Metadata Management in a Multiversion Data Warehouse

Wrembel, Robert; Bębel, Bartosz

doi:10.1007/11575801_26

Cited by 23 publications

(19 citation statements)

References 32 publications

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“…In implicit versioning, two versions of schema are maintained and transformation functions are defined to record adjustments in the DW [54]. In explicit versioning, two versions of schema as well as data are maintained [48]. Each version represents a schema and data version.…”

Section: Versioning Schemesmentioning

confidence: 99%

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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Section: Versioning Schemesmentioning

confidence: 99%

Temporal and Evolving Data Warehouse Design

Faisal¹,

Sarwar²,

Shahzad³

et al. 2017

Scientific Programming

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“…The metadata indicates for each attribute of a DW the original source of the data, their meaning and the transformations to which they have been subjected. The metadata should also include business definitions, data quality alerts, organizational changes, business rules and assumptions [14].…”

Section: Data Storagementioning

confidence: 99%

Building an Effective Data Warehousing for Financial Sector

Ferreira¹,

Almeida²,

Monteiro³

2017

ACIS

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This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a business intelligence system that, in a simple, quick but also versatile way, allows the access to updated, aggregated, real and/or projected information, regarding bank account balances. The established system extracts and processes the operational database information which supports cash management information by using Integration Services and Analysis Services tools from Microsoft SQL Server. The end-user interface is a pivot table, properly arranged to explore the information available by the produced cube. The results have shown that the adoption of online analytical processing cubes offers better performance and provides a more automated and robust process to analyze current and provisional aggregated financial data balances compared to the current process based on static reports built from transactional databases.

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“…Solutions proposed by (Solodovnikova, 2008) and (Wrembel and Bebel, 2007) allow the automatic detection of the DS changes and assist the administrator in the propagation of these changes towards the DW. These studies are mainly based on the administrator's expertise and do not propose automatic propagation rules for the DW alterations.…”

Section: Related Researchesmentioning

confidence: 99%

“…Other literature works (Bellahsene, 2002), (Wrembel and Bebel, 2007), (Solodovnikova, 2008) have examined the evolution of the source schema as well as its impact on the DW. This suits our concern in this paper.…”

Section: Related Researchesmentioning

confidence: 99%

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Toward Evolution Models for Data Warehouses

Taktak

Feki

Zurfluh

2014

Proceedings of the 2nd International Conference on Model-Driven Engineering and Software Development

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Abstract:A Data warehouse (DW) is characterized by a complex architecture, designed in order to integrate data derived from operational data sources (DS), hence providing advanced analytical tools of these data. The DW is highly dependent on its DS. Hence, evolutions of the DS schema need to be propagated to the DW schema and content. This paper presents a model-driven approach for the evolution of a multidimensional DW. It is based on two evolution models: a first evolution model for the DS and another for the DW. These two models concern the data structure aspects as well as the evolution operations. The transition between these two models is performed through specific transformation rules defined in QVT (Query\View\Transformation). INTRODUCTIONIn the data warehousing (DW) field, whatever the enterprise's philosophy falls into i) Bill Inmon's camp where the DW is one part of the overall BI system, or into ii) Ralph Kimball's camp where the DW is the conglomerate of all data marts within the enterprise, data issued from the operational systems are extracted, transformed, cleansed and finally loaded into the fact and dimension tables of the famous star schema which represents the keystone modeling diagram that has twofold objectives: first, it highlights the subject of analyses (i.e., fact representing the activity to be evaluated) and, secondly, it shows up the axes (i.e., dimensions) according to which the fact's data could be analyzed (Inmon, 2002). This strong dependency between the DW and the data source (DS) leads to a new evolution problem that addresses the impact of the DS schema evolution on the DW. In fact, the dynamic evolution of business processes within the enterprise can lead to another evolution of the DS schema. The associated DW cannot escape from this evolution which can simultaneously affect its schema, stored data, and also the ETL process (Extract-TransformLoad) (Vassiliadis, 2009). This paper treats this evolution problematic, it is organized as follows. In section 2, we overview researches related to the DW evolution problem.Section 3 proposes a model-driven approach for the propagation of DS schema changes towards the DW. Section 4 defines the evolution models of both the DS and the DW. Section 5 presents an example of transformation rules formalized in QVT (Query \View \Transformation); it is for the automatic passage between these two models. Finally, section 6 concludes the paper and enumerates our future perspectives. RELATED RESEARCHESThe DW evolution problem has been the subject of several research studies. It was treated from several points of views: Analytical need evolution, DS schema evolution, etc.Some researchers (Favre et al., 2007), (Benitez et al., 2004), (Blaschka et al., 1999 ) have limited their study to the DW evolution as a result of evolution of decision makers needs, without considering the case of the DS evolution. Other literature works (Bellahsene, 2002), (Wrembel and Bebel, 2007), (Solodovnikova, 2008) have examined the evolution of the source schema as well a...

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Metadata Management in a Multiversion Data Warehouse

Cited by 23 publications

References 32 publications

Temporal and Evolving Data Warehouse Design

Temporal and Evolving Data Warehouse Design

Building an Effective Data Warehousing for Financial Sector

Toward Evolution Models for Data Warehouses

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