Using n-grams for the Automated Clustering of Structural Models
- 848 Downloads
Abstract
Model comparison and clustering are important for dealing with many models in data analysis and exploration, e.g. in domain model recovery or model repository management. Particularly in structural models, information is captured not only in model elements (e.g. in names and types) but also in the structural context, i.e. the relation of one element to the others. Some approaches involve a large number of models ignoring the structural context of model elements; others handle very few (typically two) models applying sophisticated structural techniques. In this paper we address both aspects and extend our previous work on model clustering based on vector space model, with a technique for incorporating structural context in the form of n-grams. We compare the n-gram accuracy on two datasets of Ecore metamodels in AtlanMod Zoo: small random samples using up to trigrams and a larger one (\({\sim }\)100 models) up to bigrams.
Keywords
Model-driven engineering Model comparison Vector space model Hierarchical clustering n-gramsReferences
- 1.Babur, Ö., Cleophas, L., van den Brand, M.: Hierarchical clustering of metamodels for comparative analysis and visualization. In: Proceedings of the 12th European Conference on Modelling Foundations and Applications, 2016, pp. 2–18 (2016)Google Scholar
- 2.Babur, Ö., Cleophas, L., Verhoeff, T., van den Brand, M.: Towards statistical comparison and analysis of models. In: Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development, pp. 361–367 (2016)Google Scholar
- 3.Basciani, F., Rocco, J., Ruscio, D., Iovino, L., Pierantonio, A.: Automated clustering of metamodel repositories. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 342–358. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-39696-5_21 CrossRefGoogle Scholar
- 4.Bergroth, L., Hakonen, H., Raita, T.: A survey of longest common subsequence algorithms. In: Seventh International Symposium on String Processing and Information Retrieval, 2000, SPIRE 2000, Proceedings, pp. 39–48. IEEE (2000)Google Scholar
- 5.Bislimovska, B., Bozzon, A., Brambilla, M., Fraternali, P.: Textual and content-based search in repositories of web application models. ACM Trans. Web (TWEB) 8(2), 11 (2014)Google Scholar
- 6.Klint, P., Landman, D., Vinju, J.: Exploring the limits of domain model recovery. In: 2013 29th IEEE International Conference on Software Maintenance (ICSM), pp. 120–129. IEEE (2013)Google Scholar
- 7.Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)Google Scholar
- 8.Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing, vol. 999. MIT Press, Cambridge (1999)Google Scholar
- 9.Mass, Y., Mandelbrod, M.: Retrieving the most relevant xml components. In: INEX 2003 Workshop Proceedings, p. 58. Citeseer (2003)Google Scholar
- 10.Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: 18th International Conference on Data Engineering, 2002, Proceedings, pp. 117–128. IEEE (2002)Google Scholar
- 11.Rubin, J., Chechik, M.: N-way model merging. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, pp. 301–311. ACM (2013)Google Scholar
- 12.Stahl, T., Völter, M., Bettin, J., Haase, A., Helsen, S.: Model-Driven Software Development: Technology, Engineering, Management. Wiley, New York (2006)Google Scholar
- 13.Stephan, M., Cordy, J.R.: A survey of model comparison approaches and applications. In: Modelsward, pp. 265–277 (2013)Google Scholar