Abstract
Search processes are highly individual business processes reflecting the search behavior of users in search systems. The analysis of search processes is a promising instrument in order to improve customer journeys and experience. The quality of the analysis results depends on the underlying data, i.e., logs and the search process models. However, it is unclear what quality means with respect to search process logs and models. This paper defines search process models and revisits existing process model and log quality metrics. A metric for search process models is proposed that assesses their complexity and degree of common behavior. In order to compare metrics for search process models different logs and search processes are generated by using ontologies for user guidance during search process execution and for post processing of the logs. Based on an experiment with users in the tourism setting different logs and models are created and compared.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Bailey, P., White, R.W., Liu, H., Kumaran, G.: Mining historic query trails to label long and rare search engine queries. TWEB 4(4), 15:1–15:27 (2010)
Celonis: Process mining story postfinance: Optimizing the customer journey in banking (2018). https://youtu.be/qJ2NcdZSxA4
Dixit, P., Buijs, J.C., van der Aalst, W.M., Hompes, B., Buurman, H.: Enhancing process mining results using domain knowledge. In: SIMPDA, pp. 79–94 (2015)
Dunkl, R.: Data improvement to enable process mining on integrated non-log data sources. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2013. LNCS, vol. 8111, pp. 491–498. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-53856-8_62
Fahland, D., van der Aalst, W.M.P.: Simplifying discovered process models in a controlled manner. Inf. Syst. 38(4), 585–605 (2013)
Fernandez, F.M.H., Ponnusamy, R.: Data preprocessing and cleansing in web log on ontology for enhanced decision making. Indian J. Sci. Technol. 9(10) (2016). http://www.indjst.org/index.php/indjst/article/view/88899
Fürber, C.: Data Quality Management with Semantic Technologies. Springer, Wiesbaden (2015). https://doi.org/10.1007/978-3-658-12225-6
Gulla, J.A.: Applied Semantic Web Technologies. Auerbach Publications, Boca Raton (2011)
Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_24
Ingvaldsen, J.E., Gulla, J.A.: Industrial application of semantic process mining. Enterp. Inf. Syst. 6(2), 139–163 (2012)
Kaes, G., Rinderle-Ma, S.: Generating data from highly flexible and individual process settings through a game-based experimentation service. In: Datenbanksysteme für Business, Technologie und Web, pp. 331–350 (2017)
Koschmider, A., Oberweis, A.: Ontology based business process description. In: EMOI-INTEROP, pp. 321–333 (2005)
Kuhlthau, C.C.: Inside the search process: information seeking from the user’s perspective. Am. Soc. Inf. Sci. 42(5), 361–371 (1991)
de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016)
Lux, M., Rinderle-Ma, S.: Problems and challenges when implementing a best practice approach for process mining in a tourist information system. In: BPM 2017 Industry Track. CEUR, vol. 1985, pp. 1–12 (2017)
Ly, L.T., Indiono, C., Mangler, J., Rinderle-Ma, S.: Data transformation and semantic log purging for process mining. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 238–253. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31095-9_16
Maechler, N., Neher, K., Park, R.: From touchpoints to journeys: seeing the world as customers do, March 2016. http://bit.ly/2AAzjcJ
Medeiros, A.K.A., et al.: An outlook on semantic business process mining and monitoring. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2007. LNCS, vol. 4806, pp. 1244–1255. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76890-6_52
Mendling, J., Strembeck, M.: Influence factors of understanding business process models. In: Abramowicz, W., Fensel, D. (eds.) BIS 2008. LNBIP, vol. 7, pp. 142–153. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79396-0_13
Mertens, W., Pugliese, A., Recker, J.: Quantitative Data Analysis: A Companion for Accounting and Information Systems Research. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42700-3
Moore, H.: Cours d’économie politique. Ann. Am. Acad. Polit. Soc. Sci. 9(3), 128–131 (1897). http://bit.ly/2FGyANa
Pedrinaci, C., Domingue, J., Alves de Medeiros, A.K.: A core ontology for business process analysis. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 49–64. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68234-9_7
Petnga, L., Austin, M.: An ontological framework for knowledge modeling and decision support in cyber-physical systems. Adv. Eng. Inform. 30(1), 77–94 (2016)
Pmig, Y., Yongil, L.: Customer Journey Mining (2018)
Richardson, A.: Using customer journey maps to improve customer experience. Harvard Bus. Rev. 15(1), 2–5 (2010)
Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)
Silverstein, C., Marais, H., Henzinger, M., Moricz, M.: Analysis of a very large web search engine query log. In: ACm SIGIR Forum, vol. 33, pp. 6–12. ACM (1999)
Thomas, O., Fellmann, M.: Semantic process modeling - design and implementation of an ontology-based representation of business processes. Bus. Inf. Syst. Eng. 1(6), 438–451 (2009)
Vanderfeesten, I.T.P., Reijers, H.A., van der Aalst, W.M.P.: Evaluating workflow process designs using cohesion and coupling metrics. Comput. Ind. 59(5), 420–437 (2008)
Vanderfeesten, I., Reijers, H.A., Mendling, J., van der Aalst, W.M.P., Cardoso, J.: On a quest for good process models: the cross-connectivity metric. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 480–494. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69534-9_36
W3C: SKOS simple knowledge organization system reference, August 2009. https://www.w3.org/TR/2009/REC-skos-reference-20090818/
Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: Computational Intelligence and Data Mining, pp. 310–317 (2011)
Wieringa, R.: Design Science Methodology for Information Systems and Software Engineering. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43839-8
WST: Supplementary material (2018). http://gruppe.wst.univie.ac.at/projects/CustPro/
Acknowledgment
This work has been partly conducted within the CustPro project funded by the Vienna Business Agency.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Lux, M., Rinderle-Ma, S., Preda, A. (2018). Assessing the Quality of Search Process Models. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management. BPM 2018. Lecture Notes in Computer Science(), vol 11080. Springer, Cham. https://doi.org/10.1007/978-3-319-98648-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-98648-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98647-0
Online ISBN: 978-3-319-98648-7
eBook Packages: Computer ScienceComputer Science (R0)