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Recently, business protocol discovery has taken more attention in the field of web services. This activity permits a better description of the web service by giving information about its dynamics. The latter is not supported by theWSDL language which concerns only the static part. The problem is that the only information available to construct the dynamic part is the set of log files saving the runtime interaction of the web service with its clients. In this paper, a new approach based on the Discrete Wavelet Transformation (DWT) is proposed to discover the business protocol of web services. The DWT allows reducing the problem space while preserving essential information. It also overcomes the problem of noise in the log files. The proposed approach has been validated using artificially-generated log files.
Rocznik
Tom
Strony
535--546
Opis fizyczny
Bibliogr. 34 poz., tab., rys.
Twórcy
autor
- MISC Laboratory, Constantine University, Ali Mendjeli, Algeria
autor
autor
autor
Bibliografia
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- [4] L. Berti-Equille and M. Herschel, The 7th International Workshop on Quality in Databases (QDB 2009) in conjunction with VLDB 2009, Aug. 2009.
- [5] A. Moudjari, S. Chikhi, and H. Kheddouci, “Latent semantic analysis for business protocol discovery using log files”, International Journal of Web Engineering and Technology 9 (4), 365–396 (2014).
- [6] B. Benatallah, F. Casati, and F. Toumani, “Analysis and management of web service protocols”, in Conceptual Modeling–ER 2004, 524–541, Springer, 2004.
- [7] A. Moudjari, S. Chikhi, and A. Draa, “Business protocol discovery from log files using a tf-idf-based technique”, in 2015 Seventh International Conference on Ubiquitous and Future Networks, 651–656, IEEE, 2015.
- [8] F. Abramovich, T.C. Bailey, and T. Sapatinas, “Wavelet analysis and its statistical applications”, Journal of the Royal Statistical Society: Series D (The Statistician) 49 (1), 1–29 (2000).
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- [10] T. Ogden, Essential wavelets for statistical applications and data analysis, Springer Science & Business Media, 2012.
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- [19] L. Prasad and S. Iyengar, Wavelet analysis with applications to image processing, CRC Press, 1997.
- [20] P. Meerwald and A. Uhl, “Survey of wavelet-domain watermarking algorithms”, in Photonics West 2001-Electronic Imaging) 505–516, International Society for Optics and Photonics, 2001.
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- [22] Y.Yang, D.S. Park, S. Huang, andN. Rao, “Medical image fusion via an effective wavelet-based approach”, EURASIP Journal on Advances in Signal Processing, 2010 (1), 1 (2010).
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- [24] S.A. Majeed, H. Husain, and S.A. Samad, “Phase autocorrelation bark wavelet transform (pacwt) features for robust speech recognition”, Archives of Acoustics 40 (1), 25–31 (2015).
- [25] D.A. Keim and M. Heczko, Wavelets and their applications in databases, Bibliothek der Universität Konstanz, 2001.
- [26] T. Li, Q. Li, S. Zhu, and M. Ogihara, “A survey on wavelet applications in data mining”, ACM SIGKDD Explorations Newsletter 4 (2), 49–68 (2002).
- [27] P. Chaovalit, A. Gangopadhyay, G. Karabatis, and Z. Chen, “Discrete wavelet transform-based time series analysis and mining”, ACM Computing Surveys (CSUR) 43 (2), 6 (2011).
- [28] S. Russell and V. Yoon, “Applications of wavelet data reduction in a recommender system”, Expert Systems with Applications 34 (4), 2316–2325 (2008).
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- [30] D. Devaurs, F. De Marchi, and M. S. Hacid, “Caractérisation des transitions temporisées dans les logs de conversation de services web”, Revue des Nouvelles Technologies de l’Information E (9), 45–56 (2007).
- [31] B. Serrour, D. P. Gasparotto, H. Kheddouci, and B. Benatallah, “Message correlation and business protocol discovery in service interaction logs”, in Advanced information systems engineering, 405–419, Springer, 2008.
- [32] K. Musaraj, T. Yoshida, F. Daniel, M.-S. Hacid, F. Casati, and B. Benatallah, “Message correlation and web service protocol mining from inaccurate logs”, in Web Services (ICWS), 2010 IEEE International Conference on, 259–266, IEEE, 2010.
- [33] W.M. Van der Aalst, B.F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A.J. Weijters, “Workflow mining: A survey of issues and approaches”, Data & Knowledge Engineering 47 (2), 237–267 (2003).
- [34] K. Musaraj, Extraction automatique de protocoles de communication pour la composition de services Web. PhD thesis, Lyon University, 2010.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9ced5c63-acb5-4ccf-877b-43359a943ae3