Schema Integration on Massive Data Sources

TL;DR

This paper addresses the challenge of schema integration in big data environments by developing batch and incremental algorithms that efficiently merge schemas from diverse data sources, considering attribute name variations.

Contribution

It introduces ED Join and Semantic Join algorithms for schema integration that handle attribute representation differences, with extensive experimental validation.

Findings

Algorithms efficiently integrate schemas from massive data sources.

Proposed methods effectively handle attribute name variations.

Experimental results demonstrate high effectiveness and efficiency.

Abstract

As the fundamental phrase of collecting and analyzing data, data integration is used in many applications, such as data cleaning, bioinformatics and pattern recognition. In big data era, one of the major problems of data integration is to obtain the global schema of data sources since the global schema could be hardly derived from massive data sources directly. In this paper, we attempt to solve such schema integration problem. For different scenarios, we develop batch and incremental schema integration algorithms. We consider the representation difference of attribute names in various data sources and propose ED Join and Semantic Join algorithms to integrate attributes with different representations. Extensive experimental results demonstrate that the proposed algorithms could integrate schemas efficiently and effectively.