CAJ | 학술논문

大数据管理是当前港口航运业关注的热点之一,对航运大数据的有效分析和利用能够为政府相关部门和功能性国企在公共服务领域发挥积极作用提供基础数据的支撑。航运大数据管理在实际应用中存在大数据人才储备不足、数据安全监管规则缺失、数据资产价值评估困难和政策约束影响数据共享等诸多难点。大数据管理可以依托云计算平台提供基础硬件设施,通过建设完善的数据治理构架,针对不同数据使用场景进行分区域管理,建立开放共享的大数据生态体系。航运大数据应用到公共服务领域中,可以通过构建客观精准的大数据航运指数快速反映行业变化,使用动态监控集装箱堆场数据实现箱管智能调度,对进出口企业征信以促进供应链金融发展,实时监控报关数据以降低风险损失。
대수거관리시당전항구항운업관주적열점지일,대항운대수거적유효분석화이용능구위정부상관부문화공능성국기재공공복무영역발휘적겁작용제공기출수거적지탱。항운대수거관리재실제응용중존재대수거인재저비불족、수거안전감관규칙결실、수거자산개치평고곤난화정책약속영향수거공향등제다난점。대수거관리가이의탁운계산평태제공기출경건설시,통과건설완선적수거치리구가,침대불동수거사용장경진행분구역관리,건립개방공향적대수거생태체계。항운대수거응용도공공복무영역중,가이통과구건객관정준적대수거항운지수쾌속반영행업변화,사용동태감공집장상퇴장수거실현상관지능조도,대진출구기업정신이촉진공응련금융발전,실시감공보관수거이강저풍험손실。
Big data is gaining increasing attention from the ports and shipping industry. The analysis and exploitation of shipping data can provide government departments and functional state-owned enterprises with elementary data so that they may play a better role in public service. However, the exploitation of shipping data faces several difficult problems. Firstly, the government and state-owned enterprises do not have enough qualified human resources to process big data, so they have to rely on universities and research institutes. Secondly, since shipping data are of great commercial value, data security and business privacy are of extreme importance during its storage and transmission. Thirdly, the data assets of the shipping enterprises need to be evaluated, and an equitable mechanism of profit distribution should be set up on the basis of the ownership of data. Finally, the efficient employment of shipping big data involves theintegration of various sources in order to realize the sharing of data and resources, which requires the establishment of relevant policies by the government to promote data sharing in public service. Managing big data will bring greater profit to various sections of public service and lead to broader application prospects once the above problems are solved. Firstly, realtime data mining on the shipping data will make the shipping index more accurate as it reflects the changes of the industry more quickly than traditional shipping indices. Secondly, the containers at different container yards can be dynamically allocated and distributed when the data are monitored and processed in realtime, so that the operation efficiency of shipping enterprises is raised and social cost is saved. Thirdly, the shipping data can be used to inspect enterprises＇ credit rating and provide information on the actual operation of enterprises at fast speed and low cost, assisting the development of the supply chain finance. Fourthly, realtime monitoring of customs application data will facilitate the discovery of questionable products and missing information and give pre-warning to export-oriented enterprises, greatly reducing their risks and losses. In the future, shipping big data should be firstly integrated with the cloud infrastructure by benefiting from standard software and hardware and elastic computing resource allocation. Secondly, the data governance architecture inside the organization needs to be improved. Shipping data from variance sources should be cleaned and mended before further analysis and processing. Thirdly, data inside the organization should be grouped and partitioned in order to fulfill different requirements. Finally, a shipping big data ecosystem is proposed to realize the sharing of data among all interested individuals and companies. With big data technology developing at fast speed, the supervision departments and relevant service enterprises should adopt reliable and well-established solutions to construct an analytical platform for big data.