Knowledge Based Data Cleaning for Data Warehouse Quality

Bradji, Louardi; Boufaïda, Mahmoud

doi:10.1007/978-3-642-22410-2_33

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…Data quality is vital to organizations since it enables them make informed and accurate decisions [8,9]. According to [4] data cleansing is a relatively new research field.…”

Section: Open User Involvement In Data Cleaning For Data Warehouse Qumentioning

confidence: 99%

“…Although after Maletic and Marcus in the year 2000 who proposed a framework which other authors/researchers have looked into and proposed slight different architectures, the outcome still needs further investigation. The process is computationally expensive on very large data sets and thus it was almost impossible to do with old technology [5,8].…”

Section: Open User Involvement In Data Cleaning For Data Warehouse Qumentioning

confidence: 99%

“…They consist of dealing with missing data, determining record usability, erroneous data, etc. Different approaches address different issues [5,8].…”

Section: Open User Involvement In Data Cleaning For Data Warehouse Qumentioning

confidence: 99%

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A Review of Data Cleansing Concepts Achievable Goals and Limitations

Sarpong¹,

Arthur²

2013

IJCA

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Data Cleansing is an activity involving a process of detecting and correcting the errors and inconsistencies in data warehouse. It deals with identification of corrupt and duplicate data inherent in the data sets of a data warehouse to enhance the quality of data. The study looked into investigating some research works conducted in the area of data cleansing. A thorough review into these existing works was studied to determine the achievable goals and the limitations that arose based on the approaches conducted by the researchers. They identification of errors by most of these researchers has led into the development of several frameworks and systems to be implemented in the area of data warehousing.Generally, these findings will contribute to the emerging empirical evidence of the strategic role data cleansing play in the growth of organizations, institutions and other government agencies in terms of data quality and reporting purposes and also to gain competitive advantage since they will overcome the mere existence of dirty data.

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“…Data quality is vital to organizations since it enables them make informed and accurate decisions [8,9]. According to [4] data cleansing is a relatively new research field.…”

Section: Open User Involvement In Data Cleaning For Data Warehouse Qumentioning

confidence: 99%

Section: Open User Involvement In Data Cleaning For Data Warehouse Qumentioning

confidence: 99%

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A Review of Data Cleansing Concepts Achievable Goals and Limitations

Sarpong¹,

Arthur²

2013

IJCA

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“…They will also store information about the rules violations in order to provide analysis of such data [8,26].…”

Section: Rule Based Data Cleaningmentioning

confidence: 99%

“…Consequently, the first step in the development of any RBS for DC is to begin with a collection of the knowledge from which the rules will be derived. Organizing the collected knowledge so that translation to rules will be straightforward is also a challenging task for the knowledge engineer [2,8].…”

Section: Introductionmentioning

confidence: 99%

A Rule Management System for Knowledge Based Data Cleaning

Bradji¹,

Boufaïda

2011

IIM

Self Cite

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In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.

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