Performance Testing of Intelligent Data Layer

With the growing popularity of Object-Realational Mapping (ORM) Frameworks, the performance requirements of the applications that implement them are also growing. Thus, it is necessary to examine whether our proposed Intelligent Data Layer (IDL) can handle application requests efficiently enough compared to conventional ORM Framework implementations. Thus, an environment has been created to simulate the behavior of the application on a production server. From the queries performed and the data collected, it will then be evaluated whether IDL is suitable for use in a production environment and how much of an asset it can be in a practical environment.

Authors

Institutions

Publication Details

Published
2024-11-13
DOI
https://doi.org/10.1109/informatics62280.2024.10900775
Primary Topic
Graph Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Performance Testing of Intelligent Data Layer

Monika Borkovcová, Filip Majerík
Graph Theory and Algorithms
article

Performance Testing of Intelligent Data Layer

Monika Borkovcová, Filip Majerík
article en

Abstract

With the growing popularity of Object-Realational Mapping (ORM) Frameworks, the performance requirements of the applications that implement them are also growing. Thus, it is necessary to examine whether our proposed Intelligent Data Layer (IDL) can handle application requests efficiently enough compared to conventional ORM Framework implementations. Thus, an environment has been created to simulate the behavior of the application on a production server. From the queries performed and the data collected, it will then be evaluated whether IDL is suitable for use in a production environment and how much of an asset it can be in a practical environment.

University of Pardubice (CZ)
Erasmus+
Openalex Percentile: Top 72%
Graph Theory and Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.