A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
On the agreement of many trees
Information Processing Letters
Consensus system for solving conflicts in distributed systems
Information Sciences—Informatics and Computer Science: An International Journal
A Model for XML Schema Integration
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
Comparison of Schema Matching Evaluations
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
A bag of paths model for measuring structural similarity in Web documents
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Advanced Methods for Inconsistent Knowledge Management (Advanced Information and Knowledge Processing)
An XML Schema integration and query mechanism system
Data & Knowledge Engineering
A METHOD FOR ONTOLOGY CONFLICT RESOLUTION AND INTEGRATION ON RELATION LEVEL
Cybernetics and Systems
INCONSISTENCY OF KNOWLEDGE AND COLLECTIVE INTELLIGENCE
Cybernetics and Systems
A model for complex tree integration tasks
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
A METHOD FOR COMPLEX HIERARCHICAL DATA INTEGRATION
Cybernetics and Systems - KNOWLEDGE PROCESSING METHODOLOGIES IN INTELLIGENT AUTONOMOUS SYSTEMS
Some properties of complex tree integration criteria
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
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The consensus finding problem is known in the literature as a solution to inconsistency problems. Such inconsistency may come from different opinions of problem participants or data uncertainty. Consensus methods are used to find elements that represent all others in the inconsistent dataset and are a good compromise of the differing opinions. The O1 solution to consensus problem is best defined as finding the element that has the smallest sum of distances to all other elements. It is solved for many simple structures, but not for the complex tree structure. In this paper we propose several algorithms to find O1 consensus for complex trees (extended labeled trees), including a greedy algorithm and several approximate algorithms. We evaluate their approximation levels in terms of the 1-optimality criterion.