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首页> 外文会议>International Conference on Advances in Big Data, Computing and Data Communication Systems >A Design Science Model for the Application of Data Mining and Machine Learning Models on Constrained Devices in Low Bandwidth Areas
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A Design Science Model for the Application of Data Mining and Machine Learning Models on Constrained Devices in Low Bandwidth Areas

机译：低带宽区域约束设备数据挖掘和机器学习模型应用的设计科学模型

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The sale of commuter bus tickets by human ticket agents during peak periods can be stressful to the participants involved. The ticket agents need to select departure locations from a list of possible departures provided by the bus service. For each departure, there is then a list of possible destinations. The commuter bus service described in this research used Android devices with a large format screen along with a connected secure ticket printer. Many of the ticket vending stations were in rural areas catering for commuters who lived in rural areas and worked in urban areas. As such, the connectivity was often slow. The devices themselves were also constrained with many of them still running on Android Jelly Bean. This research looks at ways to increase the speed of individual ticket sales by using data mining and machine learning models on centralized servers, then publishing these models as small text files on REST servers. These small text files could then be downloaded by the Android devices each day at the start of business to assist in predicting departures and destinations to the ticket agents thereby decreasing the amount of time a customer and ticket agent needed to conduct a sale.
机译：在高峰期的人类票代理商销售通勤班车门票可能对所涉及的参与者有压力。票务代理需要从巴士服务提供的可能出发列表中选择离境位置。每次出发，那么就有一个可能的目的地列表。本研究中描述的通勤总线服务使用了Android设备，具有大格式屏幕以及连接的安全票证打印机。许多票子自动贩卖站都在农村地区为住在农村地区的通勤者，并在城市地区工作。因此，连接性通常很慢。这些设备本身也被限制在仍然在Android果冻豆上运行。本研究通过在集中式服务器上使用数据挖掘和机器学习模型来提高个人票据销售速度的方式，然后将这些模型作为REST服务器上的小文本文件发布。然后，这些小文本文件可以在业务开始时每天下载Android设备，以帮助预测票据代理的偏离和目的地，从而降低进行销售所需的客户和票据代理的时间。

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