Performance Benchmarking of Key-Value Store NoSQL Databases

Since the invention of the internet for military and academic research purposes, it has evolved to meet the demands of the increasing number of users on the network, who have their scope beyond military and academics. As the scope of the network expanded maintaining its security became a matter of increasing importance. With various users and interconnections of more diversified networks, the internet needs to be maintained as securely as possible for the transmission of sensitive information to be one hundred per cent safe; several anomalies may intrude on private networks. Several research works have been released around network security and this research seeks to add to the already existing body of knowledge by expounding on these attacks, proffering efficient measures to detect network intrusions, and introducing an ensemble classifier: a combination of 3 different machine learning algorithms. An ensemble classifier is used for detecting remote to local (R2L) attacks, which showed the lowest level of accuracy when the network dataset is tested using single machine learning models but the ensemble classifier gives an overall efficiency of 99.8%.

Section: Introductionmentioning

confidence: 99%

Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks

Abdulkareem,

Somefun,

Mutalub

et al. 2024

“…In addition, performance analysis of data storage is a critical issues especially for not structured query language (NoSQL) databases. Using the Yahoo cloud serving benchmark an analysis was carried out on the Redis and SSDB databases [11]. Performance evaluation of dataset is relevant because programmer expend time examining different classification techniques.…”

Section: Introductionmentioning

confidence: 99%

A systematic mapping study of performance analysis and modelling of cloud systems and applications

Odun-Ayo

Williams

Odusami

et al. 2021

Cloud computing is a paradigm that uses utility-driven models in providing dynamic services to clients at all levels. Performance analysis and modelling is essential because of service level agreement guarantees. Studies on performance analysis and modelling are increasing in a productive manner on the cloud landscape on issues like virtual machines and data storage. The objective of this study is to conduct a systematic mapping study of performance analysis and modelling of cloud systems and applications. A systematic mapping study is useful in visualization and summarizing the research carried in an area of interest. The systematic study provided an overview of studies on this subject by using a structure, based on categorization. The results are presented in terms of research such as evaluation and solution, and contribution such as tools and method utilized. The results showed that there were more discussions on optimization in relation to tool, method and process with 6.42%, 14.29% and 7.62% respectively. In addition, analysis based on designs in terms of model had 14.29% and publication relating to optimization in terms of evaluation research had 9.77%, validation 7.52%, experience 3.01%, and solution 10.51%. Research gaps were identified and should motivate researchers in pursuing further research directions

“…One of the major challenges is how to handle an increasing amount of heterogeneous IoT data with a variety of data types and data sources. Despite the popularity of relational databases, the scalability of the NoSQL database model and the document-centric data structure of NXD databases appear to be promising features for an efficient IoT system [20]. The results in [21] show that the NoSQL database is the best choice for query speed, whereas NXD is advantageous in terms of flexibility and extensibility, which are essential to cooperate with the characteristics of IoT data.…”

mentioning

confidence: 99%

A native enhanced elastic extension tables multi-tenant database

Banhawy

Saber

Amer

2020

A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.