Learning has transcended into a life-long endeavor in the information age. It is no longer restricted to confines of formal classrooms. Consequently, a student is not restricted to traditional learning resources like teachers, textbooks or printed content. Digital resources available on the Internet form a very significant component of self-learning. Copious volumes of learning resources without legal barriers to self-learning reside in digital repositories, educational institution portals and on numerous websites. Learners wishing to utilize the web for personalized learning are faced with a daunting array of content to wade through and select the suitable ones to fulfill his/her learning objectives. Therefore, it is not a question of availability; it is one of relevance and suitability. Typically, in addition to time constraints, learners lack the expertise to screen content for effective eLearning. Adaptive hypermedia systems (AHSs) offer a path to harnessing this large volume of learning resources for personalized learning. This review paper provides a concise and coherent discussion about the evolution of AHSs along with the challenges that need to be addressed for effectively harnessing openly available educational resources referred to as open corpus resources (OCRs).
Data deduplication is a specialised data compression technique used in computers to get rid of redundant copies of repeated data. Intelligent (data) compression and single-instance (data) storage are words that are similar and partly interchangeable. This method may be used to increase storage efficiency and the quantity of data that has to be sent should be reduced over networks. Unique data chunks, or byte patterns, are found and saved throughout the deduplication process through a process of analysis. Recent studies have demonstrated that main storage systems in the Cloud offer moderate to high levels of data redundancy. It demonstrates that as a result of the small I/O requests to redundant data's comparably high temporal access locality, data redundancy displays a substantially greater degree of intensity on the I/O path than that on discs. The system in this project suggests using a performance-oriented I/O deduplication technique known as POD. It is used to increase the I/O performance of main storage systems in the Cloud without compromising the latter's capacity savings.
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