A System Performance Estimation Model for Cassandra Database

2016 ◽  
Vol 9 (3) ◽  
pp. 123-136
Author(s):  
Bo-Qian Wang ◽  
Qi Yu ◽  
Xin Liu ◽  
Li Shen ◽  
Zhi-ying Wang
Keyword(s):  
System Performance ◽  
Performance Estimation ◽  
Estimation Model ◽  
Performance Estimation Model

scholarly journals Categorification of the Müller-Wichards System Performance Estimation Model: Model Symmetries, Invariants, and Closed Forms

Systems ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 6
Author(s):  
Allen D. Parks ◽  
David J. Marchette
Keyword(s):  
Closed Form ◽  
System Performance ◽  
Algebraic Method ◽  
Expressive Power ◽  
Computer Applications ◽  
Performance Estimation ◽  
Parallel Computer ◽  
Estimation Model ◽  
Data Movement ◽  
Fundamental Symmetry

The Müller-Wichards model (MW) is an algebraic method that quantitatively estimates the performance of sequential and/or parallel computer applications. Because of category theory’s expressive power and mathematical precision, a category theoretic reformulation of MW, i.e., CMW, is presented in this paper. The CMW is effectively numerically equivalent to MW and can be used to estimate the performance of any system that can be represented as numerical sequences of arithmetic, data movement, and delay processes. The CMW fundamental symmetry group is introduced and CMW’s category theoretic formalism is used to facilitate the identification of associated model invariants. The formalism also yields a natural approach to dividing systems into subsystems in a manner that preserves performance. Closed form models are developed and studied statistically, and special case closed form models are used to abstractly quantify the effect of parallelization upon processing time vs. loading, as well as to establish a system performance stationary action principle.

scholarly journals A novel academic performance estimation model using two stage feature selection

2020 ◽  
Vol 19 (3) ◽  
pp. 1610
Author(s):  
Pamela Chaudhury ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Performance Estimation ◽  
Estimation Model ◽  
Proposed Model ◽  
Differential Evolutionary Algorithm ◽  
Performance Estimation Model ◽  
Using Data

<span lang="EN-GB">Educational data mining has gained tremendous interest from researchers across the globe. Using data mining techniques in the field of education several significant findings have been made. Accurate academic performance estimation is a challenging task. In this study we have developed a novel model to estimate the academic performance of students. Techniques like conversion of categorical attributes into dummy variables, classification, two staged feature selection and an improved differential evolutionary algorithm were used. Our proposed model outperformed existing models of students’ academic performance determination and gave a new direction to it. The proposed model can help not only to reduce the number of academic failures but also help to comprehend the factors contributing to a student’s  academic performance (poor, average or outstanding).Computer</span>

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