Membrane Clustering Using the PostgreSQL Database Management System

Tarczali, Tamás; Lehotay-Kéry, Péter; Kiss, Attila

doi:10.1007/978-3-030-55187-2_30

Cited by 3 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The maximal clustering performance was analyzed over different configurations of the system with Davies-Bouldin, Calinski-Harabasz and Silhouette indexes. We also experienced with the parameters of the algorithm used in this paper and measured with the Calinski-Harabasz index how these should be set in our previous paper [44]. We reached good average clustering validity scores with the combination of c 1 = 1.6, c 2 = 0.1, c 3 = 1.8, w min = 0.2, w max = 0.9, t max = 50.…”

Section: The Used Algorithmmentioning

confidence: 85%

“…In our previous paper [44], we made some experiments using the PostgreSQL database management system on three different datasets (Iris, Wine Recognition, and Breast Cancer Wisconsin (Diagnostic) datasets) from the well-known UCI Machine Learning Repository [45]. However, in big data applications, usually, NoSQL systems are utilized, since some of the frequent operations may be faster in such systems, as it is already mentioned earlier and as it is discussed in [2].…”

Section: The Used Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

P System–Based Clustering Methods Using NoSQL Databases

2021

Self Cite

View full text Add to dashboard Cite

Models of computation are fundamental notions in computer science; consequently, they have been the subject of countless research papers, with numerous novel models proposed even in recent years. Amongst a multitude of different approaches, many of these methods draw inspiration from the biological processes observed in nature. P systems, or membrane systems, make an analogy between the communication in computing and the flow of information that can be perceived in living organisms. These systems serve as a basis for various concepts, ranging from the fields of computational economics and robotics to the techniques of data clustering. In this paper, such utilization of these systems—membrane system–based clustering—is taken into focus. Considering the growing number of data stored worldwide, more and more data have to be handled by clustering algorithms too. To solve this issue, bringing these methods closer to the data, their main element provides several benefits. Database systems equip their users with, for instance, well-integrated security features and more direct control over the data itself. Our goal is if the type of the database management system is given, e.g., NoSQL, but the corporation or the research team can choose which specific database management system is used, then we give a perspective, how the algorithms written like this behave in such an environment, so that, based on this, a more substantiated decision can be made, meaning which database management system should be connected to the system. For this purpose, we discover the possibilities of a clustering algorithm based on P systems when used alongside NoSQL database systems, that are designed to manage big data. Variants over two competing databases, MongoDB and Redis, are evaluated and compared to identify the advantages and limitations of using such a solution in these systems.

show abstract

Section: The Used Algorithmmentioning

confidence: 85%

Section: The Used Algorithmmentioning

confidence: 99%

P System–Based Clustering Methods Using NoSQL Databases

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…We partially used the same membrane clustering method that we used in two of our previous studies [ 15 , 16 ]. In the first study, we were experimenting with membrane-based clustering on data points stored in PostgreSQL DBMS .…”

Section: Materials and Methodsmentioning

confidence: 99%

“…In this paper, we still focus on a given set of genomes as the dataset because our method has previously been validated on other kinds of data outside the field of computational biology in our previous works. These studies also analyzed the utilization of different database management systems as data sources [ 15 , 16 ].…”

Section: Related Workmentioning

confidence: 99%

Membrane Clustering of Coronavirus Variants Using Document Similarity

Lehotay-Kéry

Kiss

2022

Genes

View full text Add to dashboard Cite

Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models.

show abstract

“…Cluster formation among IoT nodes is carried by the MIC algorithm [42,43,44]. MIC is parallel computing algorithm, which derive from inspiration of the procedures that take place in the biological cell.…”

Section: Hybrid Topology Constructionmentioning

confidence: 99%