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Abstract The analysis of the transformation and changes in scientific disciplines has always been a critical path for policymakers and researchers. The current study examines the changes in the research areas of data and information quality (DIQ). The aim of this study was to detect different types of changes occurring in the scientific areas including birth, death, growth, decline, merge, and splitting. A model has been developed for this data mining. To test the model, all DIQ articles published in online scientific citation indexing service or Web of Science (WOS) between 1970 and 2016 were extracted and analyzed using the given model. The study is related to the Big Data as well as the integration methods in Big Data which is the most important area in DIQ. It is demonstrated that the first and second emerging research areas are sub-disciplines of entity resolution and record linkage. Accordingly, linkage and privacy are the first emerging research area and the entity resolution using ontology is the second in DIQ. This is followed by the social media issues and genetic related DIQ issues. Highlights • Developing novel approach for finding scientific threads • Developing a model to show behavior of scientific research areas • Developing a model for finding top research areas of scientific disciplines • Finding DIQ emerging research areas [ABSTRACT FROM AUTHOR]

Flipped classroom is a relatively new model in education that primarily focuses on learner-centered instructions. In other words, the model allows both management and teachers to build a more active and dynamic learning environment on the campus. The current paper tries to document the implementation of the flipped classroom model in two big data courses. Here, the course contents have been curated from a couple of websites with different contents including videos and short books as well as reports. The mixed-method approach was applied while analyzing the student perceptions in demographically two different groups. It was found that students of both groups responded positively to the flipped classroom, with each focusing on their specific goals. Consequently, the first group focused on the academic achievement whereas the second group with managerial jobs focused on solving problems in their workplaces. Students of both groups, although preferred TED talks and documentaries, they were opposed to university-like videos and O'Reilly short books and reports. Meanwhile, the use of English language contents turned out to be both a challenge and an opportunity for students. [ABSTRACT FROM AUTHOR]

Abstract: The selection of residence location in different countries is of high priority and significance for tourists. The selection of the most appropriate hotel entails a rather complicated decision-making process. A comprehensive hotel selection model can empower the hotel managers, the tourists, and the tourism industry to make decisions based on more effective indicators of high quality services for a higher rate of satisfaction. The purpose of this research is to deeply explore the broad literature and to identify the most significant hotel selection indicators and factors in Tehran hotels and to present a comprehensive model through an exploratory factor analysis of the extracted indicators so as to provide the managers and tourists with a firm ground for making better decisions regarding the indicators of hotel selection. Promenade and comfort, security and protection, network services, pleasure, staff and their services, news and recreational information, cleanliness and room comfort, expenditure, room facilities and car parking were identified as the main hotel selection factors of Tehran hotels. Afterwards, another factor analysis has been done in order to extract the next hidden set of factors within the aforementioned factors which return two main factors of “Hotel Comfort Factors” and “Hotel Compensatory Factors”. Following the creation of the final model and based on the intrinsic vagueness of decision making in the process of selection, a set of fuzzy membership functions for the extracted factors has been provided. The intention has been to provide the expert system and decision support system developers and users with a set of practical indicators in order to help them design and implement realistic systems based on the deeply studied indicators and factors of hotel selection. Such supportive systems can be directly presented to the tourists requesting a mechanism for selecting the most appropriate hotel but lacking enough information about the important indicators and factors and also to the managers of hotels who are trying to make strategic decisions regarding the most optimized investments on the indicators of selecting a hotel. Considering the priorities of tourists, hotel managers, entrepreneurs and investors in the hotel industry require deep investigations and studies for which this paper provides a firm basis. [Copyright &y& Elsevier]

Abstract: Data mining techniques, extracting patterns from large databases have become widespread in business. Using these techniques, various rules may be obtained and only a small number of these rules may be selected for implementation due, at least in part, to limitations of budget and resources. Evaluating and ranking the interestingness or usefulness of association rules is important in data mining. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to find most efficient association rule by solving only one mixed integer linear programming (MILP). Then, utilizing this model, a new method for prioritizing association rules by considering multiple criteria is proposed. As an advantage, the proposed method is computationally more efficient than previous works. Using an example of market basket analysis, applicability of our DEA based method for measuring the efficiency of association rules with multiple criteria is illustrated. [Copyright &y& Elsevier]

Data provided by tourists always benefit tourism managers and help them offer customized services, products and destinations to future travelers. This research investigates the effect of interests on Iranian outbound tourists, especially their selection of a destination and then, using text and data mining algorithms, it introduces a model to predict tourists' destinations based on their interests and travel backgrounds. In the current study, a dataset of 244,980 travels, consisting of 6661 people, was extracted from social media to discover the relationship between tourists' interests and travel destinations. Hence, it represents a model that is created using data and text mining from travel agencies to design their marketing plans by offering and advertising destinations to travelers with specific interest categories. The model has also shown promising accuracy and interesting results for the future tourist destination data and text analysis. • The personality aspects of tourists in relation to their preferences of destinations have rarely been studied • The results of this study can help the tourism-related organizations and tourists to have more customized services and offers. • The Clustering results show that the outbound Iranian tourists can be clustered into 4 categories. • By this information and more analysis on the data, the motivations of tourists can also be extracted. [ABSTRACT FROM AUTHOR]