scholarly journals Research on Precision Marketing Model of Tourism Industry Based on User’s Mobile Behavior Trajectory

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Jialin Zhang ◽  
Tong Wu ◽  
Zhipeng Fan
Keyword(s):  
Big Data ◽  
Recommendation System ◽  
Tourism Industry ◽  
Location Based Service ◽  
Design Data ◽  
Location Services ◽  
Marketing Mode ◽  
Marketing Model ◽  
Data Platform ◽  
Industry Needs

With the deep cross-border integration of tourism and big data, the personalized demand of tourist groups is increasingly strong. Precision marketing has become a new marketing mode that the tourism industry needs to pay close attention to and explore. Based on the advantages of big data platform and location-based service, starting from the precise marketing demand of tourism, we design data flow mining technology framework for user’s mobile behavior trajectory based on location services in mobile e-commerce environment to get user track data that incorporates location information, consumption information, and social information. Data mining clustering technology is used to analyze the characteristics of users’ mobile behavior trajectories, and the precise recommendation system of tourism is constructed to provide support for tourism decision making. It can target the tourist group for precise marketing and make tourists travel smarter.

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

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 506 ◽  
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.

scholarly journals Exploring the Relationship between Perceived Big Data Advantages and Online Consumers’ Behavior: An Extended Hierarchy of Effects Model

2020 ◽  
Vol 13 (6) ◽  
pp. 73
Author(s):  
Jean-Luc Pradel Mathurin Augustin ◽  
Shu-Yi Liaw
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Customer Service ◽  
Information Search ◽  
Dynamic Pricing ◽  
Recommendation System ◽  
Tourism Industry ◽  
Social Awareness ◽  
Significance Level ◽  
Hierarchy Of Effects

This study intends to extend the hierarchy of effects model into the reality of the tourism industry after incorporation of information and communication technologies. Data analyses were conducted on 260 online questionnaires. The findings indicated consumer behavior follows a three-layer model: Attention-Intention/Desire-Action/Sharing-Social Awareness. Among big data advantages, recommendation system, information search and improved customer service are important to Attention-Intention; information search, dynamic pricing are important to Desire-Action with customer service (lower significance level); only customer service is important to Sharing-Social awareness. This model allows understanding of consumers’ behavior in online tourism as tourists are often sharing their experiences and raise awareness on service quality from e-vendors. Organizations might use big data to guarantee customers’ satisfaction and attract positive feedback particularly from the third layer of behavior.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

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