A Study of a Detection and Elimination of Data Inconsistency in Data Integration
Dattatray Raghunath Kale, Smita Y. Aparadh · Zenodo (CERN European Organization for Nuclear Research) · 2016
Data quality is highly important for running the effective business process. The real world data is spread over the various locations.A collections of these data from the different data sources and presenting the entire collection as a single source is difficult. Data integration involves combining data from numerous dissimilar sources, which are stored using different technologies and present a unified view of the data.Heterogenous and homogenous data is presented at various locations. A big problem in data integration is conflicts occurred into various data sources. Data Inconsistency exists when various and conflicting stories of the same data appear in different places. Data inconsistency shows unreliable information. So in this paper we are presenting the various techniques for finding data inconsistency in data integration.