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Accelerating similarity-based model matching using dual hashing.

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Název: Accelerating similarity-based model matching using dual hashing.
Autoři: He, Xiao, Liu, Yi, He, Huihong
Zdroj: Software & Systems Modeling; Aug2025, Vol. 24 Issue 4, p1011-1034, 24p
Témata: EUCLIDEAN distance, OPTIMIZATION algorithms, MATHEMATICAL optimization, ELECTRONIC data processing
Abstrakt: Similarity-based model matching is the cornerstone of model versioning. It pairs model elements based on a distance metric (e.g., edit distance). However, calculating the distances between elements is computationally expensive. Consequently, a similarity-based matcher typically suffers from performance issues when the model size increases. Based on observation, there are two main causes of the high computation cost: (1) when matching an element p, the matcher calculates the distance between p and every candidate element q, despite the obvious dissimilarity between p and q; (2) the matcher always calculates the distance between p and q ′ , even though q and q ′ are very similar and the distance between p and q is already known. This paper proposes a dual-hash-based approach, which employs two entirely different hashing techniques—similarity-preserving hashing and integrity-based hashing—to accelerate similarity-based model matching. With similarity-preserving hashing, our approach can quickly filter out the dissimilar candidate elements according to their similarity hashes computed using our similarity-preserving hash function, which maps an element to a 64-bit binary hash. With integrity-based hashing, our approach can cache and reuse computed distance values by associating them with the checksums of model elements. We also propose an index structure to facilitate hash-based model matching. Our approach has been implemented and integrated into EMF Compare. We evaluate our approach using open-source Ecore and UML models. The results show that our hash function is effective in preserving the similarity between model elements and our matching approach reduces time costs by 20–88% while assuring the matching results consistent with EMF Compare. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Similarity-based model matching is the cornerstone of model versioning. It pairs model elements based on a distance metric (e.g., edit distance). However, calculating the distances between elements is computationally expensive. Consequently, a similarity-based matcher typically suffers from performance issues when the model size increases. Based on observation, there are two main causes of the high computation cost: (1) when matching an element p, the matcher calculates the distance between p and every candidate element q, despite the obvious dissimilarity between p and q; (2) the matcher always calculates the distance between p and q ′ , even though q and q ′ are very similar and the distance between p and q is already known. This paper proposes a dual-hash-based approach, which employs two entirely different hashing techniques—similarity-preserving hashing and integrity-based hashing—to accelerate similarity-based model matching. With similarity-preserving hashing, our approach can quickly filter out the dissimilar candidate elements according to their similarity hashes computed using our similarity-preserving hash function, which maps an element to a 64-bit binary hash. With integrity-based hashing, our approach can cache and reuse computed distance values by associating them with the checksums of model elements. We also propose an index structure to facilitate hash-based model matching. Our approach has been implemented and integrated into EMF Compare. We evaluate our approach using open-source Ecore and UML models. The results show that our hash function is effective in preserving the similarity between model elements and our matching approach reduces time costs by 20–88% while assuring the matching results consistent with EMF Compare. [ABSTRACT FROM AUTHOR]
ISSN:16191366
DOI:10.1007/s10270-024-01173-1