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A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into Dimensions
- Source :
- IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, In press, ⟨10.1109/TKDE.2019.2925621⟩, IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2021, 33 (1), pp.28-42. ⟨10.1109/TKDE.2019.2925621⟩
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- International audience; A decision support system is used by decision makers for a long time. But, in some cases, the originally designed multidimensional schema does not cover the entire needs of decision makers, which can change over time. One such unfulfilled needs, is using facts to describe dimension members. In this article, we propose a methodology to transform the constellation schema of a data warehouse by integrating factual data into a dimension. The proposed methodology and algorithms enrich a constellation multidimensional schema with new analytical possibilities for decision makers. This enrichment has repercussions for the entire multidimensional schema that are managed by multidimensional modeling, hierarchy calculation and the hierarchy version. In this article, we present a theoretical view of the proposed methodology supported by a case study, an implemented prototype and a complete evaluation based on a standard benchmark.
- Subjects :
- Data Warehouse
Decision support system
Computer science
Big data
02 engineering and technology
computer.software_genre
Data modeling
Hierarchy
020204 information systems
Schema (psychology)
0202 electrical engineering, electronic engineering, information engineering
[INFO]Computer Science [cs]
Design methods
OLAP
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Information retrieval
business.industry
Online analytical processing
Modeling
Refinement
Data warehouse
Computer Science Applications
Version
Computational Theory and Mathematics
business
computer
Information Systems
Data integration
Subjects
Details
- ISSN :
- 23263865 and 10414347
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Knowledge and Data Engineering
- Accession number :
- edsair.doi.dedup.....d1c110b3fbb50b8cf83573d3e20149a3
- Full Text :
- https://doi.org/10.1109/tkde.2019.2925621