A performance evaluation of NoSQL databases to manage proteomics data

Messaoudi, Chaimaa; Fissoune, Rachida; Badir, Hassan

doi:10.1504/ijdmb.2018.10016724

Cited by 2 publications

(3 citation statements)

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“…They found out that OrientDB better handles deeper levels of traversal. A similar study by Messaoudi et al [14] uses, as in the previous case, Neo4j, MongoDB, and OrientDB. They evaluate databases' ability to manage proteomics data.…”

Section: A Comparisons Based On Performancementioning

confidence: 84%

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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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Section: A Comparisons Based On Performancementioning

confidence: 84%

“…There are many comparisons of multi-model databases with different representatives of its single-model variants. The significant number of these comparisons used OrientDB and compared it with Neo4j and MongoDB [12], [14]. However, none of them used a cluster setup in their comparison.…”

Section: A Comparisons Based On Performancementioning

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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“…For specific application to porphyrias, we selected all genetic polymorphisms annotated to be associated with any of the porphyrias, as well as their associated RSID, SNP location in the genome, and degree of pathogenicity 85, 86 . Participants’ raw genomics data and environmental data are stored in a non-relational database, which has been proven to be more efficient than relational databases for storing and accessing genomic data 87, 88 . A secure, HIPAA-compliant human subject meta-information database will be built as part of the next iteration of development 89, 90 .…”

Section: Methods and Implementationmentioning

confidence: 99%

Iron Hack - A symposium/hackathon focused on porphyrias, Friedreich’s ataxia, and other rare iron-related diseases

et al. 2019

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Background: Basic and clinical scientific research at the University of South Florida (USF) have intersected to support a multi-faceted approach around a common focus on rare iron-related diseases. We proposed a modified version of the National Center for Biotechnology Information’s (NCBI) Hackathon-model to take full advantage of local expertise in building “Iron Hack”, a rare disease-focused hackathon. As the collaborative, problem-solving nature of hackathons tends to attract participants of highly-diverse backgrounds, organizers facilitated a symposium on rare iron-related diseases, specifically porphyrias and Friedreich’s ataxia, pitched at general audiences. Methods: The hackathon was structured to begin each day with presentations by expert clinicians, genetic counselors, researchers focused on molecular and cellular biology, public health/global health, genetics/genomics, computational biology, bioinformatics, biomolecular science, bioengineering, and computer science, as well as guest speakers from the American Porphyria Foundation (APF) and Friedreich’s Ataxia Research Alliance (FARA) to inform participants as to the human impact of these diseases. Results: As a result of this hackathon, we developed resources that are relevant not only to these specific disease-models, but also to other rare diseases and general bioinformatics problems. Within two and a half days, “Iron Hack” participants successfully built collaborative projects to visualize data, build databases, improve rare disease diagnosis, and study rare-disease inheritance. Conclusions: The purpose of this manuscript is to demonstrate the utility of a hackathon model to generate prototypes of generalizable tools for a given disease and train clinicians and data scientists to interact more effectively.

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A performance evaluation of NoSQL databases to manage proteomics data

Cited by 2 publications

References 0 publications

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

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

Iron Hack - A symposium/hackathon focused on porphyrias, Friedreich’s ataxia, and other rare iron-related diseases

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