Conference proceeding
SAMSTAR: a semi-automated lexical method for generating star schemas from an entity-relationship diagram
Proceedings of the ACM tenth international workshop on data warehousing and olap
09 Nov 2007
Abstract
The star schema is widely accepted as the de facto data model for data warehouse design. A popular approach for developing a star schema is to develop it from an entity-relationship diagram with some heuristics. Most of the existing approaches analyze the semantics of an ERD to generate a star schema. In this paper, we present the SAMSTAR method, which semi-automatically generates star schemas from an ERD by analyzing its semantics as well as structure. The novel features of SAMSTAR are (1) the use of the notion of Connection Topology Value (CTV) in identifying the candidates of facts and dimensions and (2) the use of Annotated Dimensional Design Patterns (A_DDP) as well as WordNet to extend the list of dimensions. We illustrate our method by applying it to the examples from existing literature. We prove that the outputs of our method are a superset of those of the existing methods. The SAMSTAR method simplifies the work of experienced designers and gives a smooth head-start to novices.
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44 citations in Scopus
Details
- Title
- SAMSTAR
- Creators
- Il Song - Drexel UniversityRitu Khare - Drexel UniversityBing Dai - Drexel University
- Publication Details
- Proceedings of the ACM tenth international workshop on data warehousing and olap
- Conference
- ACM 10th international workshop on data warehousing and olap, 10th
- Series
- DOLAP '07
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-79959670172
- Other Identifier
- 991019184307804721