Skip to main content
SpringerLink
Log in

Literature review on Intention Mining-oriented Process Mining in information system

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Process Mining focused only on the activity-oriented process and neglected the users’ behaviors behind the activities, which led to overlooking the reality that they proposed to create. Recognizing the users’ underlying intentions can improve the guidance and offer better recommendations. As a result, an area of study known as Intention Mining has been merged. It aims at discovering the users’ behaviors using an event log. The intention is frequently used in different computer science research fields, including requirements definition, business process, and method engineering for context adaption. This paper reviews Intention-Oriented Process Mining based on event logs in the information systems engineering field. The objective is to identify the different models, methodologies, and algorithms proposed, the tools used, and the different challenges in these fields based on the four steps of review for the selection process, which start with the identification, followed by the screening, the eligibility, and the inclusion. For the first time, we are focused on Process Mining and intention mining based on log files and their relationship to get an idea about the area of intention mining. This paper reviews academic papers that are published in peer-reviewed venues from 2013 to 2022. These papers were examined through six main investigate questions and a systematic review. Also, we detailed the existing approaches in the Intention Mining area and present our comparative study. The results of the existing approaches indicate that Intention Mining shows a meaningful trace of research and creates existing opportunities for real technical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abb L, Bormann C, van der Aa H, Rehse JR (2022) Trace clustering for user behavior mining

  • Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the eleventh international conference on data engineering. IEEE, pp 3–14

  • Aksu Ü, Schunselaar DM, Reijers HA (2016) A cross-organizational process mining framework for obtaining insights from software products: accurate comparison challenges. In: 2016 IEEE 18th conference on business informatics (CBI), vol 1. IEEE, pp 153–162

  • Andrews R, Suriadi S, Ouyang C, Poppe E (2018) Towards event log querying for data quality. In: OTM confederated international conferences “On the Move to Meaningful Internet Systems”. Springer, pp 116–134

  • Asar S, Jalalpour S, Ayoubi F, Rahmani M, Rezaeian M (2016) Prisma; preferred reporting items for systematic reviews and meta-analyses. J Rafsanjan Univ Med Sci 15(1):68–80

    Google Scholar 

  • Baas J, Schotten M, Plume A, Côté G, Karimi R (2020) Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quant Sci Stud 1(1):377–386

    Google Scholar 

  • Baeza-Yates R, Calderón-Benavides L, González-Caro C (2006) The intention behind web queries. In: International symposium on string processing and information retrieval. Springer, pp 98–109

  • Baum LE, Eagon JA (1967) An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bull Am Math Soc 73(3):360–363

    MathSciNet  MATH  Google Scholar 

  • Bogarín A, Cerezo R, Romero C (2018) A survey on educational process mining. Wiley Interdiscip Rev 8(1):1230

    Google Scholar 

  • Buijs JC, Dongen BFv, van Der Aalst WM (2012) On the role of fitness, precision, generalization and simplicity in process discovery. In: OTM confederated international conferences “On the Move to Meaningful Internet Systems”. Springer, pp 305–322

  • Burattin A (2013) Applicability of process mining techniques in business environments

  • Chen Z, Lin F, Liu H, Liu Y, Ma W-Y, Wenyin L (2002) User intention modeling in web applications using data mining. World Wide Web 5(3):181–191

    Google Scholar 

  • Dakic D, Stefanovic D, Cosic I, Lolic T, Medojevic M (2018) Business process mining application: a literature review. Ann DAAAM Proc 29

  • Dardenne A, Van Lamsweerde A, Fickas S (1993) Goal-directed requirements acquisition. Sci Comput. Program 20(1–2):3–50

    MATH  Google Scholar 

  • Date SS (2020) A comprehensive review on intents, intention mining and intention classification. Int J Sci Res 9(11):16–20

    Google Scholar 

  • De Leoni M, Suriadi S, Ter Hofstede AH, van der Aalst WM (2016) Turning event logs into process movies: animating what has really happened. Softw Syst Model 15(3):707–732

    Google Scholar 

  • Delgado López-Cózar E, Orduña-Malea E, Martín-Martín A (2019) Google scholar as a data source for research assessment. Springer handbook of science and technology indicators, pp 95–127

  • Deneckère R, Kornyshova E (2010) Process line configuration: an indicator-based guidance of the intentional model map. In: Enterprise, business-process and information systems modeling. Springer, pp 327–339

  • Deneckere R, Hug C, Khodabandelou G, Salinesi C (2014) Intentional process mining: discovering and modeling the goals behind processes using supervised learning. Int J Inf Syst Model Des 5(4):22–47

    Google Scholar 

  • Deneckère R, Hug C, Jaffal A, Pinheiro MK, Le Grand B, Mazo R, Rychkova I (2016) Context management and intention mining for adaptive systems in mobile environments: from business process management to video games?

  • Deshmukh AM (2020) Comparison of hidden markov model and recurrent neural network in automatic speech recognition. Eur J Eng Technol Res 5(8):958–965

    Google Scholar 

  • Di Sorbo A, Panichella S, Visaggio CA, Di Penta M, Canfora G, Gall HC (2015) Development emails content analyzer: intention mining in developer discussions (t). In: 2015 30th IEEE/ACM international conference on automated software engineering (ASE). IEEE, pp 12–23

  • Diaz O, Pérez M (2022) Strategy mining for inferring business information system user intentions. Appl Sci 12(12):5949

    Google Scholar 

  • Diaz OE, Perez MG, Lascano JE (2019) Literature review about intention mining in information systems. J Comput Inf Syst 1–10

  • Díaz-Rodriguez OE, Hernández MGP (2020) Quality event log to intention mining: A study case. In: 2020 international conference on computer science, engineering and applications (ICCSEA). IEEE, pp 1–6

  • Díaz-Rodríguez OE, Pérez M (2018) Log design for storing seismic event characteristics using process, text, and opinion mining techniques. In: 2018 international conference on eDemocracy & eGovernment (ICEDEG). IEEE, pp 281–285

  • Elali R (2021) An intention mining approach using ontology for contextual recommendations. In: CAiSE (Doctoral Consortium), pp 69–78

  • Epure EV, Hug C, Deneckère R, Brinkkemper S (2013) Intention-mining: A solution to process participant support in process aware information systems. Department of Information and Computing Sciences Utrecht University, Utrecht (the Netherlands)

    Google Scholar 

  • Epure EV, Hug C, Deneckere R, Brinkkemper S (2014) What shall i do next? In: International conference on advanced information systems engineering. Springer, pp 473–487

  • Epure EV, Compagno D, Salinesi C, Deneckere R, Bajec M, Žitnik S (2018) Process models of interrelated speech intentions from online health-related conversations. Artif Intell Med 91:23–38

    Google Scholar 

  • Ghasemi M, Amyot D (2020) From event logs to goals: a systematic literature review of goal-oriented process mining. Requir Eng 25(1):67–93

    Google Scholar 

  • Goldstein A, Johanndeiter T, Frank U (2019) Business process runtime models: towards bridging the gap between design, enactment, and evaluation of business processes. IseB 17(1):27–64

    Google Scholar 

  • Grant MJ, Booth A (2009) A typology of reviews: an analysis of 14 review types and associated methodologies. Health Inf Lib J 26(2):91–108

    Google Scholar 

  • Graves A (2012) Long short-term memory. Supervised sequence labelling with recurrent neural networks. In: Studies in computational intelligence, vol 385. Springer

  • Greco G, Guzzo A, Pontieri L, Sacca D (2006) Discovering expressive process models by clustering log traces. IEEE Trans Knowl Data Eng 18(8):1010–1027

    Google Scholar 

  • Gupta EP (2014) Process mining a comparative study. Int J Adv Res Comput Commun Eng 3(11):5

    Google Scholar 

  • Habib A, Saddozai FK, Sattar A, Khan A, Hameed IA, Kundi FM (2018) User intention mining in business reviews: a review. In: 2018 5th international conference on behavioral, economic, and socio-cultural computing (BESC). IEEE, pp 243–249

  • Hajer B, Arwa B, Lobna H, Khaled G (2020) Intention mining data preprocessing based on multi-agents system. Procedia Comput Sci 176:888–897

    Google Scholar 

  • Hashemi RR, Bahrami A, LePlant J, Thurber K (2008) Discovery of intent through the analysis of visited sites

  • Hompes B, Buijs J, Van der Aalst W, Dixit P, Buurman J (2015) Discovering deviating cases and process variants using trace clustering. In: Proceedings of the 27th Benelux conference on artificial intelligence (BNAIC), November, pp 5–6

  • Horkoff J, Aydemir FB, Cardoso E, Li T, Maté A, Paja E, Salnitri M, Piras L, Mylopoulos J, Giorgini P (2019) Goal-oriented requirements engineering: an extended systematic mapping study. Requir Eng 24(2):133–160

    Google Scholar 

  • Huang Q, Xia X, Lo D, Murphy GC (2018) Automating intention mining. IEEE Trans Softw Eng

  • Jaakkola H et al (1994) Modeling the requirements engineering process. Inf Model Knowl Bases V 5:85

    Google Scholar 

  • Jans MJ, Alles M, Vasarhelyi MA (2010) Process mining of event logs in auditing: opportunities and challenges. Available at SSRN 1578912

  • Jethava V, Calderón-Benavides L, Baeza-Yates R, Bhattacharyya C, Dubhashi D (2011) Scalable multi-dimensional user intent identification using tree structured distributions. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 395–404

  • Kalenkova AA, van der Aalst WM, Lomazova IA, Rubin VA (2017) Process mining using bpmn: relating event logs and process models. Softw Syst Model 16(4):1019–1048

    Google Scholar 

  • Khalifa Y, Mandic D, Sejdić E (2021) A review of hidden markov models and recurrent neural networks for event detection and localization in biomedical signals. Inf Fusion 69:52–72

    Google Scholar 

  • Khodabandelou G (2013) Contextual recommendations using intention mining on process traces: doctoral consortium paper. In: IEEE 7th international conference on research challenges in information science (RCIS). IEEE, pp 1–6

  • Khodabandelou G, Hug C, Deneckere R, Salinesi C (2013a) Process mining versus intention mining. In: Enterprise, business-process and information systems modeling. Springer, pp 466–480

  • Khodabandelou G, Hug C, Deneckere R, Salinesi C (2013b) Supervised intentional process models discovery using hidden markov models. In: IEEE 7th International conference on research challenges in information science (RCIS). IEEE, pp 1–11

  • Khodabandelou G, Hug C, Deneckere R, Salinesi C, Bajec M, Kornyshova E, Janković M (2013c) Cots products to trace method enactment: review and selection. In: 21th European conference on information systems

  • Khodabandelou G, Hug C, Deneckere R, Salinesi C (2014a) Supervised vs. unsupervised learning for intentional process model discovery. In: Enterprise, business-process and information systems modeling. Springer, pp 215–229

  • Khodabandelou G, Hug C, Deneckère R, Salinesi C (2014b) Unsupervised discovery of intentional process models from event logs. In: Proceedings of the 11th working conference on mining software repositories. ACM, pp 282–291

  • Khodabandelou G, Hug C, Salinesi C (2014c) A novel approach to process mining: Intentional process models discovery. In: 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS). IEEE, pp 1–12

  • Khodabandelou G, Hug C, Salinesi C (2015) Mining users’ intents from logs. Int J Inf Syst Model Des 6(2):43–71

    Google Scholar 

  • Leemans SJ, Fahland D, van der Aalst WM (2015) Scalable process discovery with guarantees. In: Enterprise, business-process and information systems modeling. Springer, pp 85–101

  • Lu X, Fahland D, van der Aalst WM (2016) Interactively exploring logs and mining models with clustering, filtering, and relabeling. In: BPM (Demos), pp 44–49

  • Martín-Martín A, Thelwall M, Orduna-Malea E, Delgado López-Cózar E (2021) Google scholar, microsoft academic, scopus, dimensions, web of science, and opencitations’ coci: a multidisciplinary comparison of coverage via citations. Scientometrics 126(1):871–906

    Google Scholar 

  • Mirbel I, Ralyté J (2006) Situational method engineering: combining assembly-based and roadmap-driven approaches. Requir Eng 11(1):58–78

    Google Scholar 

  • Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group (2009) Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Ann Intern Med 151(4):264–269

    Google Scholar 

  • Moher D, Liberati A, Tetzlaff J, Altman DG et al (2010) Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Int J Surg 8(5):336–341

    Google Scholar 

  • Najar S, Kirsch-Pinheiro M, Souveyet C (2011) Towards semantic modeling of intentional pervasive information systems. In: Proceedings of the 6th international workshop on enhanced web service technologies. ACM, pp 30–34

  • Nikitina K (2020) Educational game analysis using intention and process mining. In: CEUR workshop proceedings, pp 117–125

  • Omair B, Emam A (2015) Towards big business process mining. ALLDATA 2015:42

    Google Scholar 

  • Outmazgin N, Soffer P (2013) Business process workarounds: what can and cannot be detected by process mining. In: Enterprise, business-process and information systems modeling. Springer, pp 48–62

  • Pande DN, Roy KR, Uparkar SS (2019) Intention mining for introspective behavior modelling in business intelligence

  • Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2007) A design science research methodology for information systems research. J Manag Inf Syst 24(3):45–77

    Google Scholar 

  • Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Google Scholar 

  • Ralyté J, Deneckère R, Rolland C (2003) Towards a generic model for situational method engineering. In: International conference on advanced information systems engineering. Springer, pp 95–110

  • Rashid A, Farooq MS, Abid A, Umer T, Bashir AK, Zikria YB (2021) Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges. Complex Intell Syst 1–27

  • R’bigui H, Cho C (2017) The state-of-the-art of business process mining challenges. Int J Bus Process Integr Manag 8(4):285–303

    Google Scholar 

  • Rolland C, Prakash N, Benjamen A (1999) A multi-model view of process modelling. Requir Eng 4(4):169–187

    Google Scholar 

  • Rolland C, Kirsch-Pinheiro M, Souveyet C (2010) An intentional approach to service engineering. IEEE Trans Serv Comput 3(4):292–305

    Google Scholar 

  • Salinesi C, Rolland C (2003) Fitting business models to system functionality exploring the fitness relationship. In: International conference on advanced information systems engineering. Springer, pp 647–664

  • Sarno R, Dewandono RD, Ahmad T, Naufal MF, Sinaga F (2015) Hybrid association rule learning and process mining for fraud detection. IAENG Int J Comput Sci 42(2):59

    Google Scholar 

  • Sethi TS, Kantardzic M (2017) On the reliable detection of concept drift from streaming unlabeled data. Expert Syst Appl 82:77–99

    Google Scholar 

  • Shershakov SA (2015) Vtmine framework as applied to process mining modeling. Int J Comput Commun Eng 4(3):166

    Google Scholar 

  • Snyder H (2019) Literature review as a research methodology: an overview and guidelines. J Bus Res 104:333–339

    Google Scholar 

  • Strohmaier M, Kröll M (2012) Acquiring knowledge about human goals from search query logs. Inf Process Manag 48(1):63–82

    Google Scholar 

  • Sungkono KR, Sarno R (2017a) Chmm for discovering intentional process model from event logs by considering sequence of activities. In: 2017 4th international conference on electrical engineering, computer science and informatics (EECSI). IEEE, pp 1–6

  • Sungkono KR, Sarno R (2017b) Patterns of fraud detection using coupled hidden Markov model. In: 2017 3rd International conference on science in information technology (ICSITech). IEEE, pp 235–240

  • Sureka A (2015) Intention-oriented process model discovery from incident management event logs. arXiv preprint arXiv:1507.01062

  • Suriadi S, Andrews R, ter Hofstede AH, Wynn MT (2017) Event log imperfection patterns for process mining: towards a systematic approach to cleaning event logs. Inf Syst 64:132–150

    Google Scholar 

  • Van Der Aalst W (2012) Process mining. Commun ACM 55(8):76–83

    Google Scholar 

  • Van der Aalst WM, Weijters AJ (2004) Process mining: a research agenda. Elsevier, Amsterdam

    Google Scholar 

  • Van der Aalst W, Weijters T, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Knowl Data Eng 16(9):1128–1142

    Google Scholar 

  • Van Der Aalst W, Adriansyah A, De Medeiros AKA, Arcieri F, Baier T, Blickle T, Bose JC, Van Den Brand P, Brandtjen R, Buijs J et al (2011) Process mining manifesto. In: International conference on business process management. Springer, pp 169–194

  • Van Dongen BF, de Medeiros AKA, Verbeek H, Weijters A, van Der Aalst WM (2005) The prom framework: a new era in process mining tool support. In: International conference on application and theory of petri nets. Springer, pp 444–454

  • Van Lamsweerde A (2001) Goal-oriented requirements engineering: a guided tour. In: Proceedings fifth IEEE international symposium on requirements engineering. IEEE, pp 249–262

  • van Zelst SJ, van Dongen BF, van der Aalst WM (2016) Avoiding over-fitting in ilp-based process discovery. In: International conference on business process management. Springer, pp 163–171

  • Washio T, Motoda H (2003) State of the art of graph-based data mining. ACM SIGKDD Explor Newsl 5(1):59–68

    Google Scholar 

  • Yadav P, Kalyani K, Mahamuni R (2015) User intention mining: a non-intrusive approach to track user activities for web application. In: International conference in swarm intelligence. Springer, pp 147–154

  • Yang S, Ni W, Dong X, Chen S, Farneth RA, Sarcevic A, Marsic I, Burd RS (2018) Intention mining in medical process: a case study in trauma resuscitation. In: 2018 IEEE international conference on healthcare informatics (ICHI). IEEE, pp 36–43

  • Yu E (2011) Modeling strategic relationships for process reengineering. Soc Model Requir Eng 11(2011):66–87

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IT Department, National School of Computer Science, Manouba, Tunisia

    Hajer Bouricha

  2. University of Jeddah, Jeddah, Saudi Arabia

    Lobna Hsairi

  3. University of Tunis, Tunis, Tunisia

    Khaled Ghédira

Authors
  1. Hajer Bouricha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lobna Hsairi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khaled Ghédira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hajer Bouricha.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bouricha, H., Hsairi, L. & Ghédira, K. Literature review on Intention Mining-oriented Process Mining in information system. Artif Intell Rev 56, 13841–13872 (2023). https://doi.org/10.1007/s10462-023-10490-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-023-10490-8

Keywords

Search

Navigation