Large Scale Deduplication Analysis Using Multigraph Pattern Matching Algorithm - Doss, S.A. Amala Nirmal; Jeevitha, P (International Journal of Computer Sciences and Engineering, 2025)
As information is rising each day, thus it's terribly tough task to regulate storage devices for this volatile development of digital information. Information reduction has developed into terribly important drawback. Deduplication moves toward plays an important role to get rid of redundancy in massive scale cluster massive information storage. Existing deduplication strategies don't work effectively in several things. Overlapping and slicing formula is employed for deduplication method in existing system absorb with high memory with a lot of time interval. Recently, the info deduplication cluster has matured to be a significant want of most profitable and investigate backup systems. Information deduplication cluster become accepted in storage system for information backup and archiving. Several researchers specialize in deduplication cluster by that to cut back alternative redundant information. Particularly pattern matching deduplication cluster becomes well-liked. We have a tendency to projected multi graph pattern matching formula (MGPMA) in reduplication in massive information with higher potency. The technique of mixing similarity with neighborhood is applying to the deduplication cluster with bloom filter. As associate economical information removal move toward it exploits information redundancy. As a result, deduplication systems improve storage consumption whereas reducing time delay. Finally, the experimentation shows the system have a decent performance.