Integrating VSM and Network Analysis for Tourism Strategies – Case: Mexico and the Chinese Outbound Market

Arenas, Tanya; Martínez, Miguel A.; Xu, Honggang; Matamoros, Oswaldo Morales; Chávez, Mauricio Genet Guzmán

doi:10.1007/s11213-018-9475-6

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…In the information age, massive tourism data will bring many problems to the centralized data processing mode with the cloud computing model as the core [13]. First, the processing mode of uploading all data to the cloud will not only cause low efficiency but also cause additional bandwidth overhead, at the same time, the network delay will also increase [14]. Second, due to the improvement of users' privacy awareness, the data of edge devices are likely to leak in the upload link, and the security of personal privacy cannot be guaranteed [15].…”

Section: Tourism Data Processing and Multi-source Data Fusionmentioning

confidence: 99%

Tourism Information Data Processing Method Based on Multi‐Source Data Fusion

Gan

2021

Journal of Sensors

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Urban social civilization and the quality of life of residents are gradually improved, and the development scale and trend of the leisure tourism industry have been growing. This paper constructs a multi-source data fusion model based on an ensemble learning algorithm, uses Ctrip 2020 open data set to train the model, and then obtains the tourism information data processing and prediction results. This paper takes the data of Ctrip as the training set and compares the trained model with the data of tunic and Feizhu. In this paper, sensor detection technology is used to analyze many famous scenic spots in China, including tourist type, gender, and location. The results show that tourism feature extraction results are consistent with data from trending flying bamboo, tunics, and other websites, according to the results of a multi-source fusion of tourism information. Among them, in the data of the first half of 2020, the prediction accuracy of the model after data processing is about 62%. Affected by the epidemic situation, the accuracy of the model is low. In the second half of the year, the prediction accuracy is 78%, which can be used to fuse tourism information in a short time. Therefore, the data show that the model has high learning ability and high trend prediction ability in tourism data processing, which can provide necessary information support for tourists.

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Section: Tourism Data Processing and Multi-source Data Fusionmentioning

confidence: 99%

Tourism Information Data Processing Method Based on Multi‐Source Data Fusion

Gan

2021

Journal of Sensors

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“…In this kind of system, the regulatory part (work station, machine, robot, process) must have at least the same variety as the regulated part (Operator 4.0 or work equipment 4.0) so that the system reaches stability, which necessitates the establishment of a one-to-one correspondence between the varieties on each side. In order to achieve this adjustment, adapters are employed to reduce or amplify the variety, depending on what is required [99]. This adapter is itself a system or part of a system, and can act in either direction, increasing the variety by means of amplifiers or reducing it by means of reducers, until the regulator and regulated subsystems reach the same variety and determine an affective occupational use experience.…”

Section: Law Of Requisite Varietymentioning

confidence: 99%

Life Cycle Engineering 4.0: A Proposal to Conceive Manufacturing Systems for Industry 4.0 Centred on the Human Factor (DfHFinI4.0)

et al. 2020

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Engineering 4.0 environments are characterised by the digitisation, virtualisation, and connectivity of products, processes, and facilities composed of reconfigurable and adaptive socio-technical cyber-physical manufacturing systems (SCMS), in which Operator 4.0 works in real time in VUCA (volatile, uncertain, complex and ambiguous) contexts and markets. This situation gives rise to the interest in developing a framework for the conception of SCMS that allows the integration of the human factor, management, training, and development of the competencies of Operator 4.0 as fundamental aspects of the aforementioned system. The present paper is focused on answering how to conceive the adaptive manufacturing systems of Industry 4.0 through the operation, growth, and development of human talent in VUCA contexts. With this objective, exploratory research is carried, out whose contribution is specified in a framework called Design for the Human Factor in Industry 4.0 (DfHFinI4.0). From among the conceptual frameworks employed therein, the connectivist paradigm, Ashby’s law of requisite variety and Vigotsky’s activity theory are taken into consideration, in order to enable the affective-cognitive and timeless integration of the human factor within the SCMS. DfHFinI4.0 can be integrated into the life cycle engineering of the enterprise reference architectures, thereby obtaining manufacturing systems for Industry 4.0 focused on the human factor. The suggested framework is illustrated as a case study for the Purdue Enterprise Reference Architecture (PERA) methodology, which transforms it into PERA 4.0.

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