scholarly journals BIG DATA ANALYSIS AS A NEW APPROACH FOR USABILITY ATTRIBUTES EVALUATION OF USER INTERFACES: AN AUTOMOTIVE INDUSTRY CONTEXT

Author(s):  
Julia Orlovska ◽  
Casper Wickman ◽  
Rikard Söderberg
2021 ◽  
Author(s):  
Mingchuan Yang ◽  
Xinye Shao ◽  
Guanchang Xue ◽  
Bingyu Xie

AbstractIn order to deal with the difficulty of spectrum sensing in cognitive satellite wireless networks, a large-scale cognitive network spectrum sensing algorithm based on big data analysis theory is studied, and a new algorithm using mean exponential eigenvalue is proposed. This new approach fully uses all the eigenvalues in sample covariance matrix of the sensing results to make the decision, which can effectively improve the detection performance without obtaining the prior information from licensed users. Through simulation, the performance of various large scale cognitive radio spectrum sensing algorithms based on big data analysis theory is compared, and the influence of satellite to ground channel conditions and the number of sensing nodes on the performance of the algorithm is discussed.


2016 ◽  
Vol 4 (3) ◽  
pp. 1-21 ◽  
Author(s):  
Sungchul Lee ◽  
Eunmin Hwang ◽  
Ju-Yeon Jo ◽  
Yoohwan Kim

Due to the advancement of Information Technology (IT), the hospitality industry is seeing a great value in gathering various kinds of and a large amount of customers' data. However, many hotels are facing a challenge in analyzing customer data and using it as an effective tool to understand the hospitality customers better and, ultimately, to increase the revenue. The authors' research attempts to resolve the current challenges of analyzing customer data in hospitality by utilizing the big data analysis tools, especially Hadoop and R. Hadoop is a framework for processing large-scale data. With the integration of new approach, their study demonstrates the ways of aggregating and analyzing the hospitality customer data to find meaningful customer information. Multiple decision trees are constructed from the customer data sets with the intention of classifying customers' needs and customers' clusters. By analyzing the customer data, the study suggests three strategies to increase the total expenditure of the customers within a limited amount of time during their stay.


2019 ◽  
Vol 9 (1) ◽  
pp. 01-12 ◽  
Author(s):  
Kristy F. Tiampo ◽  
Javad Kazemian ◽  
Hadi Ghofrani ◽  
Yelena Kropivnitskaya ◽  
Gero Michel

2020 ◽  
Vol 25 (2) ◽  
pp. 18-30
Author(s):  
Seung Wook Oh ◽  
Jin-Wook Han ◽  
Min Soo Kim

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