• Corpus ID: 61179601

A Descriptive Classification of Causes of Data Quality Problems in Data Warehousing

@inproceedings{Singh2010ADC,
  title={A Descriptive Classification of Causes of Data Quality Problems in Data Warehousing},
  author={Ranjit Singh and Kawaljeet Singh},
  year={2010}
}
Data warehousing is gaining in eminence as organizations become awake of the benefits of decision oriented and business intelligence oriented data bases. [] Key Result We hope this will help developers & Implementers of warehouse to examine and analyze these issues before moving ahead for data integration and data warehouse solutions for quality decision oriented and business intelligence oriented applications.

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References

SHOWING 1-10 OF 33 REFERENCES
Warehouse Creation - A Potential Roadblock to Data Warehousing
TLDR
The principal goal of this paper is to identify the common issues in data integration and data-warehouse creation, which have been studied for about two decades.
A Taxonomy of Dirty Data
TLDR
A comprehensive classification of dirty data is developed for use as a framework for understanding how dirty data arise, manifest themselves, and may be cleansed to ensure proper construction of data warehouses and accurate data analysis.
Key issues in achieving data quality and consistency in data warehousing among large organisations in Australia
  • A. Rudra, Emilie Yeo
  • Business
    Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers
  • 1999
TLDR
It is found that the quality of data in a data warehouse could be influenced by factors like: data not fully captured, heterogeneous system integration and lack of policy and planning from management.
A classification of semantic conflicts in heterogeneous database systems
TLDR
A classification of semantic conflicts which can be used as the basis for the incremental discovery and resolution of these conflicts and provides a systematic representation of alternative semantic interpretations of conflicts during the reconciliation process.
Legacy Information Systems: Issues and Directions
TLDR
An overview of existing research is offered and two promising methodologies for legacy information system migration are presented, which can support organizations into the future.
ISSN
Summary The ISSN system, which was established in the 1970s for the identification of printed serial publications, is also a powerful system for the identification of electronic resources, thanks to
Challenges with legacy data: Knowing your data enemy is the first step in overcoming it
  • Practice Leader, Agile Development, Rational Methods Group, IBM,
  • 2001
Dealing with Missing Values In The Data Warehouse
  • 1999
Data Cleaning: Problems and Current Approaches
TLDR
This work classifies data quality problems that are addressed by data cleaning and provides an overview of the main solution approaches and discusses current tool support for data cleaning.
...
...