Abstract

Schema matching is considered as one of the essential phases of data integration in database systems. The main aim of the schema matching process is to identify the correlation between schema which helps later in the data integration process. The main issue concern of schema matching is how to support the merging decision by providing the correspondence between attributes through syntactic and semantic heterogeneous in data sources. There have been a lot of attempts in the literature toward utilizing database instances to detect the correspondence between attributes during schema matching process. Many approaches based on instances have been proposed aiming at improving the accuracy of the matching process. This paper set out a classification of schema matching research in database system exploiting database schema and instances. We survey and analyze the schema matching techniques applied in the literature by highlighting the strengths and the weaknesses of each technique. A deliberate discussion has been reported highlights on challenges and the current research trends of schema matching in database. We conclude this paper with some future work directions that help researchers to explore and investigate current issues and challenges related to schema matching in contemporary databases.

Highlights

  • Nowadays, integrating and managing a tremendous amount of data has been extremely simplified due to the advancement in information technology

  • From the work presented throughout this paper, it can be concluded that matching heterogeneous databases is considered as one of the biggest challenges of data integration in database applications

  • Schema matching is a challenging issue in many contemporary database applications, including data integration, data warehousing, E-commerce, and semantic query processing

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Summary

A Survey of Schema Matching Research using Database Schemas and Instances

College of Computer Information Technology American University in the Emirates Dubai, United Arab Emirates. The main aim of the schema matching process is to identify the correlation between schema which helps later in the data integration process. The main issue concern of schema matching is how to support the merging decision by providing the correspondence between attributes through syntactic and semantic heterogeneous in data sources. There have been a lot of attempts in the literature toward utilizing database instances to detect the correspondence between attributes during schema matching process. This paper set out a classification of schema matching research in database system exploiting database schema and instances. A deliberate discussion has been reported highlights on challenges and the current research trends of schema matching in database. We conclude this paper with some future work directions that help researchers to explore and investigate current issues and challenges related to schema matching in contemporary databases

INTRODUCTION
SCHEMA INFORMATION LEVELS
Instance Level
Hybrid Level
Auxiliary Level
CLASSIFICATION OF SCHEMA MATCHING METHODS
Schema Level Matching
Instance Level Matching
Combination of Multiple Matcher
TECHNIQUES APPLIED FOR INSTANCE LEVEL MATCHING
Syntactic Techniques
Semantic Techniques
RELATED WORK OF INSTANCE-BASE SCHEMA MATCHING
Neural Networks
Machine Learning
Information Theoretic
Rule Based
DISCUSSION AND RESEARCH
Incomplete and Crowd-Sourcing Databases
Uncertain Databases
Big Data
CONCLUSION
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