Data Quality Problems in TPC-DI Based Data Integration Processes

Yang, Qishan; Ge, Mouzhi; Helfert, Markus

doi:10.1007/978-3-319-93375-7_4

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…DWHs need to rely on architectures and other components assisting them, e.g. solving data quality issues and integrating data (Yang et al, 2017). Data warehouse architectures (DWHAs) are developed synchronously along with DWHs' progress to mainly serve for DWHs.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Data Warehouse Architectures: Modeling and Classification

Yang

Helfert

2019

Proceedings of the 21st International Conference on Enterprise Information Systems

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With decades of development and innovation, data warehouses and their architectures have been extended to a variety of derivatives in various environments to achieve different organisations' requirements. Although there are some ad-hoc studies on data warehouse architecture (DWHA) investigations and classifications, limited research is relevant to systematically model and classify DWHAs. Especially in the big data era, data is generated explosively. More emerging architectures and technologies are leveraged to manipulate and manage big data in this domain. It is therefore valuable to revisit and investigate DWHAs with new innovations. In this paper, we collect 116 publications and model 73 disparate DWHAs using Archimate, then 9 representative DWHAs are identified and summarised into a "big picture". Furthermore, it proposes a new classification model sticking to state-of-the-art DWHAs. This model can guide researchers and practitioners to identify, analyse and compare differences and trends of DWHAs from componental and architectural perspectives.

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Section: Introductionmentioning

confidence: 99%

Analysis of Data Warehouse Architectures: Modeling and Classification

Yang

Helfert

2019

Proceedings of the 21st International Conference on Enterprise Information Systems

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“…DWHs module depends on the type of the architectures with other components to achieve the best accurate result through the data quality issues and data integration [7]. To primarily serve DWHs, data warehouse architectures (DWHAs) are built synchronously with DWH development.…”

mentioning

confidence: 99%

Analyses the Performance of Data Warehouse Architecture Types

Merceedi,

Yazdeen,

Ibrahim

et al. 2022

JSCDM

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The concept of storing historical data for retrieving them when needed has been conceived, and the idea was primitive to build repositories for historical data to store these data, despite the use of a specific technique for recovering these data from various storage modes. The data warehouse is the most reliable and widely used technology for scheduling, forecasting, and managing corporations. Also, it is concerned with the data storage facility that extensive collection of data. Data warehouses are called ancient modern techniques; since the early days of relational databases, the critical component of decision support, increasing focus for the database industry. Many commercial products and services are now available, and almost all of the primary database management system vendors provide them. When opposed to conventional online transaction processing applications, decision support puts slightly different demands on database technology. This paper analyzes the performance of the data warehouse architectures by studying and comparing many research works in this field. The study involves extracting, transforming, and loading the data from different recourses and the important characteristic of the architecture types. Furthermore, the tools and application service techniques used to build data warehouse architecture.

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