Handling Evolving Data Warehouse Requirements

Solodovnikova, Darja; Niedrite, Laila; Kozmina, Natalija

doi:10.1007/978-3-319-23201-0_35

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…The adjustment refers to two types of changes that are possible in the DW data model, schema changes and content changes [10].…”

Section: Working Examplementioning

confidence: 99%

“…Golfarelli and Rizzi [9] argue that a DW has "rapidly spread in the industrial world due to undeniable contribution to increasing the effectiveness and efficiency of the decisions." Since data in a DW is used for decision support, therefore the data model for a DW should be designed in a way that it is optimized for this purpose [10] . A DW is dependent on its data sources for data population, which is done through a process called Extraction, Transformation, and Loading (ETL). However, the challenging task is the presence of TDBs as data sources.…”

Section: Introductionmentioning

confidence: 99%

“…Recall from a preceding paragraph that an organization changes itself and to accommodate the organizational changes the corresponding database system is also changed. This requires adjustment to the multidimensional model [10], that is, to accommodate the changes introduced by TDB [11].…”

Section: Introductionmentioning

confidence: 99%

“…This may lead to two types of changes to a DW, content changes and schema changes [4,10]. The content changes include insert, update, and delete operations on records while schema changes include adding, modifying, and dropping an attribute or a table in the DW data model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Temporal and Evolving Data Warehouse Design

Faisal¹,

Sarwar²,

Shahzad³

et al. 2017

Scientific Programming

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The data model of the classical data warehouse (formally, dimensional model) does not offer comprehensive support for temporal data management. The underlying reason is that it requires consideration of several temporal aspects, which involve various time stamps. Also, transactional systems, which serves as a data source for data warehouse, have the tendency to change themselves due to changing business requirements. The classical dimensional model is deficient in handling changes to transaction sources. This has led to the development of various schemes, including evolution of data and evolution of data model and versioning of dimensional model. These models have their own strengths and limitations, but none fully satisfies the above-stated broad range of aspects, making it difficult to compare the proposed schemes with one another. This paper analyses the schemes that satisfy such challenging aspects faced by a data warehouse and proposes taxonomy for characterizing the existing models to temporal data management in data warehouse. The paper also discusses some open challenges.

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“…The adjustment refers to two types of changes that are possible in the DW data model, schema changes and content changes [10].…”

Section: Working Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temporal and Evolving Data Warehouse Design

Faisal¹,

Sarwar²,

Shahzad³

et al. 2017

Scientific Programming

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“…In the same context, authors in (Solodovnikova et al, 2015) have also investigated the problem of business requirements evolution. They defined a formalism for modeling the new needs of decisionmakers and proposed a semi-automatic approach to adjust and create a new version of the DW model.…”

Section: Approaches Based On Business Requirement Evolutionmentioning

confidence: 99%

The Power of a Model-Driven Approach to Handle Evolving Data Warehouse Requirements

Taktak

Alshomrani

Feki

et al. 2017

Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development

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Abstract:The Data Warehouse (DW) is characterized by complex architecture, specific modeling and design approaches. It integrates data issued from operational data sources in order to meet decision-makers' needs by providing answers for OLAP queries (On-Line Analytical Processing). In practice, both data source models and decision-makers' analytical requirements evolve over time and, therefore, lead to changes in the DW multidimensional model. In this evolving context, we have developed the DWE (Data Warehouse Evolution) framework. DWE automatically propagates the changes of the data source data-model on the DW data-model. This paper proposes a model-driven approach for extending DWE in order to consider a further related evolutionary aspect: The evolution of decision-makers' needs. It deals with the propagation of these evolutions on the DW multidimensional model. This approach relies on a classification of evolution scenarios and a set of transformation rules for the identification of evolution operations to apply on the DW.

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