Cloud storage is essential for managing user data to store and retrieve from the distributed data centre.The storage service is distributed as pay a service for accessing the size to collect the data.Due to the massiv...Cloud storage is essential for managing user data to store and retrieve from the distributed data centre.The storage service is distributed as pay a service for accessing the size to collect the data.Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy,duplication leads to increase storage space.The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis.It creates a complex nature to increase the storage consumption under cost.To resolve this problem,this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication(HIBD)based on Segmented Bind Linkage(SBL)Methods for reducing storage in a cloud environment.Initially,preprocessing is done using the sparse augmentation technique.Further,the preprocessed files are segmented into blocks to make Hash-Index.The block of the contents is compared with other files through Semantic Content Source Deduplication(SCSD),which identifies the similar content presence between the file.Based on the content presence count,the Distance Vector Weightage Correlation(DVWC)estimates the document similarity weight,and related files are grouped into a cluster.Finally,the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case.This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.展开更多
Eukaryotic genomes contain a significant fraction of repeats, which have very important biomedical function. Thus, aligning repeats from short sequences back to reference genome is the key step for further genome anal...Eukaryotic genomes contain a significant fraction of repeats, which have very important biomedical function. Thus, aligning repeats from short sequences back to reference genome is the key step for further genome analysis. Unfortunately, the current aligning algorithms performed poorly in distinguishing repeats and nonrepeats. To this end, we proposed a new algorithm, named HashRepAligner, to address this problem. Finally, the cross comparison with other algorithms was performed, and the results indicated that HashRepAligner outperformed other aligners in terms of the detecting repeats.展开更多
文摘Cloud storage is essential for managing user data to store and retrieve from the distributed data centre.The storage service is distributed as pay a service for accessing the size to collect the data.Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy,duplication leads to increase storage space.The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis.It creates a complex nature to increase the storage consumption under cost.To resolve this problem,this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication(HIBD)based on Segmented Bind Linkage(SBL)Methods for reducing storage in a cloud environment.Initially,preprocessing is done using the sparse augmentation technique.Further,the preprocessed files are segmented into blocks to make Hash-Index.The block of the contents is compared with other files through Semantic Content Source Deduplication(SCSD),which identifies the similar content presence between the file.Based on the content presence count,the Distance Vector Weightage Correlation(DVWC)estimates the document similarity weight,and related files are grouped into a cluster.Finally,the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case.This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.
文摘Eukaryotic genomes contain a significant fraction of repeats, which have very important biomedical function. Thus, aligning repeats from short sequences back to reference genome is the key step for further genome analysis. Unfortunately, the current aligning algorithms performed poorly in distinguishing repeats and nonrepeats. To this end, we proposed a new algorithm, named HashRepAligner, to address this problem. Finally, the cross comparison with other algorithms was performed, and the results indicated that HashRepAligner outperformed other aligners in terms of the detecting repeats.
