DIRA: A Framework Of Data Integration Using Data Quality

TL;DR

This paper introduces DIRA, a data integration framework that improves answer quality by assessing data source quality, selecting significant sources, and ranking top-k answers based on user-defined quality criteria.

Contribution

The paper proposes a novel framework, DIRA, which integrates data quality assessment and ranking to enhance data integration and query answer relevance.

Findings

Effective data source selection based on quality measures

Ranking of top-k query answers according to user preferences

Improved accuracy of query responses through quality-aware integration

Abstract

Data integration is the process of collecting data from different data sources and providing user with unified view of answers that meet his requirements. The quality of query answers can be improved by identifying the quality of data sources according to some quality measures and retrieving data from only significant ones. Query answers that returned from significant data sources can be ranked according to quality requirements that specified in user query and proposed queries types to return only top-k query answers. In this paper, Data integration framework called data integration to return ranked alternatives (DIRA) will be introduced depending on data quality assessment module that will use data sources quality to choose the significant ones and ranking algorithm to return top-k query answers according to different queries types.