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Intelligent Port Data Management Systems to improve capability

机译：智能港口数据管理系统以提高能力

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摘要

From operations management's point of view, the nature of business models is the tool for knowing data, processing data and extracting value from data. Recently many studies advocate big data research which one common object to bring in intelligence from the huge amount of data. Nevertheless, owing to the characteristics of unstructured data, extracting the value in the big data still requires further research. This paper demonstrates a case study on container throughput forecasting model based on previous socio-economic data. The objective is to create an intelligent logistics centre for port operations. First, descriptive statistics has been conducted to describe the potential influencing factors. And then a variable selection process was applied to confirm the variables which will be included in the forecasting model. Then a forecasting model can be built by support vector machine algorithm. To evaluate the performance of the proposed method, other forecasting models were used, and the results showed that the proposed method outperform other common models, and also has the ability to help the stakeholders to make decisions.
机译：从运营管理的角度来看，业务模型的本质是用于了解数据，处理数据并从数据中提取价值的工具。最近，许多研究提倡大数据研究，这是从海量数据中引入情报的一个常见对象。然而，由于非结构化数据的特性，在大数据中提取价值仍需要进一步研究。本文展示了基于先前社会经济数据的集装箱吞吐量预测模型的案例研究。目的是为港口运营创建一个智能物流中心。首先，已经进行了描述性统计来描述潜在的影响因素。然后，应用变量选择过程来确认将包含在预测模型中的变量。然后可以通过支持向量机算法建立预测模型。为了评估该方法的性能，使用了其他预测模型，结果表明该方法优于其他常用模型，并且能够帮助利益相关者做出决策。

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