Browsing by Author "Haddad, Mohammed"

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    A Graph Approach for Enhancing Process Models Matchmaking
    (IEEE, 2015-06-27) Belhoul, Yacine; Yahiaoui, Saïd; Haddad, Mohammed; Gater, Ahmed; Kheddouci, Hamamache; Bouzeghoub, Mokrane
    Recent attempts have been done to measure similarity of process models based on graph matching. This problem is known to be difficult and its computational complexity is exponential. Thus, heuristics should be proposed to obtain approximations. Spectral graph matching methods, in particular eigenvalue-based projections, are know to be fast but they lost some quality in the obtained matchmaking. In this paper, we propose a graph approach for the problem of inexact matching of process models. Our approach combines a spectral graph matching method and a string comparator based algorithm in order to improve the quality of process models matchmaking. The proposed method performs the matchmaking at both structural and semantic levels. Experimentation is provided to show the performance of our method to rank a collection of process models according to a particular user query, compared to previous work.
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    Coloring based approach for matching unrooted and/or unordered trees
    (Elsevier, 2013-04) Yahiaoui, Saïd; Haddad, Mohammed; Effantin, Brice; Kheddouci, Hamamache
    We consider the problem of matching unrooted unordered labeled trees, which refers to the task of evaluating the distance between trees. One of the most famous formalizations of this problem is the computation of the edit distance defined as the minimum-cost sequence of edit operations that transform one tree into another. Unfortunately, this problem has been proved to be NP-complete. In this paper, we propose a new algorithm to measure distance between unrooted unordered labeled trees. This algorithm uses a specific graph coloring to decompose the trees into small components (stars and bistars). Then, it determines a distance between two trees by computing the edit distance between their components. We prove that the proposed distance is a pseudo-metric and we analyze its time complexity. Our experimental evaluations on large synthetic and real world datasets confirm our analytical results and suggest that the distance we propose is accurate and its algorithm is scalable.