Data deduplication is widely used to reduce the size of backup workloads, but it has the
known disadvantage of causing poor data locality, also referred to as the fragmentation …

Efficiently computing the weighted Jaccard similarity has become an active research topic in
machine learning and theory. For sparse data, the standard technique is based on the …

The emergence of big data applications makes efficient representation for large-scale
dynamic data sets a challenge. The state-of-the-art design, ie, the dynamic cuckoo filter …

Multiple data reduction techniques have been investigated to lower storage costs for a wide
variety of customers. In this work, we focus on similarity-based delta compression, which …

Data deduplication techniques construct an index consisting of fingerprint entries to identify
and eliminate duplicated copies of repeating data. The bottleneck of disk-based index …

The1 Jaccard similarity has been widely used in search and machine learning, especially in
industrial practice. For binary (0/1) data, the Jaccard similarity is often called the …

Deduplication decreases the physical occupancy of files in a storage volume by removing
duplicate copies of data chunks, but creates data-sharing dependencies that complicate …

Cloud backup is becoming the preferred way for users to support disaster recovery. In
addition to its convenience, users are deeply concerned about reducing storage costs in the …

The data deduplication technique identifies the duplicates and minimizes the redundant
storage data in the backup server. The chunk level deduplication plays a significant role in …

Large scale machine learning (ML) and deep learning (DL) platforms face challenges when
integrated with deduplication enabled storage clusters. In the quest to achieve smart and …