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The problem of version detection is critical in many important application scenarios, including software clone identification, Web page ranking, plagiarism detection, and peer-to-peer searching. A natural and commonly used approach to version detection relies on analyzing the similarity between files. Most of the techniques proposed so far rely on the use of hard thresholds for similarity measures. However, defining a threshold value is problematic for several reasons: in particular (i) the threshold value is not the same when considering different similarity functions, and (ii) it is not semantically meaningful for the user. To overcome this problem, our work proposes a version detection mechanism for XML documents based on Naïve Bayesian classifiers. Thus, our approach turns the detection problem into a classification problem. In this paper, we present the results of various experiments on synthetic data that show that our approach produces very good results, both in terms of recall and precision measures.

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Abstract. In this paper, we propose an object-oriented version model which presents temporal concepts to store not only the object lifetime but also the history of dynamic attributes and relationships defined in the versioned objects and versions. One of the main features of our model is the possibility of having two different time orders, branched time for the object and linear time for each version. The model supports integration with existing databases, by allowing the modeling of normal classes among the temporal versioned classes. Finally, an approach to its implementation on top of a commercial database is presented.