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.

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS.

The Nevada Solar Energy-Water-Environment Nexus project collects a large amount of environmental data from a variety of sensors such as soil, atmosphere, biology, and ecology. Mostly, the environmental data is related to a development of renewable energy resources in the Nexus project. The environmental data can have an impact on other research fields if it can easily be shared with other researchers, students, teachers, and general users. Therefore, Nevada Climate Change Portal (NCCP) site was created for Nexus project with a purpose of sharing such data. However, there are some challenges to address in utilizing such data, collecting the data, and sharing the data among the users. In this research, the authors propose Extended Web Service Architecture for solving these challenges. The authors implement Arduino instead of CR1000 as a collector due to its cost effectiveness. The authors also use REST API to overcome the limitations of Arduino. Moreover, the authors experiment with popular Web-based data visualization tools such as Google Chart, Flex, OFC, and D3 to visualize NCCP data.