A vast amount of Data is generated in various medical fields that can improve predictions and clinical decision-making. The use of big Data analytics in medicine, especially in areas related to health and lifespan, can play a crucial role. This article examines the applications of big Data in predicting health outcomes using genomic Data. Big Data analytics in medicine can play a vital role in predicting and managing diseases, improving clinical decision-making, and enhancing health outcomes. Combining genomic Data with big Data analytics can help identify complex patterns and provide personalized therapies. Given the high potential of this approach, further research is needed in this area to capitalize on available opportunities and address existing challenges.

With the advancements in science and technology‎, ‎the industrial and aviation sectors have witnessed a significant increase in Data‎. ‎A vast amount of Data is generated and utilized continuously‎. ‎It is imperative to employ Data mining techniques to extract and uncover knowledge from this Data‎. ‎Data mining is a method that enables the extraction of valuable information and hidden relationships from Datasets‎. ‎However‎, ‎the current aviation Data presents challenges in effectively extracting knowledge due to its large volume and diverse structures‎. ‎Air Traffic Management (ATM) involves handling Big Data‎, ‎which exceeds the capacity of conventional acquisition‎, ‎matching‎, ‎management‎, ‎and processing within a reasonable timeframe‎. ‎Aviation Big Data exists in batch forms and streaming formats‎, ‎necessitating the utilization of parallel hardware and software‎, ‎as well as stream processing‎, ‎to extract meaningful insights‎. ‎Currently‎, ‎the map-reduce method is the prevailing model for processing Big Data in the aviation industry‎. ‎This paper aims to analyze the evolving trends in aviation Big Data processing methods‎, ‎followed by a comprehensive investigation and discussion of Data analysis techniques‎. ‎We implement the map-reduce optimization of the K-Means algorithm in the Hadoop and Spark environments‎. ‎The K-Means map-reduce is a crucial and widely applied clustering method‎. ‎Finally‎, ‎we conduct a case study to analyze and compare aviation Big Data related to air traffic management in the USA using the K-Means map-reduce approach in the Hadoop and Spark environments‎. ‎The analyzed Dataset includes flight records‎. ‎The results demonstrate the suitability of this platform for aviation Big Data‎, ‎considering the characteristics of the aviation Dataset‎. ‎Furthermore‎, ‎this study presents the first application of the designed program for air traffic management‎.

Day by day Data volumes are increasing and Big Data analytics are analyzing massive Datasets, which increasingly occur in web-scale business intelligence problems. The aim of this paper is to demonstrate the use of modify Fuzzy TOPSIS approach for the big Data analytics platform selection, which may be used by businesses, public sector institutions to solve multiple criteria decision making problems.

Value creation is a major factor not only in the sustainability of organizations but also in the maximization of profit, customer retention, business goals fulfillment, and revenue. Value creation is a complex process, particularly when it involves the external and internal factors of the organization. When the value is intended to be created from Big Data scenarios, value creation entails being understood over a broader range of complexity. A question that arises here is how organizations can use this massive quantity of Data and create business value? The present study seeks to provide a model for creating organizational value using Big Data analytics (BDA). To this end, after reviewing the related literature and interviewing experts, the BDA-based organizational value creation model is developed. Accordingly, five hypotheses are formulated, and a questionnaire is prepared. Then, the respective questionnaire is given to the research statistical population (i. e., IT managers and experts, particularly those specializing in Data analysis) to test the research hypotheses. Afterward, the relationships among the model variables are scrutinized using the structural equation modeling (measurement and structural models). The results of the study indicate that investigating the infrastructures of the Big Data analytics, as well as the capabilities of the organization and those of Big Data analytics is the initial requirement to create organizational value using BDA. Thereby, the Big Data analytics strategy is formulated, and ultimately, the organizational value is created as well.