scholarly journals P System–Based Clustering Methods Using NoSQL Databases

Computation ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 102
Author(s):  
Péter Lehotay-Kéry ◽  
Tamás Tarczali ◽  
Attila Kiss
Keyword(s):  
Management System ◽  
Database Management ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Database Systems ◽  
Database Management System ◽  
P Systems ◽  
Main Element ◽  
P System ◽  
Clustering Methods

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.

The Impact of Big Data on Security

Big Data ◽  
2016 ◽  
pp. 1495-1518
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

scholarly journals A better way for finding the optimal number of nodes in a distributed database management system

2010 ◽  
Vol 4 (2) ◽  
pp. 19-22
Author(s):  
Rashed Mustafa ◽  
Md Javed Hossain ◽  
Thomas Chowdhury
Keyword(s):  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Distributed Database ◽  
Optimal Number ◽  
Database Management System ◽  
Data Fragmentation ◽  
Large Databases ◽  
Very Large Databases ◽  
Distributed Database Management

Distributed Database Management System (DDBMS) is one of the prime concerns in distributed computing. The driving force of development of DDBMS is the demand of the applications that need to query very large databases (order of terabytes). Traditional Client- Server database systems are too slower to handle such applications. This paper presents a better way to find the optimal number of nodes in a distributed database management systems. Keywords: DDBMS, Data Fragmentation, Linear Search, RMI.   DOI: 10.3329/diujst.v4i2.4362 Daffodil International University Journal of Science and Technology Vol.4(2) 2009 pp.19-22

scholarly journals Index Self-tuning with Agent-based Databases

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Rogerio Luıs De Carvalho Costa ◽  
Sergio Lifschitz ◽  
Marcos Antonio Vaz Salles
Keyword(s):  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Database Management System ◽  
The Self ◽  
Agent Based ◽  
Integration Architecture ◽  
System Components ◽  
Intelligent Behavior ◽  
Self Tuning

The use of software agents as Database Management System components lead to database systems that may be configured and extended to support new requirements. We focus here with the self-tuning feature, which demands a somewhat intelligent behavior that agents could add to traditional DBMS modules. We propose in this paper an agent-based database architecture to deal with automatic index creation. Implementation issues are also discussed, for a built-in agents and DBMS integration architecture.

scholarly journals A geospatial database management system for the collection of medicinal plants

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Supattra Puttinaovarat ◽  
Paramate Horkaew
Keyword(s):  
Medicinal Plants ◽  
Real Time ◽  
Spatial Data ◽  
Management System ◽  
Database Management ◽  
Spatial Information ◽  
Database Systems ◽  
Database Management System ◽  
Real Time Analysis ◽  
Geospatial Database

Medicinal plants are increasingly used, both for medical applications and personal healthcare. However, existing herbal database systems for plant retrieval offer only basic information and do not support real-time analysis of the spatial aspects of plantations and distribution sites. Moreover, data records are usually static and not publicly available as they rely on costly proprietary software packages. To address these shortcomings, including limiting the time needed for collection and data processing, a novel medicinal plants geospatial database management system is proposed. The system allows localization of plant sites and data presentation on an interactive heat map displaying spatial information of plants selected by the user within a specific radius from the user’s location, including automatic presentation of an itinerary giving the optimal route between user location and plant destinations selected. The approach relies on dynamic and role-based data management, an interactive map that includes graphics and integrated geospatial analyses thanks to cross-platform, geographical a JavaScript library and Google API. Both spatial data and attributes are available in real time. The system would support effective collaboration, among herb farmers, government agencies, private investors, healthcare professionals and the general public with regard to various aspects of medicinal plants and their applications.

scholarly journals A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zhenni Jiang ◽  
Xiyu Liu ◽  
Minghe Sun
Keyword(s):  
Shannon Entropy ◽  
Clustering Algorithm ◽  
P Systems ◽  
P System ◽  
Clustering Methods ◽  
K Nearest Neighbors ◽  
Density Peak ◽  
New Variant ◽  
Real World Datasets ◽  
Density Peak Clustering

This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.

The Impact of Big Data on Security

2015 ◽  
pp. 276-298 ◽  
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

Cardiological Database Management System as a Mediator to Clinical Decision Support

1996 ◽  
Vol 35 (01) ◽  
pp. 52-58 ◽  
Author(s):  
A. Mavromatis ◽  
N. Maglaveras ◽  
A. Tsikotis ◽  
G. Pangalos ◽  
V. Ambrosiadou ◽  
...  
Keyword(s):  
Management System ◽  
Database Management ◽  
Medical Information ◽  
Object Oriented ◽  
Medical Personnel ◽  
Database Management System ◽  
Ease Of Use ◽  
Medical Database ◽  
Analysis And Design ◽  
Object Oriented Design

AbstractAn object-oriented medical database management system is presented for a typical cardiologic center, facilitating epidemiological trials. Object-oriented analysis and design were used for the system design, offering advantages for the integrity and extendibility of medical information systems. The system was developed using object-oriented design and programming methodology, the C++ language and the Borland Paradox Relational Data Base Management System on an MS-Windows NT environment. Particular attention was paid to system compatibility, portability, the ease of use, and the suitable design of the patient record so as to support the decisions of medical personnel in cardiovascular centers. The system was designed to accept complex, heterogeneous, distributed data in various formats and from different kinds of examinations such as Holter, Doppler and electrocardiography.

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