Conceptual Multidimensional Modeling for Data Warehouses: A Survey

Gosain, Anjana; Singh, Jaspreeti

doi:10.1007/978-3-319-11933-5_33

Cited by 11 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These studies provided traditional approaches that are designed for particular models and cannot easily be applied to real-world cases. Recent surveys (Ania and Samuel, 2014;Gosain and Singh, 2015;Oscar and Alberto, 2009) have been conducted to compare available DW design methods. However, they did not identify relevant methods for designing DWs for HE.…”

Section: Data Warehouse Design Methodsmentioning

confidence: 99%

Design of a data warehouse model for decision support at higher education

Aljawarneh

2016

Information Development

View full text Add to dashboard Cite

The expansion of higher education (HE) institutions and their increased emphasis on strategic planning have raised the demand for integrated information systems that can support strategic analysis. A data warehouse (DW) provides decision data for managers in a form that facilitates their access, using business intelligence (BI) tools to enhance their decision making process. However, available DW models and design methods do not address the HE industry’s specific needs. In this paper, we aim to fill this gap by providing a methodological framework that encompasses methods tailored to meet the needs of HE. Also, a prototype has been developed using actual student data to validate the framework. It shows the efficacy of the framework in supporting strategic analysis at universities, leading them to develop effective strategies and policies. The paper further provides guidelines for researchers on the important issues to be considered for designing efficient DWs.

show abstract

Section: Data Warehouse Design Methodsmentioning

confidence: 99%

Design of a data warehouse model for decision support at higher education

Aljawarneh

2016

Information Development

View full text Add to dashboard Cite

show abstract

“…By convention, most DW schema are based on dimensional design model (DDM), although no consensus on its formalism has been established yet [14]. Also, DDM design relies partly on non-consensual "best-known practices", some of them hardly automatable.…”

Section: Relational Vs Dimensionalmentioning

confidence: 99%

“…Possible incompatibilities or negative interactions between various solutions can be present. Previous surveys [9,12,14,19,40,46,56] do not clearly identify the best methods that suits the HD and some of the comparison criteria used are not well-documented. Furthermore, none of the surveys compare complete methods in the context of a real-world implementation.…”

Section: Introductionmentioning

confidence: 98%

Data Warehouse Design Methods Review: Trends, Challenges and Future Directions for the Healthcare Domain

Khnaisser

Lavoie

Diab

et al. 2015

Communications in Computer and Information Science

View full text Add to dashboard Cite

Abstract. In secondary data use context, traditional data warehouse design methods don't address many of today's challenges; particularly in the healthcare domain were semantics plays an essential role to achieve an effective and implementable heterogeneous data integration while satisfying core requirements. Forty papers were selected based on seven core requirements: data integrity, sound temporal schema design, query expressiveness, heterogeneous data integration, knowledge/source evolution integration, traceability and guided automation. Proposed methods were compared based on twenty-two comparison criteria. Analysis of the results shows important trends and challenges, among them (1) a growing number of methods unify knowledge with source structure to obtain a well-defined data warehouse schema built on semantic integration; (2) none of the published methods cover all the core requirements as a whole and (3) their potential in real world is not demonstrated yet.

show abstract

“…Researchers have proposed conceptual models following unified modelling language (UML), entity relationship (ER) as well as ad hoc notations, for instance [3, 5]. A survey of these modelling approaches is available in [21]. In spite of numerous modelling approaches existing in the literature, there is a lack of quantitative measures to assess the quality of multidimensional models.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive complexity metric for data warehouse multidimensional model understandability

Gosain

Singh

2020

IET softw.

Self Cite

View full text Add to dashboard Cite

Data warehouse quality can be determined during the initial phases of data warehouse development by quantifying the structural complexity of multidimensional models using metrics. The structural complexity of a multidimensional model is guided by its elements, types, and relationships among those elements. So far, most of the researchers have dealt with metrics based on various elements (facts, dimensions, dimensional hierarchies, and hierarchy levels) existing in these models. However, not much consideration is given to different types of dimensions based on hierarchy types and different relationships among those elements. Therefore, this work proposes a comprehensive complexity metric for measuring multidimensional model complexity by taking into account various elements, their types and the relationships among the elements at various levels of granularity in these models. The theoretical validation of the proposed metric using the property-based framework given by Briand et al. characterises it as a complexity measure. Furthermore, the empirical study, employing statistical techniques (correlation and multinomial regression), on 26 multidimensional models and 20 subjects proved that the authors' proposed metric is strongly correlated with multidimensional model understandability. Hence, this metric can be considered as a good predictor for data warehouse multidimensional model understandability.

show abstract

Conceptual Multidimensional Modeling for Data Warehouses: A Survey

Cited by 11 publications

References 39 publications

Design of a data warehouse model for decision support at higher education

Design of a data warehouse model for decision support at higher education

Data Warehouse Design Methods Review: Trends, Challenges and Future Directions for the Healthcare Domain

Comprehensive complexity metric for data warehouse multidimensional model understandability

Contact Info

Product

Resources

About