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A big-data approach for investigating destination image gap in Sanya City: When will the online and the offline goes parted?

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Regional Sustainability 2021年第1期2卷 98-108页

作者： Lingkun Meng Yi Liu Yuanlei Wang Xiaojuan Li School of Tourism Management Sun Yat-sen UniversityZhuhai519082China

Tourism destination images in terms of the gaps between the projected and perceived images are of great significance in the development of ***,the use of big-data in tourism studies remains under-utilized despite the boom in big-data applications and the increasing number of electronic User Generated Contents(UGC).Aiming to take advantage of tourism UGC to fully understand the destination image gap between official promotion materials and tourist perception of Sanya City in China,this study innovatively employed a big-data analysis technique,Tourism Sentiment Evaluation(TSE)model and proposed a new analysis framework integrating the“cognitive-affective”model with the gpp analysis of projected and perceived destination image to explore the destination image gap of Sanya It is found that Sanya's perceptive destination image is overall consistent with its official positioning;however,there also exist image gaps between the two groups in terms of the impact of festival events and tourists'attitude towards core scenic spots amongst *** study's findings are discussed in light of their methodological,theoretical,and practical implications for destination positioning,marketing,and management.

关键词： big-data Tourism reviews Destination image “Cognitive-affective”model Tourism sentiment evaluation model

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big-data Processing Techniques and Their Challenges in Transport Domain

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ZTE Communications 2015年第1期13卷 50-59页

作者： Aftab Ahmed Chandio Nikos Tziritas Cheng-Zhong Xu Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Institute of Mathematics and Computer Science University of Sindh Department of Electrical and Computer Engineering Wayne State University

This paper describes the fundamentals of cloud computing and current big-data key technologies. We categorize big-da- ta processing as batch-based, stream-based, graph-based, DAG-based, interactive-based, or visual-based according to the processing technique. We highlight the strengths and weaknesses of various big-data cloud processing techniques in order to help the big-data community select the appropri- ate processing technique. We also provide big data research challenges and future directions in aspect to transportation management systems.

关键词： big-data cloud computing transportation management sys-tems MapReduce bulk synchronous parallel

维普期刊数据库

Semantic Recognition of a data Structure in big-data

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Journal of Computer and Communications 2014年第9期2卷 93-102页

作者： Aicha Ben Salem Faouzi Boufares Sebastiao Correia Laboratory LIPN-UMR 7030-CNRS University Paris 13Sorbonne Paris CitéVilletaneuseFrance Company Talend SuresnesFrance

data governance is a subject that is becoming increasingly important in business and government. In fact, good governance data allows improved interactions between employees of one or more organizations. data quality represents a great challenge because the cost of non-quality can be very high. Therefore the use of data quality becomes an absolute necessity within an organization. To improve the data quality in a big-data source, our purpose, in this paper, is to add semantics to data and help user to recognize the big-data schema. The originality of this approach lies in the semantic aspect it offers. It detects issues in data and proposes a data schema by applying a semantic data profiling.

关键词： data Quality big-data Semantic data Profiling data Dictionary Regular Expressions Ontology

Extreme learning machines: new trends and applications

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Science China(Information Sciences) 2015年第2期58卷 5-20页

作者： DENG ChenWei HUANG GuangBin XU Jia TANG JieXiong School of Information and Electronics Beijing Institute of Technology School of Electronic Engineering Nanyang Technological University

Extreme learning machine(ELM), as a new learning framework, draws increasing attractions in the areas of large-scale computing, high-speed signal processing, artificial intelligence, and so on. ELM aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism and represents a suite of machine learning techniques in which hidden neurons need not to be tuned. ELM theories and algorithms argue that "random hidden neurons" capture the essence of some brain learning mechanisms as well as the intuitive sense that the efficiency of brain learning need not rely on computing power of ***, compared with traditional neural networks and support vector machine, ELM offers significant advantages such as fast learning speed, ease of implementation, and minimal human intervention. Due to its remarkable generalization performance and implementation efficiency, ELM has been applied in various applications. In this paper, we first provide an overview of newly derived ELM theories and approaches. On the other hand,with the ongoing development of multilayer feature representation, some new trends on ELM-based hierarchical learning are discussed. Moreover, we also present several interesting ELM applications to showcase the practical advances on this subject.

关键词： extreme learning machine fast learning high-speed and real-time signal processing large-scale computing big-data feature representation

考虑海洋环境突发事件的大数据海陆协同治理体系研究

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生态环境与保护 2016年第1期 104-108页

作者：李健赵世卓史浩

针对当前海洋环境突发事件治理体系在大数据时代收集、储存、分析、预警能力不足与单一海上应急部门处置能力不强的缺点，分析大数据时代海陆协同治理海洋环境突发事件的优势，构建大数据背景下海陆协同治理体系，有效结合大数据对数据... 详细信息

针对当前海洋环境突发事件治理体系在大数据时代收集、储存、分析、预警能力不足与单一海上应急部门处置能力不强的缺点，分析大数据时代海陆协同治理海洋环境突发事件的优势，构建大数据背景下海陆协同治理体系，有效结合大数据对数据信息的收集、分析能力强，与海陆协同能够有效提升应急处置效率的特点，能够在我国大规模开发海洋资源背景下处置海洋环境突发事件提供有效的支持。

关键词：海洋环境突发事件大数据海陆协同 marine environment emergency big-data sea-mainland coordination

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