TY - JOUR
T1 - COMORP
T2 - Rapid prototyping for mathematical database cost models development
AU - Ouared, Abdelkader
AU - Amrani, Moussa
AU - Schobbens, Pierre Yves
N1 - Funding Information:
All authors approved the final version of the manuscript.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/12
Y1 - 2022/12
N2 - The database technology evolution trends had pushed the researchers to redesign and adapt the past mathematical database cost models with the consideration of additional aspects due to the emergence of this evolution. As a result, many scientists today propose database cost models by rethinking query processing and optimization with every change in hardware, workload, and applications. This requires a high level of domain expertise. However, assisting users to build database cost models by context-aware is difficult and an increasingly harder challenge. A new paradigm is needed to change cost model development methodology by designing it at a high level of abstraction to mitigate the gap between core database and conceptual modeling communities in order to shorten its long development cycle. Moreover, we need to support incrementally complex design of cost models and follow the evolution of database technologies that change rapidly and continuously to fit the new requirements. We investigate Model-Driven Engineering paradigms that enable database cost models, fast prototyping of modeling/analysis and optimize reusability of its integration components. This article presents a framework that aims to develop cost models as an extensible and customized kernel that provides metrics related to the most sensitive layers of database systems and assists the cost model composition process by combinations of fundamental primitives. We implement our framework to help designers/researchers create a successful cost model product semi-automatically correlate with their manifests. Experimental evaluations show that by using our framework, we can reduce the time spent by 60% on CM building, while being able to recommend useful components with up to 90%.
AB - The database technology evolution trends had pushed the researchers to redesign and adapt the past mathematical database cost models with the consideration of additional aspects due to the emergence of this evolution. As a result, many scientists today propose database cost models by rethinking query processing and optimization with every change in hardware, workload, and applications. This requires a high level of domain expertise. However, assisting users to build database cost models by context-aware is difficult and an increasingly harder challenge. A new paradigm is needed to change cost model development methodology by designing it at a high level of abstraction to mitigate the gap between core database and conceptual modeling communities in order to shorten its long development cycle. Moreover, we need to support incrementally complex design of cost models and follow the evolution of database technologies that change rapidly and continuously to fit the new requirements. We investigate Model-Driven Engineering paradigms that enable database cost models, fast prototyping of modeling/analysis and optimize reusability of its integration components. This article presents a framework that aims to develop cost models as an extensible and customized kernel that provides metrics related to the most sensitive layers of database systems and assists the cost model composition process by combinations of fundamental primitives. We implement our framework to help designers/researchers create a successful cost model product semi-automatically correlate with their manifests. Experimental evaluations show that by using our framework, we can reduce the time spent by 60% on CM building, while being able to recommend useful components with up to 90%.
KW - Cost model development
KW - Database performance
KW - Model driven engineering
KW - Rapid development
KW - Reusable components
UR - http://www.scopus.com/inward/record.url?scp=85141920060&partnerID=8YFLogxK
U2 - 10.1016/j.cola.2022.101173
DO - 10.1016/j.cola.2022.101173
M3 - Article
AN - SCOPUS:85141920060
SN - 2590-1184
VL - 73
JO - Journal of Computer Languages
JF - Journal of Computer Languages
M1 - 101173
ER -