Distance education (DE), which has evolved under the wings of information technologies in the last decade, has become a fundamental part of our modern education system. DE has not only replaced the traditional education method as in social sciences and lifelong learning opportunities but also has significantly strengthened traditional education in mathematics, science, and engineering fields that require practical and intensive study. However, it is deprived of supporting some key elements found in traditional educational approaches such as (i) modern computer laboratories with installed special software suitable for the student’s field of interest; (ii) adequate staff for maintenance and proper functioning of laboratories; (iii) face-to-face technical support; (iv) license fees. For students to overcome these shortcomings, a virtual application pool is needed where they can easily access all the necessary applications via remote access. This research aims to develop a platform-independent virtual laboratory environment for DE students. This article has been developed specifically to guide DE institutions and to make a positive contribution to the literature. Technology Acceptance Model (TAM) has been used to explain student behaviors. It was concluded that students using the platform performed more successful grades (12.89%) on laboratory assessments and that the students using the developed platform were found to be more satisfied with the education process.

The most important key features of this study are high performance, easy scalability and serverless architecture. In this way, the system can work with fewer hardware elements and be more robust than others that use name node architecture. Also, both the reliability and performance of the system are significantly increased by separating replication nodes from data nodes. As a result, a complete big data solution that is easy to manage and performs well has been produced and successfully used.

Keywords: encoding algorithm; compression; Huffman encoding algorithm; arithmetic coding algorithm

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Digital medical image usage is common in health services and clinics. These data have a vital importance for diagnosis and treatment; therefore, preservation, protection, and archiving of these data are a challenge. Rapidly growing file sizes differentiated data formats and increasing number of files constitute big data, which traditional systems do not have the capability to process and store these data. This study investigates an efficient middle layer platform based on Hadoop and MongoDB architecture using the state-of-the-art technologies in the literature. We have developed this system to improve the medical image compression method that we have developed before to create a middle layer platform that performs data compression and archiving operations. With this study, a platform using MapReduce programming model on Hadoop has been developed that can be scalable. MongoDB, a NoSQL database, has been used to satisfy performance requirements of the platform. A four-node Hadoop cluster has been built to evaluate the developed platform and execute distributed MapReduce algorithms. The actual patient medical images have been used to validate the performance of the platform. The processing of test images takes 15,599 seconds on a single node, but on the developed platform, this takes 8,153 seconds. Moreover, due to the medical imaging processing package used in the proposed method, the compression ratio values produced for the non-ROI image are between 92.12% and 97.84%. In conclusion, the proposed platform provides a cloud-based integrated solution to the medical image archiving problem.