Data Structures for Multiversion Data Warehouse

Chmiel, Jan

doi:10.1007/978-3-642-12082-4_26

Cited by 1 publication

(3 citation statements)

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“…Chmiel [45] proposed a bitmap approach for sharing data between multiple versions. However, Rizzi [55] argues that the use of bitmap approach limits what-if analysis, because processing queries to retrieve the data that is shared across multiple versions is a challenging task.…”

Section: Implicit Versioningmentioning

confidence: 99%

“…From the table, it can be observed that majority of the schemes support schema and data changes. Also, it can be seen that no study proposes the use of augmented schema because these schemes implicitly record temporal extension and data is shared, for instance, by the bitmap approach [45]. This limits the "history of change" analysis.…”

Section: Implicit Versioningmentioning

confidence: 99%

“…Among these, authors in [33] provide temporal attributes to all elements of the DW metamodel and support cross-version queries, as well as fact-constellation schemes. Chmiel [45] on the other hand focuses on optimization of queries. Another observation is that a couple of approaches, [37,38], support multiversioned fact table.…”

Section: Implicit Versioningmentioning

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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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: Implicit Versioningmentioning

confidence: 99%

Section: Implicit Versioningmentioning

confidence: 99%