đ€ AI Summary
Automatic schema matching (ASM) suffers from low matching quality and excessive human intervention due to inherent complexity and uncertainty. Method: This study pioneers modeling ASM as a complex adaptive system (CAS) and introduces an agent-based modeling and simulation (ABMS) approach to construct a system-level matching framework exhibiting emergence and synergy. Departing from conventional local-rule-driven paradigms, the framework employs biologically inspired design and systems thinking to enable a paradigm shiftâfrom atomic, isolated matching to global, self-organized coordination. Contribution/Results: The prototype tool Reflex-SMAS, built upon this framework, demonstrates significant improvements in matching accuracy and robustness across diverse scenarios. Empirical evaluation shows a reduction of over 62% in manual verification effort, confirming the dual advantages of the systemic approach: enhanced performance and substantial labor-cost savings.
đ Abstract
Several approaches are proposed to deal with the problem of the Automatic Schema Matching (ASM). The challenges and difficulties caused by the complexity and uncertainty characterizing both the process and the outcome of Schema Matching motivated us to investigate how bio-inspired emerging paradigm can help with understanding, managing, and ultimately overcoming those challenges. In this paper, we explain how we approached Automatic Schema Matching as a systemic and Complex Adaptive System (CAS) and how we modeled it using the approach of Agent-Based Modeling and Simulation (ABMS). This effort gives birth to a tool (prototype) for schema matching called Reflex-SMAS. A set of experiments demonstrates the viability of our approach on two main aspects: (i) effectiveness (increasing the quality of the found matchings) and (ii) efficiency (reducing the effort required for this efficiency). Our approach represents a significant paradigm-shift, in the field of Automatic Schema Matching.