Automating ETL processes using the domain-specific modeling approach

Petrović, Marko D.; Vuckovic, Milica; Turajlić, Nina; Babarogić, Slađan; Anicic, Nenad; Marjanovic, Zoran

doi:10.1007/s10257-016-0325-8

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…A data warehouse (DW) is an integrated collection of subject-oriented decision support data [29]. Data warehousing (DW) is a specific type of information system and should enable the acquisition of business data, its transformation into appropriate strategic business information, and subsequent storage of data in a format that facilitates business analysis [30]. The cloud is currently the hardware and software solution of the data center providing the services [31].…”

Section: Cloud Storagementioning

confidence: 99%

Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0

Vrchota

Pech

2019

Applied Sciences

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Industry 4.0 includes digital process transformation, information technology (IT) development, mobile devices, learning software, automation, and robotics, as well as intelligent sensors to collect large datasets, store, analyze, and use them in business, including simulation, virtual reality, and digital twins. The aim of the paper is to characterize the readiness of the enterprise to use Industry 4.0. In the research, a questionnaire survey was carried out on a sample of 276 enterprises mainly from the manufacturing industry. Using explorative factor analysis, the index of Industry 4.0 (VPi4) was designed to determine the level of Industry 4.0 implementation in the enterprises. The results were further verified by a statistical analysis, using Mann–Whitney test and correlation coefficients. The results indicate that the VPi4 index was consistent in terms of distribution when comparing the results on the verification sample. Its results correlate with the subjective perception of the enterprises, and different levels of the index reflect the difference in technological intensity of the industry. The VPi4 index enables the enterprises to determine their own level of current state of readiness for Industry 4.0, to better prioritize business development. The proposed solution categorizes Industry 4.0 components into a useful theoretical framework. Further research offers the possibility of applying the index in other sectors, its relation to the size of enterprises, and updating with respect to new trends in information technology.

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Section: Cloud Storagementioning

confidence: 99%

Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0

Vrchota

Pech

2019

Applied Sciences

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“…Similar to our approach, the authors then use clustering to determine the similarity between data sources while we use similarity matrices in order to determine the best possible subset of data to be extracted. The authors in [9] present an ETL approach which uses domain-specific modelling. Their own language (DSL) is used to model the different steps in the ETL process.…”

Section: Related Researchmentioning

confidence: 99%

Representative Sample Extraction from Web Data Streams

Scriney

Xing

McCarren

et al. 2019

Lecture Notes in Computer Science

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Smart or digital city infrastructures facilitate both decision support and strategic planning with applications such as government services, healthcare, transport and traffic management. Generally, each service generates multiple data streams using different data models and structures. Thus, any form of analysis requires some form of extracttransform-load process normally associated with data warehousing to ensure proper cleaning and integration of heterogeneous datasets. In addition, data produced by these systems may be generated at a rate which cannot be captured completely using standard computing resources. In this paper, we present an ETL system for transport data coupled with a smart data acquisition methodology to extract a subset of data suitable for analysis.

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“…In [20], the authors present an automatic ETL approach facilitated by domain specific modelling, where they use their own language (DSL) to represent different stages of the ETL process. A user (typically a domain expert), using DSL, outlines the concepts and operations for the conceptual ETL process.…”

Section: Related Researchmentioning

confidence: 99%

Automating Data Mart Construction from Semi-structured Data Sources

Scriney

McCarthy

McCarren

et al. 2018

The Computer Journal

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The global food and agricultural industry has a total market value of USD 8 trillion in 2016, and decision makers in the Agri sector require appropriate tools and up-to-date information to make predictions across a range of products and areas. Traditionally, these requirements are met with information processed into a data warehouse and data marts constructed for analyses. Increasingly however, data is coming from outside the enterprise and often in unprocessed forms. As these sources are outside the control of companies, they are prone to change and new sources may appear. In these cases, the process of accommodating these sources can be costly and very time consuming. To automate this process, what is required is a sufficiently robust Extract-Transform-Load (ETL) process; external sources are mapped to some form of ontology, and an integration process to merge the specific data sources. In this paper, we present an approach to automating the integration of data sources in an Agri environment, where new sources are examined before an attempt to merge them with existing data marts. Our validation uses three separate case studies of real world data to demonstrate the robustness of our approach and the efficiency of materialising data marts

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Automating ETL processes using the domain-specific modeling approach

Cited by 8 publications

References 19 publications

Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0

Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0

Representative Sample Extraction from Web Data Streams

Automating Data Mart Construction from Semi-structured Data Sources

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