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In this short note, we shall briefly summarize this own follow-up research as well as the impact on research and practice, in the three areas of data quality, data warehouse process engineering, and automated model management. We end with some ongoing research questions and open challenges.
24 sie 2023 · Theoretical: limits the scope, depth, or applicability of a study. Methodological: limits the quality, quantity, or diversity of the data. Empirical: limits the representativeness, validity, or reliability of the data. Analytical: limits the accuracy, completeness, or significance of the findings.
12 sie 2022 · This study allows for the identification of the main challenges and issues related to the design of Big Data warehousing systems, mainly involving the lack of a generic design model for data collection, storage, processing, querying, and analysis.
2 gru 2019 · In our study, we consider the generation and propagation of data quality problems from data sources to data warehouse and develop a model to find cost-effective data sources to optimize the data warehouse’s data quality.
1 sty 2023 · This paper presents a holistic perspective focusing on possible data warehouse architectures, data types, data schemes, storage, security and privacy in a healthcare environment. We discuss existing DW architectures dedicated to clinical data and suggest the best DW approaches for clinical data warehousing. Previous.
This paper analyzes the performance of the data warehouse architectures, through studding and comparing many research works in this filed. the study involves the extract, transform and load...
7 lis 2022 · This article presents a detailed overview of the roles of data warehouses and data lakes in modern enterprise data management. We detail the definitions, characteristics and related works for the respective data management frameworks. Furthermore, we explain the architecture and design considerations of the current state of the art.
