DeepCM: Deep neural networks to improve accuracy prediction of database cost models

Ouared, Abdelkader; Chadli, Abdelhafid; Daoud, M.

doi:10.1002/cpe.6724

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…Note that they are many impact factors and interactions between them, that can be hidden and significantly impact the s selection. 12…”

Section: Physical Design Self-tuningmentioning

confidence: 99%

“…[18][19][20] For more details, please refer to a survey of AI capability inside databases. 20 Several studies investigate the deployment of AI solutions to rethinking query processing/optimization 12,[21][22][23][24] and to select the best query plans. 25,26 In Reference 27, the authors highlight the importance of modular designs that studies self-driving data management.…”

Section: Ai-based Database Designmentioning

confidence: 99%

“…However, selecting the

𝒫 𝒮

s in the physical design phase by considering the complex system as a whole is still a hard task. Note that they are many impact factors and interactions between them, that can be hidden and significantly impact the

𝒫 𝒮

s selection 12 …”

Section: Introductionmentioning

confidence: 99%

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Explainable AI for DBA: Bridging the DBA's experience and machine learning in tuning database systems

Ouared

Amrani

Schobbens

2023

Concurrency and Computation

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SummaryRecently artificial intelligence techniques in the database community have become a driver for many database applications. The proposed solution adopting AI in the core database shows that incorporating AI improves the query processing and the self‐tuning of database systems. In traditional systems, self‐tuning database systems are commonly addressed with heuristics to suggest the physical structures (e.g., creation of indexes and materialized views) that enable the fastest execution of queries. However, existing designer tools do not explain/justify how the system behaves and the reasoning behind tuning activities. Moreover, these tools do not keep the database administrator (DBA) in the loop of the optimization process to trust some of the automatic tuning decisions. To address this problem, we introduce a framework called Explain‐Tun that enables to predict and explain self‐tuning actions with transparent strategy from historical data using two explicit models, that is, decision tree and random forests. First, we propose AI‐based DBMS to explain how to select physical structures and provide decision rules extracted by machine learning (ML) as a designed plug‐gable component. Second, a goal‐oriented model to keep DBA in the loop of the optimization process in order to manipulate ML models as CRUD entities. Finally, we evaluate our approach on three use cases, results show that bridging the DBA's experience and ML make sense in tuning database systems.

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“…Note that they are many impact factors and interactions between them, that can be hidden and significantly impact the s selection. 12…”

Section: Physical Design Self-tuningmentioning

confidence: 99%

Section: Ai-based Database Designmentioning

confidence: 99%

See 1 more Smart Citation

Explainable AI for DBA: Bridging the DBA's experience and machine learning in tuning database systems

Ouared

Amrani

Schobbens

2023

Concurrency and Computation

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“…A framework named DeepCM was introduced in Reference 23 to improve the prediction accuracy. However, it failed to perform the preprocessing.…”

Section: Related Workmentioning

confidence: 99%

Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction

Chinnasamy

Balasubramanian

2022

Concurrency and Computation

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Early and accurately predicting the students' dropout enables schools to recognize the students based on available educational data. The early student dropout prediction is a major concern of education administrators. The existing classification techniques were unable to handle the early stage accurate performance of student dropout prediction with maximum accuracy and minimum time. In order to resolve the issue, a novel technique called rank biserial Otsuka-Ochiai stochastic embedded feature selection based bivariate kernelized regressive bootstrap aggregative classifier (RBOOSEFS-BKBAC) is motivated to perform student dropout prediction. The aim of the designing RBOOSEFS-BKBAC is to improve student dropout accuracy and minimal time consumption. Initially, the data preprocessing is to perform the data normalization, data cleaning, and duplicate data removal. Next, rank biserial correlation is used for discovering the correlated features. Followed by, Otsuka-Ochiai stochastic neighbor embedded feature selection is carried out to select significant features. Finally, bivariate kernelized regressive bootstrap aggregative classification technique is to perform classification with help of weak classifier. By using Bucklin voting scheme, the classification outcomes are obtained for increasing prediction accuracy as well as minimizing error. Experimental evaluation is performed by using Student-Drop-India2016 dataset with different metrics such as prediction accuracy, precision, recall, F-measure, as well as time. The result of proposed RBOOSEFS-BKBAC technique is provided that the higher prediction accuracy by 5% and lesser the prediction time by 15%, as compared to the state-of-the-art methods. K E Y W O R D Sbivariate kernelized regressive bootstrap aggregative classification, Otsuka-Ochiai stochastic neighbor embedded feature selection, preprocessing, rank biserial correlation-based feature extraction, school student dropout prediction INTRODUCTIONStudents' dropout is a difficult issue in the learning process for both the individual and society. Early prediction is a main challenge in the dropout prediction. Predicting the student's at-risk of dropout offers various benefits to educators, students, and the educational institution. Machine learning algorithms were applied in education to address the predicting dropout of students issue from schools. So that potential drop outs are documented

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A model-based DevOps process for development of mathematical database cost models

2023

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DeepCM: Deep neural networks to improve accuracy prediction of database cost models

Cited by 8 publications

References 38 publications

Explainable AI for DBA: Bridging the DBA's experience and machine learning in tuning database systems

Explainable AI for DBA: Bridging the DBA's experience and machine learning in tuning database systems

Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction

A model-based DevOps process for development of mathematical database cost models

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