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Cloud computing technology is quite inevitable in today's smart world. The excessive utilization of data mandates updated storage space, which is highly expensive and cloud storage is the best solution to it. As charges are levied for the utilized space, data redundancy must be avoided for the effective exploitation of cloud space. Data deduplication is a technique, which removes redundant data and conserves storage, bandwidth and charges. However, data retrieval upon deduplicated data is not well explored in the existing literature. This work attempts to present an effective retrieval framework for deduplicated data in a cloud environment by presenting two protocols namely Data Outsourcing Protocol (DOP) and Data Retrieval Protocol (DRP). The retrieval performance of the proposed approach is tested and compared with the existing approaches in terms of standard performance measures. The work performance of the proposed Deduplicated Data Retrieval (DDR) framework performs better in terms of retrieval precision, recall and time conservation rates. The average precision and recall rates attained by the proposed work are 97.9% and 95.75% respectively. [ABSTRACT FROM AUTHOR]

Understanding data deduplication in storage is essential for investigating the optimization of various data storage issues. For detecting and removing duplicate data, data deduplication has become an important and cost-effective optimization technique. Storage issues in the storage area for organizations exist, and if not conveyed to optimize there and then, a slower rate of storage capacity is expected. The proposed system (EdocDedup) addresses the aforementioned issue by applying data deduplication technique and implementing SHA-256 for hash value calculation and only keeping the unique hash values on an electronic document’s dataset containing word files, text files, html files, excel files, zip files, pdf files, and PowerPoint presentation files. By demanding the proposed technique there is a benefit in storage saved and a variety of duplicate files are explored efficiently. EdocDedup's performance is achieved through the use of useruploaded files. [ABSTRACT FROM AUTHOR]

The effectiveness of a Current Research Information System (CRIS) is based on satisfying essential institutional needs, or purposes, regarding the capture, processing and reporting of research-related activities and outcomes. These needs, or purposes, exemplified in the Code of Good Practice and introduced in 1998, have remained relatively constant over time. However, the scope and nature of the underlying data supporting these needs have grown in complexity, thus necessitating a concurrent increase in sophistication for how data quality is addressed and improved. This publication aims to introduce and analyse the implications and solutions required to improve data quality within the scope of fit for purpose in a CRIS context. Drawing from, and building on, data and information quality foundations and descriptions, a data quality framework is introduced, and detailed product functionality is described. A discussion on the combination of framework and functionality highlights how data quality can be improved in CRIS products. [ABSTRACT FROM AUTHOR]

This abstract provides an update on development and implementation progress of the Pure community module in the 5 years since the initial launch of the service. The paper revisits the conclusions of our 2018 presentation [1] , providing updates on the 'future developments' and encourages a discussion of the complexities in supporting multi-institutional collaborations. In addition, we will provide an overview of some key challenge areas we have been working on together with community customers and community owners (technical and operational). [ABSTRACT FROM AUTHOR]

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 detecting the appropriate chunk boundaries, which solves the challenges such as minimum throughput and maximum chunk size variance in the data stream. To provide the solution, we propose a new chunking algorithm called Dynamic Prime Chunking (DPC). The main goal of DPC is to dynamically change the window size within the prime value based on the minimum and maximum chunk size. According to the result, DPC provides high throughput and avoid significant chunk variance in the deduplication system. The implementation and experimental evaluation have been performed on the multimedia and operating system datasets. DPC has been compared with existing algorithms such as Rabin, TTTD, MAXP, and AE. Chunk Count, Chunking time, throughput, processing time, Bytes Saved per Second (BSPS) and Deduplication Elimination Ratio (DER) are the performance metrics analyzed in our work. Based on the analysis of the results, it is found that throughput and BSPS have improved. Firstly, DPC quantitatively improves throughput performance by more than 21% than AE. Secondly, BSPS increases a maximum of 11% than the existing AE algorithm. Due to the above reason, our algorithm minimizes the total processing time and achieves higher deduplication efficiency compared with the existing Content Defined Chunking (CDC) algorithms. [ABSTRACT FROM AUTHOR]

Closed-circuit television (CCTV) technology continuously captures and stores video streams. Users are typically required by policy to store all the captured video for a certain period. Accordingly, increasing the number of CCTV operation cycles and photographing positions expands the amount of data to be stored. However, expanding the available storage space for video data incurs increased costs. In recent years, this problem has been addressed with cloud storage solutions, which enable multiple users and devices to access and store data simultaneously. However, because of the large amount of data to be stored, a vast storage space is required. Consequently, cloud storage administrators need a way to store data more efficiently. To save storage space, deduplication technology has been proposed to prevent duplicate storage of the same data. However, because cloud storage is hosted on remote servers, data encryption technology must be applied to address data exposure issues. Although deduplication techniques for encrypted data have been studied, there have been various security vulnerabilities. We attempted to solve this problem by addressing various issues such as poison attacks, property forgery, and ownership management while removing the redundant data and handling the data more securely. [ABSTRACT FROM AUTHOR]

Cloud computing enables on-demand and ubiquitous access to a centralized pool of configurable resources such as networks, applications, and services. This makes that huge of enterprises and individual users outsource their data into the cloud server. As a result, the data volume in the cloud server is growing extremely fast. How to efficiently manage the ever-increasing datum is a new security challenge in cloud computing. Recently, secure deduplication techniques have attracted considerable interests in the both academic and industrial communities. It can not only provide the optimal usage of the storage and network bandwidth resources of cloud storage providers, but also reduce the storage cost of users. Although convergent encryption has been extensively adopted for secure deduplication, it inevitably suffers from the off-line brute-force dictionary attacks since the message usually can be predictable in practice. In order to address the above weakness, the notion of DupLESS was proposed in which the user can generate the convergent key with the help of a key server. We argue that the DupLESS does not work when the key server is corrupted by the cloud server. In this paper, we propose a new multi-server-aided deduplication scheme based on the threshold blind signature, which can effectively resist the collusion attack between the cloud server and multiple key servers. Furthermore, we prove that our construction can achieve the desired security properties. [ABSTRACT FROM AUTHOR]

Common Map of Academia (COMAC) is a system that keeps information about scientists and publications. It uses bibliographical information as a source of knowledge. In the article we show briefly this system. We present how we used that system for parts of Polish national CRIS: Polish national bibliography and newly created PolIndex - polish citation index. We present algorithms used for enhancing CRIS data: document deduplication, citations matching and authors identification. Finally we show performance statistics. [ABSTRACT FROM AUTHOR]

According to the characteristics of data de-duplication, the metadata indexing structure based on B+ tree was introduced mainly for continuous data access applications, in which the metadata was divided into two kinds by popular and cold, with different layout strategies. As a consequence, the low power disks can stay in the standby state for a longer period of time with better energy efficiency, while effectively reducing random disk accesses with no additional hardware overhead. The method proposed in this paper makes it possible to store the index of the medium on disk only, rather than on disk and memory in previous studies. [ABSTRACT FROM AUTHOR]