A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

Mazumdar, Somnath; Seybold, Daniel; Kritikos, Kyriakos; Verginadis, Yiannis

doi:10.1186/s40537-019-0178-3

Cited by 86 publications

(43 citation statements)

References 93 publications

(134 reference statements)

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“…To face Big Data challenges as explained above in Section 'Big Data characteristics' , data management platforms include advanced analytics and computational capabilities, and also leverage cloud possibilities [61,86] (Data storage and analysis in hybrid cloud). This kind of data management platforms enables building large information databases, Fig.…”

Section: The Evolution Of Big Data Monetizationmentioning

confidence: 99%

Big data monetization throughout Big Data Value Chain: a comprehensive review

et al. 2020

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Value creation is the rational goal of any organization. However, in a changing world, in terms of data, it is very difficult to find strategies to sustainably generate new value and improve operations. In this respect, generating value may mean revisiting some deeplyheld conceptions of different processes. For this, each organization has to ensure an efficient and updated management of value creation processes for both inter-organization and partners. Value chain (VC) is one of the key solutions to such dilemmas.

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Section: The Evolution Of Big Data Monetizationmentioning

confidence: 99%

Big data monetization throughout Big Data Value Chain: a comprehensive review

et al. 2020

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“…An overview of data storage and positioning procedures in the cloud big data biological system was clarified by Mazumdar, S., et al [19] in 2019. In this review paper, the best is given in the class diagram of cloud-driven big data arrangement together with the data storage strategies.…”

Section: Literaturementioning

confidence: 99%

Replication and Uploading Data on Cloud for Disaster Recovery Process

Prabha¹

2021

TURCOMAT

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Disaster recovery is a diligent issue in IT business. This issue is progressively significant in cloud computing, since Cloud Service Providers (CSPs) are bound to provide all facilities to their clients regardless of whether the server farm is down, because of a disaster. During the disaster, the data may be lost. To overcome this problem, replication is generated for each input data. The main objective of this paper is to upload different data on optimal location of cloud. The proposed system consists of three modules, namely, replica generation; choose optimal location and recovery process. Initially, to avoid the data loss, the input data are replicated. After replication process, the data are stored on cloud with the help of oppositional gravitational search algorithm (OGSA) which then retrieves only the request based data. Hence, we could avoid the data loss due to disaster. The presentation of proposed methodology is analyzed by different metrics comparing with various methods.

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“…The survey by Mazumdar et al [18] provided a better understanding of choosing the right database solution based non-functional requirements.…”

Section: B Comparisons Based On Effectivenessmentioning

confidence: 99%

How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB

Macák¹,

Stovcik²,

Bühnová³

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Digitalization is currently the key factor for progress, with a rising need for storing, collecting, and processing large amounts of data. In this context, NoSQL databases have become a popular storage solution, each specialized on a specific type of data. Next to that, the multi-model approach is designed to combine benefits from different types of databases, supporting several models for data. Despite its versatility, a multi-model database might not always be the best option, due to the risk of worse performance comparing to the single-model variants. It is hence crucial for software engineers to have access to benchmarks comparing the performance of multi-model and single-model variants. Moreover, in the current Big Data era, it is important to have cluster infrastructure considered within the benchmarks.In this paper, we aim to examine how the multi-model approach performs compared to its single-model variants. To this end, we compare the OrientDB multi-model database with the Neo4j graph database and the MongoDB document store. We do so in the cluster setup, to enhance state of the art in database benchmarks, which is not yet giving much insight into cluster-operating database performance.

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A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

Cited by 86 publications

References 93 publications

Big data monetization throughout Big Data Value Chain: a comprehensive review

Big data monetization throughout Big Data Value Chain: a comprehensive review

Replication and Uploading Data on Cloud for Disaster Recovery Process

How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB

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