是过载的。因此,本文提出了一种新的大数据处理框架,以改进对这些大量数据的处理、处理和存储。拟议的框架旨在提供一个更好的双模解决方案,用于在历史和实时场景中处理在线路演参与过程生成的数据。重新制定了多个功能模块,如应用程序控制器、消息代理、数据处理模块和数据存储模块,以提供更高效的解决方案,满足在线路演数据分析程序的新需求。进行了一些测试,将拟议框架的性能与现有类似框架进行比较,并验证拟议框架在满足在线路演数据处理要求方面的性能。实验结果证明,与现有的类似大数据处理框架相比,所提出的在线路演框架具有多种优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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