On Schema Evolution in Multidimensional Databases

Blaschka, Markus; Sapia, Carsten; Höfling, Gabriele

doi:10.1007/3-540-48298-9_17

Cited by 102 publications

(81 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the literature, different authors [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15] have proposed different operators to handle schema evolution at different levels. The schema evolution approach focuses on dimension updates [1,3,6,8,9,13,15], instances updates [2,7,13,14], facts updates and attribute updates [3,5,6,8,10,12,13].…”

Section: Literature Survey On Schema Evolutionmentioning

confidence: 99%

“…The schema evolution approach focuses on dimension updates [1,3,6,8,9,13,15], instances updates [2,7,13,14], facts updates and attribute updates [3,5,6,8,10,12,13]. In [4], author has discussed about the quality factors and there affects on evolution operators.…”

Section: Literature Survey On Schema Evolutionmentioning

confidence: 99%

“…In this paper [3], author defined a schema evolution related to algebra on formal description of multidimensional schemas and instances. It consists of 6 tuple (F, L, A, gran, class, attr) explained in Table 3: - It consists of removing an existing fact from the MD model but this fact must not be connected to any dimension or any attributes.…”

Section: 3blaschka Etal [1999]mentioning

confidence: 99%

“…Authors in [3,6,8,13,15] were focused on dimension evolution and fact evolution. Benitez [8] presented add/ delete /rename of measure whereas Blashka [3] presented add and delete of fact.…”

Section: Comparative Analysismentioning

confidence: 99%

See 3 more Smart Citations

Schema Evolution for Data Warehouse: A Survey

Arora¹,

Gosain²

2011

IJCA

View full text Add to dashboard Cite

Data warehouse is considered as the core component of the modern decision support systems. Due to the major support of data warehouse in the daily transaction of an enterprise, the requirements for the design and the implementation of DW are dynamic and subjective. This dynamic nature of the data warehouse may reflect the evolution in the data warehouse. Data warehouse evolution may be focused on three approaches namely schema evolution, schema versioning and view maintenance. Evolution of the data warehouse may often change their data and structure (schema changes). These schema changes may be consider according to the change in structure, software and users" requirement. Schema evolution in data warehouse consists of various level namely structural level, conceptual level and behavioural level. This paper mainly focuses on schema evolution and proposes the operators to handle the creation and evolution of aggregated fact table. Our work is to do comparative study for various approaches of schema evolution.

show abstract

Section: Literature Survey On Schema Evolutionmentioning

confidence: 99%

Section: Literature Survey On Schema Evolutionmentioning

confidence: 99%

Section: 3blaschka Etal [1999]mentioning

confidence: 99%

“…Authors in [3,6,8,13,15] were focused on dimension evolution and fact evolution. Benitez [8] presented add/ delete /rename of measure whereas Blashka [3] presented add and delete of fact.…”

Section: Comparative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Schema Evolution for Data Warehouse: A Survey

Arora¹,

Gosain²

2011

IJCA

View full text Add to dashboard Cite

show abstract

“…Other previous work on schema evolution, e.g. [1][2][3][4], has also presented approaches in terms of just one data model.…”

Section: Introductionmentioning

confidence: 99%

Schema Evolution in Data Warehousing Environments – A Schema Transformation-Based Approach

Fan

Poulovassilis

2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In heterogeneous data warehousing environments, autonomous data sources are integrated into a materialised integrated database. The schemas of the data sources and the integrated database may be expressed in different modelling languages. It is possible for either the data source schemas or the warehouse schema to evolve. This evolution may include evolution of the schema, or evolution of the modelling language in which the schema is expressed, or both. In such scenarios, it is important for the integration framework to be evolvable, so that the previous integration effort can be reused as much as possible. This paper describes how the AutoMed heterogeneous data integration toolkit can be used to handle the problem of schema evolution in heterogeneous data warehousing environments. This problem has been addressed before for specific data models, but AutoMed has the ability to cater for multiple data models, and for changes to the data model.

show abstract