Comparative study of data warehouses modeling approaches: Inmon, Kimball and Data Vault

Yessad, Lamia; Labiod, Aissa

doi:10.1109/icsrs.2016.7815845

Cited by 21 publications

(15 citation statements)

References 1 publication

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“…Data in the Inmon approach is secured alive and well. As showed by Yessad et al, (2016), Inmon dispersions the data transport focus into four levels. The operational level which is the place the data is sourced from.…”

Section: Data Warehouse Approachesmentioning

confidence: 99%

“…Finishing managed is at the atomic level. In case the data stockroom shapes gigantic volumes if data it is dependably incited that the organizing system be done outside of the data warehouse [1].…”

Section: Data Warehouse Approachesmentioning

confidence: 99%

“…The data is institutionalized in the midst of the atomic level and in the midst of the ETL procedure and denormalised while being stacked into the data bazaars. Another level as appeared by Yessad et al, (2016) is that called the individual level. In this level the data examination and isolating happens.…”

Section: Data Warehouse Approachesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Data Warehouse Approaches for Higher Education Institution

Makele¹,

Doss²

2018

International Journal of Innovative Research in Applied Sciences and Engineering

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Today universities like some other enterprises depend on their information to settle on basic choices and make prescient examination. They likewise have a need to keep up information outside of their value-based databases to keep up authentic information of understudies. This paper plans to investigate a perfect approach for building an information distribution center for colleges and look at the methodologies to be specific Top-Down Approach, Bottom-Up Approach and Data Vault approach.

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Section: Data Warehouse Approachesmentioning

confidence: 99%

Section: Data Warehouse Approachesmentioning

confidence: 99%

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A Survey on Data Warehouse Approaches for Higher Education Institution

Makele¹,

Doss²

2018

International Journal of Innovative Research in Applied Sciences and Engineering

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“…The SQL Server version of DW followed the Star schema (see Fig. 2) introduced by Kimball in 1997 to the broad audience however invented in 1960s along with dimensions and facts [21].…”

Section: Microsoft Sql Servermentioning

confidence: 99%

Data Warehouse Design for Big Data in Academia

Shijitha¹,

Karthigaikumar²,

Paul³

2022

Computers, Materials &Amp; Continua

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This paper describes the process of design and construction of a data warehouse ("DW") for an online learning platform using three prominent technologies, Microsoft SQL Server, MongoDB and Apache Hive. The three systems are evaluated for corpus construction and descriptive analytics. The case also demonstrates the value of evidence-centered design principles for data warehouse design that is sustainable enough to adapt to the demands of handling big data in a variety of contexts. Additionally, the paper addresses maintainability-performance tradeoff, storage considerations and accessibility of big data corpora. In this NSF-sponsored work, the data were processed, transformed, and stored in the three versions of a data warehouse in search for a better performing and more suitable platform. The data warehouse engines-a relational database, a No-SQL database, and a big data technology for parallel computations-were subjected to principled analysis. Design, construction and evaluation of a data warehouse were scrutinized to find improved ways of storing, organizing and extracting information. The work also examines building corpora, performing ad-hoc extractions, and ensuring confidentiality. It was found that Apache Hive demonstrated the best processing time followed by SQL Server and MongoDB. In the aspect of analytical queries, the SQL Server was a top performer followed by MongoDB and Hive. This paper also discusses a novel process for render students anonymity complying with Family Educational Rights and Privacy Act regulations. Five phases for DW design are recommended: 1) Establishing goals at the outset based on Evidence-Centered Design principles; 2) Recognizing the unique demands of student data and use; 3) Adopting a model that integrates cost with technical considerations; 4) Designing a comparative database and 5) Planning for a DW design that is sustainable. Recommendations for future research include attempting DW design in contexts involving larger data sets, more refined operations, and ensuring attention is paid to sustainability of operations.

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“…Next, there is the data mart layer, which contains various data models used by business intelligence tools and other IT applications. We opted for the Data Vault approach in designing the data warehouse, which ensures flexibility and high performance (see, for example, Yessad and Labiod 2016).…”

Section: Functionalities and Their Developmentmentioning

confidence: 99%

Open access to data on higher education and science: A case study of the RAD-on platform in Poland

Protasiewicz¹,

Podwysocki²,

Ostrowska³

et al.

EPiC Series in Computing

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In the era of data-driven policies, there is a need to ensure that decision-makers and citizens alike have easy access to the highest quality data. In the creation of RAD-on: an information system comprising reports, analyses, and data on science and higher education in Poland, we have attempted to overcome barriers to decision-making in the field of research and innovation. The system presents open government data (OGD) on subjects that are relevant to science and innovation policy, including: the publication patterns of scientists and academics in Poland; graduates from Polish universities and their incomes; and data on research projects conducted in Poland and financed using national and international funds. RAD-on aims to ensure transparency and participation through interaction with its users and a methodologically coherent approach to data. In this article, we describe the complex architecture of the RAD-on system, and present its reporting capabilities. While doing so, we focus on the development process, which accounted for recent studies on the usability of open data. We address the problem of users’ understanding of the possibilities of the data platform, and outline our efforts to mitigate the risk of data being wrongly interpreted and used in decision-making processes.

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Comparative study of data warehouses modeling approaches: Inmon, Kimball and Data Vault

Cited by 21 publications

References 1 publication

A Survey on Data Warehouse Approaches for Higher Education Institution

A Survey on Data Warehouse Approaches for Higher Education Institution

Data Warehouse Design for Big Data in Academia

Open access to data on higher education and science: A case study of the RAD-on platform in Poland

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