Abstract
Enterprise architecture provides a visualisation tool for stakeholder to manage and improve the current organization strategy to achieve its objectives. However, building an enterprise architecture is a time-consuming and often highly complex task. It involves data collection and analysis in several levels of granularity, from the physical nodes to the business execution. Existing solutions does not provide techniques to learn the relationship between the levels of granularity. In this paper, we proposed a method to correlate the business and application layers in ArchiMate notation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zachman, J.A.: Enterprise architecture: the issue of the century. Database Program. Des. 10(3), 44–53 (1997)
Lankhorst, M. (ed.): Enterprise Architecture at Work: Modelling, Communication, and Analysis. Springer, Heidelberg (2005)
Kaisler, S., Armour, F., Valivullah, M.: Enterprise architecting: critical problems. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, HICSS 2005, p. 224b, January 2005
Farwick, M., Breu, R., Hauder, M., Roth, S., Matthes, F.: Enterprise architecture documentation: empirical analysis of information sources for automation. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 3868–3877, January 2013
Aier, S., Buckl, S., Franke, U., Gleichauf, B., Johnson, P., Nrman, P., Schweda, C.M., Ullberg, J., Gallen, C.-S., Mnchen, T.U.: A survival analysis of application life spans based on enterprise architecture models. In: 3rd International Workshop on Enterprise Modelling and Information Systems Architectures, pp. 141–154 (2009)
Lankhorst, M.: ArchiMate language primer, Telematica institute (2004)
van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16, 1128–1142 (2004)
Fdhila, W., Rinderle-Ma, S., Indiono, C.: Memetic algorithms for mining change logs in process choreographies. In: Franch, X., Ghose, A.K., Lewis, G.A., Bhiri, S. (eds.) ICSOC 2014. LNCS, vol. 8831, pp. 47–62. Springer, Heidelberg (2014)
Wang, J., Han, J.: Bide: efficient mining of frequent closed sequences. In: Proceedings of 20th International Conference on Data Engineering, pp. 79–90, March 2004
Gomariz, A., Campos, M., Marin, R., Goethals, B.: ClaSP: an efficient algorithm for mining frequent closed sequences. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 50–61. Springer, Heidelberg (2013)
Yan, X., Han, J., Afshar, R.: Clospan: mining closed sequential patterns in large datasets. In: SDM, pp. 166–177 (2003)
Holm, H., Buschle, M., Lagerstrm, R., Ekstedt, M.: Automatic data collection for enterprise architecture models. Softw. Syst. Model. 13(2), 825–841 (2014)
Buschle, M., Holm, H., Sommestad, T., Ekstedt, M., Shahzad, K.: A tool for automatic enterprise architecture modeling. In: Nurcan, S. (ed.) CAiSE Forum 2011. LNBIP, vol. 107, pp. 1–15. Springer, Heidelberg (2012)
Wen, L., Wang, J., van der Aalst, W.M.P., Huang, B., Sun, J.: A novel approach for process mining based on event types. J. Intell. Inf. Syst. 32, 163–190 (2009)
Lankhorst, M., Proper, H., Jonkers, H.: The anatomy of the ArchiMate language. Int. J. Inf. Syst. Model. Des. 1(1), 1–32 (2010)
Wierda, G.: ArchiMate 2.0 understanding the basics, White paper, The Open Group, February 2013
Jonkers, H., Band, I., Quartel, D.: The archisurance case study, White paper, The Open Group, Spring (2012)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16, 1424–1440 (2004)
Zhang, M., Kao, B., Yip, C.-L., Cheung, D.: A GSP-based efficient algorithm for mining frequent sequences. In: Proceedings of IC-AI, pp. 497–503 (2001)
Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)
Viger, P.F., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.-W., Tseng, V.S.: SPMF: a java open-source pattern mining library. J. Mach. Learn. Res. 15, 3389–3393 (2014)
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W.E., Weijters, A.J.M.M.T., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Saraswati, A., Chang, CF., Ghose, A., Dam, H.K. (2015). Learning Relationships Between the Business Layer and the Application Layer in ArchiMate Models. In: Johannesson, P., Lee, M., Liddle, S., Opdahl, A., Pastor López, Ó. (eds) Conceptual Modeling. ER 2015. Lecture Notes in Computer Science(), vol 9381. Springer, Cham. https://doi.org/10.1007/978-3-319-25264-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-25264-3_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25263-6
Online ISBN: 978-3-319-25264-3
eBook Packages: Computer ScienceComputer Science (R0)