DAREF: MDA framework for modelling data warehouse requirements and deducing the multidimensional schema

Beggar, Omar El; Letrache, Khadija; Ramdani, Mohammed

doi:10.1007/s00766-020-00339-9

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…With the continuous popularization and application of various new grain storage technologies, the quality management situation has been improved to a certain extent, and a series of rules and regulations, technical speci cations, and operating procedures for grain management have been formulated one after another. However, the quality management of most enterprises is still relatively extensive, the operation level is low, the management is not standardized, the potential safety hazards still exist, there are still large defects and loopholes, and they do not have su cient quality assurance ability [3]. Every year, we emphasize the safety of grain storage, but there is a sense of insecurity.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Storage Data Classification System Based on the BP Neural Network

Li¹

2022

Journal of Control Science and Engineering

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In order to solve the problem of multifeature recognition and classification of many kinds of pests, this study puts forward a method of pest feature classification using the BP neural network. Through the preprocessing of stored grain pest images, five characteristic parameters are obtained and optimized and input into the BP network for training. The experimental results show that sample 3 of flat grain thief and sample 4 of bark beetle are not well recognized. Because these two kinds of pests have small bodies and thin legs, some detailed features are eliminated after image processing, resulting in a low recognition rate. But the overall recognition rate can reach 95%. Conclusion. The experiment has obtained good recognition results. This method is accurate and effective for the classification and recognition of stored grain pests and provides a scientific basis for the scientific decision-making of controlling stored grain pests.

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Section: Introductionmentioning

confidence: 99%

Intelligent Storage Data Classification System Based on the BP Neural Network

Li¹

2022

Journal of Control Science and Engineering

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“…Utilising meta-models for requirements engineering is a widely researched area, particularly to enable the integration of requirements with software platform architecture to ensure traceability and change management. For example, El Begger et al [13] recently proposed a model-driven architecture framework to design data warehouse requirements and generate a multidimensional data schema. However, their UML-based model cannot be integrated with other meta-data associated with analytics such as business domain, process and data lineage.…”

Section: Model-driven Requirements Engineering and Platform Designmentioning

confidence: 99%

A knowledge-driven approach for designing data analytics platforms

2022

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Big data analytics technologies are rapidly expanding across all industry sectors as organisations try to make analytics an integral part of their everyday decision-making. Although there are many software tools and libraries to assist analysts and software engineers in developing solutions, organisations are looking for flexible analytics platforms that can address their specific objectives and requirements. To minimise costs, such platforms also need to co-exist with existing IT infrastructures and reuse knowledge and resources already accumulated within the organisation. To address such needs, this paper proposes the Data Analytics Solution Engineering (DASE) framework—a knowledge-driven approach supported by semantic web technologies for requirements engineering, design and development of new data analytics platforms. It includes a meta-model that captures data analytics platform requirements via a Knowledge Base, a set of guidelines that organisations can follow in engineering data analytics platforms and a reference architecture that demonstrates how to use these guidelines. We evaluate the DASE framework through two case studies and demonstrate how it can facilitate knowledge-based and requirements-driven data analytics platform engineering. The resulting data analytics platforms are observed to be user friendly, easy to maintain and flexible in handling changes to requirements. This work contributes to the body of knowledge in knowledge-driven requirements engineering, and data analytics platform engineering by providing a meta-model and a reference architecture that can be tailored to different analytics application domains.

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“…El Beggar et al 21 developed an MDA (Multi-dimensional Architecture) framework for DW modeling and realizing the multi-dimensional schema.…”

Section: Literature Reviewmentioning

confidence: 99%

“…El Beggar et al 21 developed an MDA (Multi‐dimensional Architecture) framework for DW modeling and realizing the multi‐dimensional schema. The presented architecture was also called DAREF (Data Warehouse Requirements Engineering Framework).…”

Section: Literature Reviewmentioning

confidence: 99%

Automated heuristic based context dependent ETL process to generate multi‐dimensional model for tabular data

Hira¹,

Deshpande

2022

Concurrency and Computation

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Over the past decade, enterprises have broadly adopted data warehousing in various activities. Today, abundant information is available on websites in the form of tables or spreadsheets. This huge amount of data cannot be processed directly because of its complexity, heterogeneity, and gap between user requirements. In this work, an automatic approach is proposed to build the multi-dimensional structure (MDS) of heterogeneous tabular data format for intelligent decision-making. The proposed MDS is generated by identifying components such as dimensions and hierarchies. It automatically extracts measures based on the spatial characteristics of data dimensions like region, time as well as their hierarchies. This proposed approach automatically generates a multi-dimensional model for BI tools without complicated ETL (Extraction, Transformation and Loading) process and helps to solve several business queries, like "Top 5 states in India based on Irrigated area in 2009". Moreover, the proposed method reduces the time and cost of building multi-dimensional models to a very large extent. The correctness of proposed method is tested with the synthetic and economic datasets of Government websites where information is stored in tabular formats and various heterogeneous setups where the proposed method saved approximately 4000 to 5000 computing hours of the ETL process.

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DAREF: MDA framework for modelling data warehouse requirements and deducing the multidimensional schema

Cited by 8 publications

References 28 publications

Intelligent Storage Data Classification System Based on the BP Neural Network

Intelligent Storage Data Classification System Based on the BP Neural Network

A knowledge-driven approach for designing data analytics platforms

Automated heuristic based context dependent ETL process to generate multi‐dimensional model for tabular data

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