CMCD: A data warehouse modeling framework based on goals and business process models

Chakiri, Houda; Mohajir, Mohammed El; Assem, Nasser

doi:10.1109/afrcon.2017.8095605

Cited by 3 publications

(7 citation statements)

References 7 publications

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“…In Chakiri et al (2017), we based our DM design on the contextual modeling (goal, rational and business diagrams) of user requirements to automatically extract the DW facts, dimensions, measures and time granularity. Contextual modeling is a form of modeling that draws the structure of an organization and relationships between its different stakeholders.…”

Section: Data Warehouse Design Frameworkmentioning

confidence: 99%

“…We aim at presenting a comprehensive framework for the design of the multidimensional (MD) schema of a DW that enables decision-makers to measure and assess governance at local municipality offices. This work is an extension of our previous work (Chakiri et al , 2017), where we offered a semi-automatic design of the DW based on the goal and business contextual models.…”

Section: Introductionmentioning

confidence: 99%

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A data warehouse hybrid design framework using domain ontologies for local good-governance assessment

Chakiri

Mohajir

Assem

2020

Self Cite

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Purpose Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government. The purpose of this paper is to propose the extraction of the DW schema based on a mixed approach that adopts both i* framework for requirements-based representation and domain ontologies for data source representation, to extract the multi-dimensional (MD) elements. The data was collected from various sources and information systems (ISs) deployed in different municipalities. Design/methodology/approach The authors present a framework for the design and implementation of a DW for local good-governance assessment. The extraction of facts and dimensions of the DW’s MD schema is done using a hybrid approach, where the extraction of requirement-based DW schema and source-based DW schema are done in parallel followed by the reconciliation of the obtained schemas to obtain the good-governance assessment DW final design. Findings The authors developed a novel framework to design and implement a DW for local good-governance assessment. The framework enables the extraction of the DW MD schema by using domain ontologies to help capture semantic artifacts and minimize misconceptions and misunderstandings between different stakeholders. The introduction and use of domain ontologies during the design process serves the generalization and automation purpose of the framework. Research limitations/implications The presently conducted research faced two main limitations as follows: the first is the full automation of the design process of the DW and the second, and most important, is access to local government data as it remains limited because of the lack of digitally stored data in municipalities, especially in developing countries in addition to the difficulty of accessing the data because of regulatory aspects and bureaucracy. Practical implications The local government environment is among the public administrations most subject to change-adverse cultures and where the authors can face high levels of resistance and significant difficulties during the implementation of decision support systems, despite the commitment/engagement of decision-makers. Access to data sources stored by different ISs might be challenging. While approaching the municipalities for data access, it was done in the framework of a research project within one of the most notorious universities in the country, which gave more credibility and trust to the research team. There is also a need for further testing of the framework to reveal its scalability and performance characteristics. Originality/value Compared to other local government assessment ad hoc tools that are partially or entirely based on subjectively collected data, the framework provides a basis for automated design of a comprehensive local government DW using e-government domain ontologies for data source representation coupled with the goal, rationale and business process diagrams for user requirements representations, thus enabling the extraction of the final DW MD schema.

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Section: Data Warehouse Design Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A data warehouse hybrid design framework using domain ontologies for local good-governance assessment

Chakiri

Mohajir

Assem

2020

Self Cite

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“…DW plays a crucial role in decision making by providing reliable and efficient tools to decision makers. The data warehouse integrates a wide range of heterogeneous data sources in multidimensional structures that support decision making [36]. Data warehousing applies ETL technology [36]; [34].…”

Section: Overviewmentioning

confidence: 99%

“…The data warehouse integrates a wide range of heterogeneous data sources in multidimensional structures that support decision making [36]. Data warehousing applies ETL technology [36]; [34]. With respect to the integration process of different sources, this paper by [36] suggested the use of the contextual model to conceptual design (CMCD).…”

Section: Overviewmentioning

confidence: 99%

“…Data warehousing applies ETL technology [36]; [34]. With respect to the integration process of different sources, this paper by [36] suggested the use of the contextual model to conceptual design (CMCD). The paper explained that the CMCD will produce a unified approach in extracting automatically the DW multidimensional model in order to meet business framework.…”

Section: Overviewmentioning

confidence: 99%

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A Theoritical Exploration of Data Management and Integration in Organisation Sectors

Offia¹,

Crowe²

2019

IJDMS

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Big data development is a disturbing issue that will affect enterprise across various sectors. The increase of data volume, high speed of data generation and increasing rate of different data from heterogeneous sources have led to difficulties in data management. This paper first reviews different aspects of big data management, including data integration and traditional data warehouse, and their associated challenges. The problems include increase of redundant data, data accessibility, time consumption in data modelling and data movement from heterogeneous sources into a central database, especially in the big data environment. We then propose a logical data management approach using RESTview technology to integrate and analyse data, without fully adopting traditional ETL processes. Data that for governance, corporate, security or other restriction reasons cannot be copied or moved, can easily be accessed, integrated and analysed, without creating a central repository. Data can be kept in its original form and location, eliminating the movement of data, significantly speeding up the process and allowing for live data interrogation. It may not be the practical solution for every situation but, it is a feasible solution that is comparably cost effective.

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Engineering the Black-Box Meta Model of Data Exploration

Winter

Yang

2020

Advances in Enterprise Engineering XIII

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CMCD: A data warehouse modeling framework based on goals and business process models

Cited by 3 publications

References 7 publications

A data warehouse hybrid design framework using domain ontologies for local good-governance assessment

A data warehouse hybrid design framework using domain ontologies for local good-governance assessment

A Theoritical Exploration of Data Management and Integration in Organisation Sectors

Engineering the Black-Box Meta Model of Data Exploration

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