Statistical Model Checking for Real-Time Database Management Systems: A Case Study

2019 ◽  
Author(s):  
Simin Cai ◽  
Barbara Gallina ◽  
Dag Nystrom ◽  
Cristina Seceleanu
Keyword(s):  
Model Checking ◽  
Statistical Model ◽  
Real Time ◽  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Statistical Model Checking ◽  
Real Time Database

In Silico Laboratory Experiments using Statistical Model Checking: A new model of the Palytoxin-Induced Pump Channel as Case Study

2020 ◽  
pp. 1-1
Author(s):  
Gabriel Domingo Vilallonga ◽  
Daniel Edgardo Riesco ◽  
Antonio-Carlos Guimaraes Almeida ◽  
Antonio Marcio Rodrigues ◽  
Sergio V. A. Campos
Keyword(s):  
Model Checking ◽  
Statistical Model ◽  
In Silico ◽  
Laboratory Experiments ◽  
Statistical Model Checking ◽  
New Model

CPU runtime optimization in data damage tracking quarantine and recovery (DTQR) scheme based on customized ANN

2021 ◽  
Vol 25 (3) ◽  
pp. 323-333
Author(s):  
M. Somasundara Rao ◽  
Koduganti Venkata Rao ◽  
M.H.M. Krishna Prasad
Keyword(s):  
Real Time ◽  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Accurate Data ◽  
Time Data ◽  
Security Systems ◽  
Real Time Data ◽  
Recovery Scheme ◽  
Damage Tracking

Database Management Systems (DBMS) are regularly used to store and process touchy endeavour information. In any case, it is beyond the realm of imagination to expect to verify the information by depending on the entrance control and security instruments of such frameworks alone; clients may handle their benefits or go around security systems to malevolently adjust and get to the information. Hence, we have developed a reliable, secure, and real-time data damage tracking Quarantine and recovery scheme using Customized ANN approach. The proposed DTQR scheme recovers the accurate data from any newer data and eliminates the fraudulent data. The approach also provides a solution for runtime problems occurring in the DBMS. Moreover, the proposed technique implemented in the working platform of JAVA and the results are analyzed with existing techniques to prove the efficiency of the proposed system.

Comparative Case Study Difference Between Azure Cloud SQL and Mongo Atlas MongoDB NoSQL Database

2021 ◽  
Vol 9 (7) ◽  
pp. 999-1002
Keyword(s):  
Relational Database ◽  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Comparative Case Study ◽  
Nosql Databases ◽  
Nosql Database ◽  
Relational Database Management ◽  
Relational Database Management Systems

Azure SQL and Atlas Mongodb NoSQL(Azure instance) databases are the most popular, systematic process to database solutions. Which Azure SQL database is also referred to as RDBMS (Relational Database Management Systems). The data are structured into tables or associations. The Atlas Mongodb NoSQL database is called a non-relational database management systems. The data are included in unstructured tables or associations. In this research, evaluate both the Azure SQL and Atlas Mongodb NoSQL databases. During the experiment compare the loading time, response time, and retrieval time of both Azure SQL and Atlas Mongodb NoSQL databases, and justify which one is fast, efficient and better performance.

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