DaQL 2.0: Measure Data Quality based on Entity Models

Lettner, Christian; Stumptner, Reinhard; Fragner, Werner; Rauchenzauner, Franz; Ehrlinger, Lisa

doi:10.1016/j.procs.2021.01.327

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…Increasing the data normalization level will result in more tables with fewer columns [38]. A potential area for future exploration is enhancing operations across entity models, explicitly focusing on matching operations designed to identify duplicate or asynchronously stored data [39]. The data structure, that is, the data stored in the database, follows a relational structure, so the relational type of the DBMS must be considered, b).…”

Section: ) Stage 2 Conceptual Database Designmentioning

confidence: 99%

Implementation of Relational Database in the STEAM-Problem Based Learning Model in Algorithm and Programming

Suryani,

Ambiyar,

Huda

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Currently, digital technology is developing in all fields. The development of this technology certainly has a significant impact on the world of education. A suitable learning model is needed, especially in the Algorithm and Programming course, to face global challenges in the Industrial Revolution 4.0. Students are expected to have skills that include critical and creative thinking in solving problems, communication, and collaboration with the support of technology. The STEAM-Problem Based Learning model can be used in the Algorithm and Programming learning process with seven syntaxes: preparation and knowledge identification; problem identification; plan solution; create and test products; communicate; evaluation and feedback and giving rewards. All activities carried out in the learning process by lecturers and students can be stored in a database. This research attempts to determine the validity of the STEAM-Problem Based Learning database design, which will be implemented in the Algorithm and Programming course. The data analysis technique used is validity analysis, which is based on assessing the data obtained through a questionnaire or a questionnaire using a Likert scale. Data was processed using Aiken’s V validity coefficient formula to test the expert’s judgment. Assessment indicators include correctness, consistency, relevance, completeness, and minimality. The results of the study show that the validity test on the STEAM-Problem Based Learning database design is valid, so it is feasible to implement it in algorithm and programming learning.

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Section: ) Stage 2 Conceptual Database Designmentioning

confidence: 99%

Implementation of Relational Database in the STEAM-Problem Based Learning Model in Algorithm and Programming

Suryani,

Ambiyar,

Huda

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Data Integration, Management, and Quality: From Basic Research to Industrial Application

Ehrlinger

Lettner

Fragner³

et al. 2022

Communications in Computer and Information Science

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Towards a Risk-Based Continuous Auditing-Based Certification for Machine Learning

Knoblauch,

Großmann

2023

Rev Socionetwork Strat

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Machine learning systems have gained widespread adoption across various industries. This includes highly regulated ones that need to match certain quality requirements based on a given risk exposure. The MLOps paradigm, following a similar approach to DevOps, promises major improvements in quality and speed, with a focus on deploying ML models at a fast pace with high quality on an automated basis. However, traditional point-in-time certifications with manual audits are inadequate for MLOps setups due to frequent changes to the ML system. To overcome this challenge, we propose Continuous Audit-Based Certification (CABC), which uses automated audits to issue or revoke certificates based on an automated assessment of artifacts from the MLOps lifecycle. Our approach utilizes artifacts from the MLOps lifecycle for quality measurements based on standards such as ISO 25012. We propose a risk-based measurement selection, an audit API for standardized retrieval of data for measurement, a tamper-proof data collection process, and an architecture for separation of duties in the certification process. CABC aims to improve efficiency, enhance trust in the ML system, and support highly regulated industries in achieving their quality goals.

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DaQL 2.0: Measure Data Quality based on Entity Models

Cited by 5 publications

References 10 publications

Implementation of Relational Database in the STEAM-Problem Based Learning Model in Algorithm and Programming

Implementation of Relational Database in the STEAM-Problem Based Learning Model in Algorithm and Programming

Data Integration, Management, and Quality: From Basic Research to Industrial Application

Towards a Risk-Based Continuous Auditing-Based Certification for Machine Learning

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