The Design of the Intelligent Analysis Framework for the Informationization of the Tourism Industry in Guizhou Province Under the Concept of Big Data

This paper aims to contribute to the debate on Open Innovation in the age of Big Data by shedding new light on the role that social networks can play as enabling platforms for tourists’ involvement and sources for the creation and management of valuable knowledge assets. The huge amount of data generated on social media by tourists related to their travel experiences can be a valid source of open innovation. To achieve this aim, this paper presents evidence of a digital tourism experience, through a longitudinal case study of a destination in Apulia, a Southern European region. The findings of the study demonstrate how social Big Data could open up innovation processes that could be of support in defining sustainable tourism experiences in a destination.

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Computer Big Data Sentiment Intelligent Analysis Using Maximum Likelihood and Deep Learning

10.1109/icdsca53499.2021.9650200 ◽

2021 ◽

Author(s):

Xuweiyi Chen

Keyword(s):

Big Data ◽

Deep Learning ◽

Maximum Likelihood ◽

Intelligent Analysis

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Customer engagement and value co-creation/destruction: the internal fostering and hindering factors and actors in the tourist/hotel experience

Journal of Research in Interactive Marketing ◽

10.1108/jrim-05-2020-0104 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Clement Nangpiire ◽

Joaquim Silva ◽

Helena Alves

Keyword(s):

Design Methodology ◽

Hospitality Industry ◽

Document Analysis ◽

Tourism Industry ◽

Theory And Practice ◽

Customer Engagement ◽

Analysis Framework ◽

Content Type ◽

Depth Interviews ◽

Practical Implications

PurposeThe customer as an active and engaged value co-creator raises new challenges for theory and practice, especially in the hospitality industry. However, the connection between engagement and co-creation is little studied in the hotel/tourism literature. This paper proposes a connection between customer engagement (CE) and value co-creation frameworks to ascertain and depict the internal actors' activities and factors that foster or hinder guests' co-creation and destruction of value.Design/methodology/approachThe researchers used qualitative methods (35 in-depth interviews, document analysis and four observation sessions) in seven regions of Ghana to explore the customer's perspective. Data were analyzed with NVivo11 within a thematic analysis framework.FindingsThe findings suggest that positive and negative engagement fosters or hinders guests' interactions, which lead to value co-creation or destruction. The research also discovered that negative interactions occasioned by any factor or actor trigger value destruction at multiple stages of the experience journey.Practical implicationsIndustry players can use the framework developed to assess their businesses, explore and reflect on the proposed value they aim to generate, and thus be more aware of how they can better facilitate value co-creation with their consumers and avoid value destruction.Originality/valueThis research proposes a novel connection between customer interactions, engagement and value co-creation to ascertain and depict the internal actors' activities and factors that foster or hinder customers' experience in the hotel/tourism industry.

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Hilbert Index-based Outlier Detection Algorithm in Metric Space

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2016100103 ◽

2016 ◽

Vol 8 (4) ◽

pp. 34-54

Author(s):

Honglong Xu ◽

Haiwu Rong ◽

Rui Mao ◽

Guoliang Chen ◽

Zhiguang Shan

Keyword(s):

Big Data ◽

Data Management ◽

Outlier Detection ◽

Metric Space ◽

Detection Algorithm ◽

Analysis Framework ◽

Distance Calculation ◽

The World ◽

Speed Up ◽

Detection Speed

Big data is profoundly changing the lifestyles of people around the world in an unprecedented way. Driven by the requirements of applications across many industries, research on big data has been growing. Methods to manage and analyze big data to extract valuable information are the key of big data research. Starting from the variety challenge of big data, this dissertation proposes a universal big data management and analysis framework based on metric space. In this framework, the Hilbert Index-based Outlier Detection (HIOD) algorithm is proposed. HIOD can handle all datatypes that can be abstracted to metric space and achieve higher detection speed. Experimental results indicate that HIOD can effectively overcome the variety challenge of big data and achieves a 2.02 speed up over iORCA on average and, in certain cases, up to 5.57. The distance calculation times are reduced by 47.57% on average and up to 89.10%.

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Big Data collision analysis framework

Intelligent Vehicular Networks and Communications ◽

10.1016/b978-0-12-809266-8.00008-9 ◽

2017 ◽

pp. 177-184

Author(s):

Anand Paul ◽

Naveen Chilamkurti ◽

Alfred Daniel ◽

Seungmin Rho

Keyword(s):

Big Data ◽

Analysis Framework ◽

Data Collision

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Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Electronics ◽

10.3390/electronics8050506 ◽

2019 ◽

Vol 8 (5) ◽

pp. 506 ◽

Cited By ~ 6

Author(s):

Faisal Mehmood ◽

Shabir Ahmad ◽

DoHyeun Kim

Keyword(s):

Big Data ◽

Real Time ◽

Recommendation System ◽

Tourism Industry ◽

Jeju Island ◽

Third Party ◽

User Preference ◽

Optimal Route ◽

Route Recommendation ◽

Travel Route Recommendation

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

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