A performance evaluation of NoSQL databases to manage proteomics data

Messaoudi, Chaimaa; Fissoune, Rachida; Badir, Hassan

doi:10.1504/ijdmb.2018.095556

Cited by 4 publications

(3 citation statements)

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“…As a part of NoSQL benchmarks, the multi-model database benchmark is listed separately due to the particularity of its data model. According to Messaoudi et al (2017Messaoudi et al ( , 2018, in biomedical big data, the authors selected a single multi-model database OrientDB and a polyglot persistence instance composed of MongoDB and Neo4j to carry out performance evaluation with multiple workloads, such as insertion, deletion, and search operations. The results showed that MongoDB performed better than OrientDB in processing document data, and OrientDB performed better than Neo4j in querying graph data when the depth of the graph reached three layers.…”

Section: Multi-model Database Benchmarksmentioning

confidence: 99%

A Benchmark for Performance Evaluation of a Multi-Model Database vs. Polyglot Persistence

Sheng

Nedjah

et al. 2023

Journal of Database Management

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As the need for handling data from various sources becomes crucial for making optimal decisions, managing multi-model data has become a key area of research. Currently, it is challenging to strike a balance between two methods: polyglot persistence and multi-model databases. Moreover, existing studies suggest that current benchmarks are not completely suitable for comparing these two methods, whether in terms of test datasets, workloads, or metrics. To address this issue, the authors introduce MDBench, an end-to-end benchmark tool. Based on the multi-model dataset and proposed workloads, the experiments reveal that ArangoDB is superior at insertion operations of graph data, while the polyglot persistence instance is better at handling the deletion operations of document data. When it comes to multi-thread and associated queries to multiple tables, the polyglot persistence outperforms ArangoDB in both execution time and resource usage. However, ArangoDB has the edge over MongoDB and Neo4j regarding reliability and availability.

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Section: Multi-model Database Benchmarksmentioning

confidence: 99%

A Benchmark for Performance Evaluation of a Multi-Model Database vs. Polyglot Persistence

Sheng

Nedjah

et al. 2023

Journal of Database Management

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“…The difficulty in using biomedical data is mainly due to the great heterogeneity of the data sources: querying them and exploiting the wealth of information they contain is a complex task full of obstacles. The problems that the researchers are required to face are mainly due to syntactic and semantic conflicts between data sources (Messaoudi, Fissoune, & Hassan, 2016). Syntactic conflicts are related to the diversity and multiplicity of models (structured, semi-structured, unstructured) and data formats.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the analysis of existing systems and in accordance with (Messaoudi, Fissoune, & Hassan, 2016), the current approaches to biomedical data integration, like those described so far, can be broadly classified into three categories: data warehouse (i.e., databases that integrate a selected set of data into a common schema); linked data integration systems (i.e., based on the adoption of semantic web standards); workflow-based integration systems (i.e., integrating external data sources, based on a predefined pattern, to respond to a specific request).…”

Section: Related Workmentioning

confidence: 99%

A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

Capuano

Foggia

Greco

et al. 2022

International Journal on Semantic Web and Information Systems

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Understanding the role played by genetic variations in diseases, exploring genomic variants and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, we propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis.

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