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Abstract
Current big sensing data processing on Cloud have adopted some data compression techniques. However, due to the high volume and velocity of big sensing data, traditional data compression techniques lack sufficient efficiency and scalability for data processing. Based on specific on-Cloud data compression requirements, propose a novel scalable data compression approach based on calculating similarity among the partitioned data chunks. Instead of compressing basic data units, the compression will be conducted over partitioned data chunks. Map Reduce is used for algorithm implementation to achieve extra scalability on Cloud.
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How to Cite
C.Navamani, & M.Jayavel. (2018). Exploring Application Level Semantic for Data Compression . International Journal of Intellectual Advancements and Research in Engineering Computations, 6(2), 1487–1491. Retrieved from https://ijiarec.com/ijiarec/article/view/682