Dr. Hadoop: In Search of a Needle in a Haystack

Nayak, Sabuzima; Patgiri, Ripon

doi:10.1007/978-3-030-05366-6_8

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The Dr. Hadoop [24,68] is designed to achieve the infinite scalability of metadata. It is based on the Doubly Circular Linked List.…”

Section: Hashed-based Mdsmentioning

confidence: 99%

A Survey on Large Scale Metadata Server for Big Data Storage

Patgiri¹,

Nayak²

2020

Preprint

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Big Data is defined as high volume of variety of data with an exponential data growth rate. Data are amalgamated to generate revenue, which results a large data silo. Data are the oils of modern IT industries. Therefore, the data are growing at an exponential pace. The access mechanism of these data silos are defined by metadata. The metadata are decoupled from data server for various beneficial reasons. For instance, ease of maintenance. The metadata are stored in metadata server (MDS). Therefore, the study on the MDS is mandatory in designing of a large scale storage system. The MDS requires many parameters to augment with its architecture. The architecture of MDS depends on the demand of the storage systemâĂŹs requirements. Thus, MDS is categorized in various ways depending on the underlying architecture and design methodology. The article surveys on the various kinds of MDS architecture, designs, and methodologies. This article emphasizes on clustered MDS (cMDS) and the reports are prepared based on a) Bloom filter-based MDS, b) Client-funded MDS, c) Geo-aware MDS, d) Cache-aware MDS, e) Load-aware MDS, f) Hash-based MDS, and g) Tree-based MDS. Additionally, the article presents the issues and challenges of MDS for mammoth sized data.

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“…The Dr. Hadoop [24,68] is designed to achieve the infinite scalability of metadata. It is based on the Doubly Circular Linked List.…”

Section: Hashed-based Mdsmentioning

confidence: 99%

A Survey on Large Scale Metadata Server for Big Data Storage

Patgiri¹,

Nayak²

2020

Preprint

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“…It uses Bloom Filter to avoid unnecessary disk access. In Metadata server, Bloom Filter helps to eliminate unnecessary checking by the nodes that does not contain the requested data [30]. Singh et al [31] proposed a fuzzy-enabled folding approach using Bloom Filter to provide Bloom Filter-as-a-service.…”

Section: Bloom Filtermentioning

confidence: 99%

A Review on Role of Bloom Filter on DNA Assembly

Nayak

Patgiri

2019

IEEE Access

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The advancement of DNA assembly techniques has greatly boosted up the bioinformatics research and discovery. More precisely, DNA assembly has achieved tremendous popularity due to the ability to decode the hidden information in the DNA. DNA assembly is the process of finding the correct sequence of the nucleotide bases in DNA. The key challenges are a) size of the genomic data and, b) time to process the genomic data. Apparently, genomic data are voluminous consisting of many repeated fragments. The huge sized genomic data makes the DNA assembling a time consuming process. To address the space and time complexity, bloom filter is deployed in DNA assembling. Moreover, bloom filter plays a vital role in DNA processing to deal with the repeated data of DNA. A bloom filter is a probabilistic data structure for membership filter. Bloom filter uses a tiny amount of memory size to store information on the genomic data. However, DNA assembling is a very memory-intensive process. The whole process consists of many stages. In every stage, the repeated data need to be taken care of. Hence, bloom filter is deployed in every stage for its implementation. This paper presents the impact of bloom filter in DNA assembling process. It also gives a precise explanation on every aspect of the DNA assembling process. The focus of this paper is to review the techniques that implemented bloom filter. INDEX TERMS Bloom filter, DNA sequencing, DNA assembly, de novo, de Bruijn Graph, bioinformatics, big data.

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