Construction of visual data scheduling and retrieval model for customer service data analysis platform

Jing Yang; Mingjie Zhang; Qing Zhu; Jian Gong

doi:10.1117/12.2628534

22 April 2022 Construction of visual data scheduling and retrieval model for customer service data analysis platform

Jing Yang, Mingjie Zhang, Qing Zhu, Jian Gong

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121740K (2022) https://doi.org/10.1117/12.2628534
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

The current data scheduling retrieval model have the problem that the performance of sub flow path at non bottleneck is restrained, which affects the performance of the model. This paper constructs a visual data scheduling and retrieval model for customer service data analysis platform. Using microservice encapsulation, the overall architecture of the platform is designed, and the corresponding services are called to complete business processing. Considering the fairness of bottleneck, the queuing mechanism under path congestion is established, and the transmission path is allocated according to the number of data packets that need to skip the buffer, which is read and sent according to the path sequence number. Combined with round-trip delay and path congestion, a data scheduling retrieval model is established to schedule and retrieve the path with low congestion. The simulation results show that compared with the models based on service differentiation, scheduling priority allocation, cloud computing and evolutionary multi-objective optimization, the total amount of data transmission, throughput and bandwidth utilization of this model are improved. It has certain transmission advantages in case of network congestion, which is conducive to improving the stability of the platform.

Citation Download Citation

Jing Yang, Mingjie Zhang, Qing Zhu, and Jian Gong "Construction of visual data scheduling and retrieval model for customer service data analysis platform", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121740K (22 April 2022); https://doi.org/10.1117/12.2628534

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KEYWORDS

Data modeling

Data analysis

Data transmission

Visualization

Visual process modeling

Clouds

Visual analytics

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