Real-Time Data Streaming and AI Enhancements: E-Commerce Live Streaming Shopping

Authors

  • Arjun Mantri Independent Researcher

DOI:

https://doi.org/10.47941/ijce.2004

Keywords:

Real-Time Data Streaming, Artificial Intelligence, E-Commerce, Live Streaming Shopping, Consumer Engagement.

Abstract

This paper explores the transformative potential of real-time data streaming and artificial intelligence (AI) in the context of e-commerce live streaming shopping. By leveraging advance technologies such as Storm, Trident, Samza, and Spark Streaming, businesses can process and analyze data in real-time, enhancing consumer engagement and driving sales in real time. This paper reviews the literature on live streaming selling, product promotion, and multichannel sales, and discusses the challenges and opportunities associated with these technologies. The findings provide valuable insights for businesses and researchers aiming to harness the power of real-time data streaming in the dynamic landscape of social commerce using real time streaming

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References

Wingerath, Wolfram, Gessert, Felix, Friedrich, Steffen and Ritter, Norbert. Real-time stream processing for Big Data” it- Information Technology, vol. 58, no. 4, 2016, pp. 186-194. https://doi.org/10.1515/itit-2016-0002.

Xiao Zeng, Biyi Fang, Haichen Shen, and Mi Zhang. 2020. Distream: scaling live video analytics with workload-adaptive distributed edge intelligence. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems (SenSys’20). Association for Computing Machinery, New York, NY, USA, 409–421. https://doi.org/10.1145/3384419.3430721

Andrew Psaltis, Streaming Data: Understanding the real-time pipeline, Manning, 2017.

Wang, Tong-Yuan, et al. "Live Streaming Service Introduction and Optimal Contract Selection in an E-commerce Supply Chain." IEEE Transactions on Engineering Management (2024).

B. K. Sunny, P. S. Janardhanan, A. B. Francis and R. Murali, “Implementation of a self-adaptive real time recommendation system using spark machine learning libraries,” 2017 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Kollam, India, 2017, pp. 1-7, doi: 10.1109/SPICES.2017.8091310.

Xu, Xiaoyu et al. “What Drives Consumer Shopping Behavior in Live Streaming Commerce.” Journal of Electronic Commerce Research 21 (2020): 144.

Apasrawirote, Darlin & Yawised, Kritcha. (2022). Factors Influencing the Behavioral and Purchase Intention on Live-streaming Shopping. Asian Journal of Business Research. 12. 39-56. 10.14707/ajbr.220119.

Wang, Y., Lu, Z., Cao, P. et al. How Live Streaming Changes Shopping Decisions in E-commerce: A Study of Live Streaming Commerce. Comput Supported Coop Work 31, 701–729 (2022). https://doi.org/10.1007/s10606-022-09439-2.

Hwang, S.B., Kim, S. (2006). Dynamic Pricing Algorithm for E-Commerce. In: Sobh, T., Elleithy, K. (eds) Advances in Systems, Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5263-4_24.

Zheng, T., Chen, G., Wang, X. et al. Real-time intelligent big data processing: technology, platform, and applications. Sci. China Inf. Sci. 62, 82101 (2019). https://doi.org/10.1007/s11432-018-9834-8.

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Published

2024-06-16

How to Cite

Mantri, A. (2024). Real-Time Data Streaming and AI Enhancements: E-Commerce Live Streaming Shopping. International Journal of Computing and Engineering, 5(5), 22–32. https://doi.org/10.47941/ijce.2004

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Section

Articles