Two-ETL Phases for Data Warehouse Creation: Design and Implementation

Nabli, Ahlem; Bouaziz, Senda; Yangui, Rania; Gargouri, Faïez

doi:10.1007/978-3-319-23135-8_10

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…In [20], Nabli et al proposed a method, called two-ETL phases, for DW creation, involving a BPMN-based design and an implementation. A three-layer methodology is proposed: (i) business requirements layer; (ii) ETL process layer, and (iii) physical design layer.…”

Section: Summary Of Etl Process Modeling Approaches Based On Bpmnmentioning

confidence: 99%

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Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons

Dhaouadi

Bousselmi

Gammoudi

et al. 2022

Data

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The extract, transform, and load (ETL) process is at the core of data warehousing architectures. As such, the success of data warehouse (DW) projects is essentially based on the proper modeling of the ETL process. As there is no standard model for the representation and design of this process, several researchers have made efforts to propose modeling methods based on different formalisms, such as unified modeling language (UML), ontology, model-driven architecture (MDA), model-driven development (MDD), and graphical flow, which includes business process model notation (BPMN), colored Petri nets (CPN), Yet Another Workflow Language (YAWL), CommonCube, entity modeling diagram (EMD), and so on. With the emergence of Big Data, despite the multitude of relevant approaches proposed for modeling the ETL process in classical environments, part of the community has been motivated to provide new data warehousing methods that support Big Data specifications. In this paper, we present a summary of relevant works related to the modeling of data warehousing approaches, from classical ETL processes to ELT design approaches. A systematic literature review is conducted and a detailed set of comparison criteria are defined in order to allow the reader to better understand the evolution of these processes. Our study paints a complete picture of ETL modeling approaches, from their advent to the era of Big Data, while comparing their main characteristics. 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.

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Section: Summary Of Etl Process Modeling Approaches Based On Bpmnmentioning

confidence: 99%

“…We noticed that in the literature, other literature reviews have been presented, such as those of [8,[19][20][21]; however, most of these studies deal with a limited number of approaches, including only some formalisms. In addition, their comparison was limited to some criteria and did not include Big Data characteristics.…”

Section: Introductionmentioning

confidence: 99%

Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons

Dhaouadi

Bousselmi

Gammoudi

et al. 2022

Data

View full text Add to dashboard Cite

show abstract

“…These tools cannot be adjusted in real time and have limited flexibility in modifying and analyzing the data stored in the source and target databases. Many solutions cannot provide a statistical perspective on connected databases on both sides [7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, when data are loaded for the first time, the complete load approach is employed, involving the complete cleaning of tables. In addition, when adding modified or new records to a target source, an incremental load is employed [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Relational Data Extraction and Transformation: A Study to Enhance Information Systems Performance

Hasan

Bakar

2022

JICCE

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The most effective method to improve information system capabilities is to enable instant access to several relational database sources and transform data with a logical structure into multiple target relational databases. There are numerous data transformation tools available; however, they typically contain fixed procedures that cannot be changed by the user, making it impossible to fulfill the near-real-time data transformation requirements. Furthermore, some tools cannot build object references or alter attribute constraints. There are various situations in which tool changes in data type cause conflicts and difficulties with data quality while transforming between the two systems. The R-programming language was extensively used throughout this study, and several different relational database structures were utilized to complete the proposed study. Experiments showed that the developed study can improve the performance of information systems by interacting with and exchanging data with various relational databases. The study addresses data quality issues, particularly the completeness and integrity dimensions of the data transformation processes.

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Introduction

Taniar

Rahayu

2021

Data-Centric Systems and Applications

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Two-ETL Phases for Data Warehouse Creation: Design and Implementation

Cited by 8 publications

References 10 publications

Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons

Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons

Relational Data Extraction and Transformation: A Study to Enhance Information Systems Performance

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