CostDL: A Cost Models Description Language for Performance Metrics in Database

Ouared, Abdelkader; Ouhammou, Yassine; Bellatreche, Ladjel

doi:10.1109/iceccs.2016.031

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…From the industry and academia, there has been much research on physical database design problems such as plan optimization, Knob tuning, buffer pool sizes, Index and view recommendation, automate physical data layout and so forth. 4,5,16,17 Advisors in turn, invokes the existing DBMS via its API leveraging the what-if physical design analysis capabilities. After evaluating the output design sent back to DBA, he decides to materialize the designer's output or merely modifying some of its input parameters, we can cite Index Tuning Wizard, 7 Tuning Advisor in Microsoft SQL Server, 8 Teradata's Index Wizard, 9 and IBM DB2's Design Advisor.…”

Section: Traditional Database Design Toolsmentioning

confidence: 99%

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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Section: Traditional Database Design Toolsmentioning

confidence: 99%

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

Ouared

Amrani

Schobbens

2023

Concurrency and Computation

Self Cite

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“…The same words may differently be arranged in a document, that is, “CPU,” “The central processing unit” or “Device processing,” and much more. We have proposed a cost model description language called CostDL 16 to homogenize the way cost models are expressed (for more details, please refer to our previous work of Reference 16). The CostDL language is a Domain Specific Language (DSL) dedicated to cost models domain.…”

Section: Our Frameworkmentioning

confidence: 99%

“…Variability view of an excerpt of context parameters and their categories. (A) Excerpt of the

𝒞 ℳ

context feature model 16 . (B) Example of

{𝒞 ℳ}_{e x p}

context instance…”

Section: Our Frameworkmentioning

confidence: 99%

“…Moreover, we have noticed that

𝒞 ℳ

s can be constructed as machine‐interpretable entities to ease their share, adaptation, and reuse. Therefore, our reflection process led us to propose in Reference 16 a cost model description language called CostDL 16 to homogenize the way such cost models are expressed. CostDL allows describing different parameters and formulas.…”

Section: Introductionmentioning

confidence: 99%

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Capitalizing the database cost models process through a service‐based pipeline

Ouared

Ouhammou

2021

Concurrency and Computation

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Designing a database cost model is one of the main research topics related to the physical design phase. It follows the evolution of database technology in order to evaluate and quantify the performance metrics (e.g., response time, energy consumption, etc.). Therefore, it makes the community researchers sensitive to the generated results.However, reusing and comparing database cost models require extracting related information manually from the research publications. This process is error-prone and time-consuming. Unfortunately, many researchers claim the difficulty of surveying and reproducing cost models already published in several/journal articles and/or reports. This difficulty is due to the absence of a process describing the cost model itself formally as well as the context of its utilization. This article presents an approach enabling the extraction of cost models information (context, parameters, features, etc.) as a set of orchestrated services. These services are implemented using natural language processing and machine-learning techniques via a work-flow pipeline inspired by DevOps practices. We illustrate our approach on a case study to stress the feasibility and benefits of our proposal by emphasizing the reproduction and automatization facilities.

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“…A CM is difficult to develop, since it includes several parameters belonging to databases, platforms, DBMS, queries, devices, etc. Ouared et al (2016). The evolution of non-functional requirements, databases, processing devises, storage systems, etc.…”

Section: Databases Research Challengesmentioning

confidence: 99%