• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1342-1359
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• 2021

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.

• 한국언어정보학회지:언어와정보
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• 제9권2호
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• pp.49-68
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• 2005

Application of chunking to English and some other European languages has shown that it is a viable parsing mechanism for natural languages. Although a small number of attempts have been made to apply chunking to the analysis of the Korean language, it still is not clear enough what criteria there are to identify appropriate units of chunking, and how efficient and valid the chunking algorithms would be when applied to some authentic Korean texts. The purpose of this research is to provide an alternative set of algorithms for chunking Korean, and to implement them, and to test them against some English-Korean parallel corpora, which is English and Korean bibles matched sentence by sentence. It is shown in the paper that aligning related texts and identifying matched phrases between the two languages can be achieved through appropriate chunking and matching algorithms defined on the morphologically-tagged parallel corpus. Chunking and matching processes are based on the content words rather than the function words, and the matching itself is done in terms of the transfer dictionary. The implementation is done in C and XML, and can be accessed through the Internet.