文献类型：期刊文献

中文题名：知识型旅游目的地管理平台框架及其构建

英文题名：FRAMEWORK AND CONSTRUCTION OF A KNOWLEDGEABLE DESTINATION

作者：乔向杰[1];张凌云[1]

第一作者：乔向杰

机构：[1]北京联合大学旅游学院

第一机构：北京联合大学旅游学院

年份：2014

卷号：29

期号：4

起止页码：104-110

中文期刊名：人文地理

外文期刊名：Human Geography

收录：人大复印报刊资料;CSTPCD;;北大核心:【北大核心2011】；CSSCI:【CSSCI2014_2016】；

基金：国家社科基金重点项目(13AJY016);北京市教委科技计划项目(SQKM201411417012);北京联合大学新起点计划项目(zk201220)

语种：中文

中文关键词：知识型目的地;数据仓库;OLAP;数据挖掘;旅游市场决策支持平台

外文关键词：knowledgeable destination; data warehouse; OLAP; data mining; tourism decision support platform

摘要：旅游目的地的竞争力很大程度上依赖于信息如何通过基于ICT的基础设施和服务来满足相关利益方的需求。但是,在目前旅游目的地已积累了大量可用数据的情况下,这些有价值的知识却没有被很好的利用。旅游目的地的管理竞争力和组织学习可以应用商务智能的方法得到显著地提高。本文在总结国外文献与案例的基础上,提出知识型目的地管理平台的一般框架,并基于此框架,引入商务智能方法,构建了一个基于数据仓库的旅游市场决策支持平台,以期能够服务于旅游目的地的管理者的决策制定与专家学者的相关研究。
Through a literature review on the theory and practice of a knowledgeable destination from abroad, some conclusions are drawn as follows： （1）The competitiveness of tourism destinations depends largely on how information needs of stakeholders can be satisfied through ICT-based infrastructures and services. （2） Managerial competences and organizational learning in tourism destinations could be significantly enhanced by applying methods of business intelligence （BI）. （3）The knowledge-based paradigm regards tourism as a complex social phenomenon where knowledge is the basis for sustainable destination development. A general framework for a knowledge destination is put forward in the paper which includes data sources, data＇s ex- traction, transformation and load（ETL）, a data warehouse, a knowledge creation layer and a knowledge application layer. Each part of the framework which involves their contents, application status are discussed in detail as well. Moreover, a tourism decision support platform based on data warehouse is constructed, with using business intelligence technology. The system＇s data sources and indicators, technical architecture, knowledge presentation and application cases are also provided. Be the help of the platform, tourism managers or decision-makers can： （1） flexibly access the data by using various criteria; （2）quickly grasp the market＇s de- velopment status and trends by viewing or combining different multi-dimensional reports; （3）easily analyze the correlation between data to mine the in-depth causes for the data; （4） customize their own analysis and save them. The paper concludes by providing some challenges of the development of the platform and some future work are also offered.