Data warehouse design: a schema-transformation approach

Marotta, Adriana; Ruggia, Raúl

doi:10.1109/sccc.2002.1173188

Cited by 21 publications

(23 citation statements)

References 3 publications

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“…(ii) For handling changes in a DW (recall schema and data changes), a number of efforts [17][18][19][20][21] have been made. According to Eder and Wiggisser [27] and Eder and Wiggisser [28], there are two ways to handle these changes: (a) updating schema, transforming existing data into the new schema, discarding the schema, and using the updated schema for future data population: this is called evolution scheme [2]; (b) creating a new schema, maintaining both schemas, and using only the new schema for future data population [24]: however, both schemas can be used for retrieval from the DW.…”

Section: Taxonomy Of Temporal Andmentioning

confidence: 99%

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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: Taxonomy Of Temporal Andmentioning

confidence: 99%

“…To summarize, the existence of TDBs and changes in them lead to the development of a Temporal Data Warehouse (TDW). A TDW requires consideration of several aspects in its DW, including temporal support in its data models [12][13][14][15][16], changes to its schema, and changes to its data [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Evolving Data Warehouse Design

Faisal¹,

Sarwar²,

Shahzad³

et al. 2017

Scientific Programming

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“…Schema evolution may be managed by two different approaches namely adaptational approach and versioning approach. In [17] adaptational approach existing instances have to be adapted to the new schema and the application programs that run over the database before the changes, also have to be updated. In versioning approach, new version is created over previous version and no modification is applied directly on the existing schema.…”

Section: Introductionmentioning

confidence: 99%

Schema Evolution for Data Warehouse: A Survey

Arora¹,

Gosain²

2011

IJCA

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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.

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“…Therefore, practitioners and researches have strived to propose methodologies to design multidimensional models from existing ER diagrams [3,5,8,9,[14][15][16][19][20][21]. Even though [12,16,20] acknowledged the importance of grain consistence, they provide only vague guidelines for the mapping and do not specify any concrete algorithms to perform task.…”

Section: Introductionmentioning

confidence: 99%

“…Golfarelli et al [8] presented a graphical conceptual model (Dimensional Fact model) for data warehouses and a semi-automated methodology to construct a tree-structured fact schema from an Entity-Relation schema. Marotta et al [15] provided a set of transformation rules to trace the mapping between source logical schema and data warehouse logical schema. Tryfona et al, [21] presented a new model, the starER model, to make semantics richer than traditional multidimensional model to record manyto-many relationships between fact and dimensional tables.Boehnlein et al [3] proposed the SERM model to visualize existence dependencies between data object types.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Grain Preservation Mapping Algorithm: From ER Diagram to Multidimensional Model

Chen

Hsu

2005

Advanced Distributed Systems

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Abstract. Many practitioners and researchers advocate that the designs of the data models of the data warehouses should incorporate the source data as much as possible to answer the finest levels of queries. On the other hand, the source data are very likely to come from systems designed with ER Diagrams. Therefore, many researches have been devoted to design methodologies to build multidimensional model based on corresponding source ER diagrams. However, to the best of our knowledge, no algorithm has been proposed to systematically translates an entire ER Diagram into a multidimensional model with hierarchical snowflake structures. The algorithm proposed in the paper promised to do so with two characteristics, namely, grain preservation and minimal distance from each table to the fact table. Grain preservation characteristic guarantees that translated multidimensional model has cohesive granularity among entities. The minimal distance characteristics guarantees that if an entity can be connected to the fact table in the derived model with more than one paths, the one with the shortest hops will always be chosen. The first characteristic is achieved by translating problematic relationships between entities with weight factor attributes in bridging tables and enhancing fact tables with unique primary keys. The second characteristic is achieved by including a revised shortest path algorithm in the translating algorithm with the distance being calculated as the number of relationships required between entities.

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Data warehouse design: a schema-transformation approach

Cited by 21 publications

References 3 publications

Temporal and Evolving Data Warehouse Design

Temporal and Evolving Data Warehouse Design

Schema Evolution for Data Warehouse: A Survey

An Efficient and Grain Preservation Mapping Algorithm: From ER Diagram to Multidimensional Model

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