A Comparative Analysis of Data Cleaning Approaches to Dirty Data

Abstract

Data Cleansing or (data scrubbing) is an activity involving a process of detecting and correcting the errors and inconsistencies in data warehouse. Thus poor quality data i.e.; dirty data present in a data mart can be avoided using various data cleaning strategies, and thus leading to more accurate and hence reliable decision making. The quality data can only be produced by cleaning the data and pre-processing it prior to loading it in the data warehouse.As not all the algorithms address the problems related to every type of dirty data, one has to prioritize the need of its organization and use the algorithm according to their requirements and occurrence of dirty data. This paper focuses on the two data cleaning algorithms: Alliance Rules and HADCLEAN and their approaches towards the data quality. It also includes a comparison of the various factors and aspects common to both. General TermsData Mart, Data Warehouse, Dirty data.

Choosing allowability boundaries for describing objects in subject areas

“…Detecting anomalies of objects that differ from the main part of the data in the subject areas during data mining [5], there are several approaches to detecting outliers depending on the type of task: parsing, data transformation, methods of enforcing integrity constraints, duplicate detection methods, and others [6]. Almost fully similar works were overviewed by [7] in 2018.…”