Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach

Liu, Qi; Feng, Gengzhong; Tayi, Giri Kumar; Tian, Jun

doi:10.1007/s10796-019-09963-5

Cited by 15 publications

(6 citation statements)

References 50 publications

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“…Based on our research, we've discovered several key aspects of successful DWH initiatives and proposed a five-step process for creating your DWH for use in your endeavors. [22].…”

Section: Discussionmentioning

confidence: 99%

Application of Data Warehouse Technology Based on Neural Network in Physical Education Quality Management

Zhao

Liu

2022

Mathematical Problems in Engineering

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Data warehouse technology has been created because of China’s technological advancement and the increasing requirements of the educational sector. Physical assessments are treated as tests by many students. Institutions spend plenty of time every year through physical tests, yet the results are rarely shared with students. Teachers are impeded by the size and complexity of physical test data, finding it challenging to support experiments or judge individual students’ development. Students have trouble following up and delivering test-based feedback after instruction. In recent years, various researchers have offered insightful advice on how to build multidimensional database structures for such trouble. However, quality requirements alone are not adequate to guarantee quality in reality. So, this paper presents a novel Hypertuned wide polynet convolutional neural network (HWPCNN) framework in the data warehouse technology to attain the greatest performance in physical education quality management. In this paper, we first apply HWPCNNs for physical education quality management to analyze the accuracy and recall of the model. It is no secret that HWPCNN is now one of the most widely used deep learning techniques. When it comes to managing the quality of physical education, the HWPCNN’s local perception feature in the data warehouse technology allows it to achieve the best possible results. To validate the model’s performance, it is compared to other models and then improved further to increase its accuracy. The physical education resources are gathered as a raw dataset for this inquiry. The raw dataset is cleaned using the Z-score approach to get it ready for further data processing. Then, a sparse matrix approach is employed to build a data cube, while the proposed method is used to index multidimensional databases. To demonstrate that our work is of the best quality in managing physical education, performance metrics of the suggested method are also evaluated and compared with other traditional methods.

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“…Based on our research, we've discovered several key aspects of successful DWH initiatives and proposed a five-step process for creating your DWH for use in your endeavors. [22].…”

Section: Discussionmentioning

confidence: 99%

Application of Data Warehouse Technology Based on Neural Network in Physical Education Quality Management

Zhao

Liu

2022

Mathematical Problems in Engineering

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“…High system response time of analytical queries is also one of the most challenging issues of data warehouse effectiveness (Azgomi and Sohrabi, 2019). The lack of system and data quality from data warehouses poses enormous risks related to decision-making and business processes like monetary loss and operational inefficiencies (Liu et al , 2021; Aftab and Siddiqui, 2018).…”

Section: Introductionmentioning

confidence: 99%

An empirical study on data warehouse systems effectiveness: the case of Jordanian banks in the business intelligence era

Al-Okaily

Teoh

et al. 2022

EMJB

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PurposeDespite the increasing role of the data warehouse as a supportive decision-making tool in today's business world, academic research for measuring its effectiveness has been lacking. This paucity of academic interest stimulated us to evaluate data warehousing effectiveness in the organizational context of Jordanian banks.Design/methodology/approachThis paper develops a theoretical model specific to the data warehouse system domain that builds on the DeLone and McLean model. The model is empirically tested by means of structural equation modelling applying the partial least squares approach and using data collected in a survey questionnaire from 127 respondents at Jordanian banks.FindingsEmpirical data analysis supported that data quality, system quality, user satisfaction, individual benefits and organizational benefits have made strong contributions to data warehousing effectiveness in our organizational data context.Practical implicationsThe results provide a better understanding of the data warehouse effectiveness and its importance in enabling the Jordanian banks to be competitive.Originality/valueThis study is indeed one of the first empirical attempts to measure data warehouse system effectiveness and the first of its kind in an emerging country such as Jordan.

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“…The connection to data quality comes in several aspects as: data cleansing in transformation, standardization for consistency, data enrichment for completeness, data profiling and validation, quality checks in loading, integration with data quality tools, etc. [4,5,6].…”

Section: Loadmentioning

confidence: 99%

Custom data quality mechanism in Data Warehouse facilitated by data integrity checks

Georgiev,

Valkanov

2024

MEM

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In the era of data-driven decision-making, Data Warehousing (DWH) is crucial for organizations seeking to leverage extensive datasets. However, the success of DWH initiatives depends on the quality of the enclosed data. Insuﬃcient quality data in Data Warehousing can impact the accuracy of analytical results, leading to misguided decisions and reduced business performance. This paper examines the signiﬁcance of Data Quality Mechanisms in addressing challenges related to data quality. Data Quality Mechanisms play a crucial role in identifying, rectifying, and preventing data quality issues throughout the data lifecycle. This paper explores fundamental concepts, challenges, and impacts of data quality on business operations. It emphasizes the critical role of robust Data Quality Mechanisms in ensuring the accuracy, completeness, and reliability of data within the Data Warehousing ecosystem. As organizations increasingly recognize data as a strategic asset, it is imperative to implement eﬀective data quality mechanisms to unlock the true potential of data warehouses and derive actionable insights.

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Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach

Cited by 15 publications

References 50 publications

Application of Data Warehouse Technology Based on Neural Network in Physical Education Quality Management

Application of Data Warehouse Technology Based on Neural Network in Physical Education Quality Management

An empirical study on data warehouse systems effectiveness: the case of Jordanian banks in the business intelligence era

Custom data quality mechanism in Data Warehouse facilitated by data integrity checks

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