Facilitating process analysis through visualising process history: experiences with a Dutch municipality

Nowadays vast quantities of data are stored as a result of the operation of software systems and devices. The analysis of this data can provide valuable insights. In the field of Business Process Management, event logs may provide valuable information for business process improvement. This is the realm of process mining, an area which has provided many analysis techniques over the past decade. Despite the abundance of process mining techniques, it remains a challenge to provide results that are understandable by domain experts. Discovered process models are often perceived as abstract and static. Conformance checking techniques provide detailed results that are only understandable for process analysts. Therefore, we propose an approach to dynamically visualize event data on intuitive 'maps'. States of the process are visualized on a collection of maps thus resulting in sequences of 'photographs' of the process under investigation. By replaying the event log using such visualizations we can create a collection of 'process movies'. Our visualisation approach has been implemented in ProM and allows for any type of 'map' as long as activity instances can be associated to map coordinates. Moreover, the approach has been evaluated in collaboration with a Dutch municipality

A Literature Review on Business Process Management

Business Process (BP) is a set of coordinated tasks that define how to achieve organizational goals. It emerges as an efficient tool, whose main goal is supporting the design, administration, setup, disclosure and analysis of business processes, and organizations use it to identify opportunities to reduce costs, increase service or product quality, etc. The goal of BPM is to manage business processes. Organizations wish to manage perfectly these processes instead of fixing the non-ideal process setups or outcomes in a reactive manner. At present, variability management in the business processes domain is considered as a key of reuse. Process mining offers a set of techniques that retrieves information from event logs and gives companies a better understanding of their processes. Process mining has gained significant attention in both research and industry as a range of data mining tools has emerged. In this study, we will provide a systematic literature review from 2017 to 2021; we will use Kitchenham method to conduct this SLR. Data source as IEEE, ACM, Springer and ScienceDirect are used to obtain literature. We had, as a result, 51 papers from 3079 papers to complete this paper. This SLR had for objective to see the research trend on the topics of business process management, improvement, modeling and approaches using data mining

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

[EN] The study presents some results of customer paths¿ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men¿s bathroom or women¿s bathroom. Since the study has a comprehensive scope, we focused on male and female customers¿ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.Dogan, O.; Bayo-Monton, JL.; Fernández Llatas, C.; Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors. 19(3):1-20. https://doi.org/10.3390/s19030557S120193Oosterlinck, D., Benoit, D. F., Baecke, P., & Van de Weghe, N. (2017). Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits. Applied Geography, 78, 55-65. doi:10.1016/j.apgeog.2016.11.005Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B., & Drap, P. (2016). Tracking multiple persons under partial and global occlusions: Application to customers’ behavior analysis. Pattern Recognition Letters, 81, 11-20. doi:10.1016/j.patrec.2016.04.011Wu, Y., Wang, H.-C., Chang, L.-C., & Chou, S.-C. (2015). Customer’s Flow Analysis in Physical Retail Store. Procedia Manufacturing, 3, 3506-3513. doi:10.1016/j.promfg.2015.07.672Dogan, O., & Öztaysi, B. (2018). In-store behavioral analytics technology selection using fuzzy decision making. Journal of Enterprise Information Management, 31(4), 612-630. doi:10.1108/jeim-02-2018-0035Hwang, I., & Jang, Y. J. (2017). Process Mining to Discover Shoppers’ Pathways at a Fashion Retail Store Using a WiFi-Base Indoor Positioning System. IEEE Transactions on Automation Science and Engineering, 14(4), 1786-1792. doi:10.1109/tase.2017.2692961Abedi, N., Bhaskar, A., Chung, E., & Miska, M. (2015). Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses. Transportation Research Part C: Emerging Technologies, 60, 124-141. doi:10.1016/j.trc.2015.08.010Mou, S., Robb, D. J., & DeHoratius, N. (2018). Retail store operations: Literature review and research directions. European Journal of Operational Research, 265(2), 399-422. doi:10.1016/j.ejor.2017.07.003Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., & Weijters, A. J. M. M. (2003). Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering, 47(2), 237-267. doi:10.1016/s0169-023x(03)00066-1Ou-Yang, C., & Winarjo, H. (2011). Petri-net integration – An approach to support multi-agent process mining. Expert Systems with Applications, 38(4), 4039-4051. doi:10.1016/j.eswa.2010.09.066Partington, A., Wynn, M., Suriadi, S., Ouyang, C., & Karnon, J. (2015). Process Mining for Clinical Processes. ACM Transactions on Management Information Systems, 5(4), 1-18. doi:10.1145/2629446Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., … Song, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88, 34-43. doi:10.1016/j.ijmedinf.2015.12.018Funkner, A. A., Yakovlev, A. N., & Kovalchuk, S. V. (2017). Towards evolutionary discovery of typical clinical pathways in electronic health records. Procedia Computer Science, 119, 234-244. doi:10.1016/j.procs.2017.11.181Jans, M., Alles, M., & Vasarhelyi, M. (2013). The case for process mining in auditing: Sources of value added and areas of application. International Journal of Accounting Information Systems, 14(1), 1-20. doi:10.1016/j.accinf.2012.06.015Yoshimura, Y., Sobolevsky, S., Ratti, C., Girardin, F., Carrascal, J. P., Blat, J., & Sinatra, R. (2014). An Analysis of Visitors’ Behavior in the Louvre Museum: A Study Using Bluetooth Data. Environment and Planning B: Planning and Design, 41(6), 1113-1131. doi:10.1068/b130047pDe Leoni, M., van der Aalst, W. M. P., & Dees, M. (2016). A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems, 56, 235-257. doi:10.1016/j.is.2015.07.003Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Arroyo, R., Yebes, J. J., Bergasa, L. M., Daza, I. G., & Almazán, J. (2015). Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls. Expert Systems with Applications, 42(21), 7991-8005. doi:10.1016/j.eswa.2015.06.016Popa, M. C., Rothkrantz, L. J. M., Shan, C., Gritti, T., & Wiggers, P. (2013). Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information. Pattern Recognition Letters, 34(7), 809-819. doi:10.1016/j.patrec.2012.04.015Kang, L., & Hansen, M. (2017). Behavioral analysis of airline scheduled block time adjustment. Transportation Research Part E: Logistics and Transportation Review, 103, 56-68. doi:10.1016/j.tre.2017.04.004Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42(23), 9236-9251. doi:10.1016/j.eswa.2015.07.040Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Van der Aalst, W. M. P., Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Alves de Medeiros, A. K., Song, M., & Verbeek, H. M. W. (2007). Business process mining: An industrial application. Information Systems, 32(5), 713-732. doi:10.1016/j.is.2006.05.003M. Valle, A., A.P. Santos, E., & R. Loures, E. (2017). Applying process mining techniques in software process appraisals. Information and Software Technology, 87, 19-31. doi:10.1016/j.infsof.2017.01.004Juhaňák, L., Zounek, J., & Rohlíková, L. (2019). Using process mining to analyze students’ quiz-taking behavior patterns in a learning management system. Computers in Human Behavior, 92, 496-506. doi:10.1016/j.chb.2017.12.015Sedrakyan, G., De Weerdt, J., & Snoeck, M. (2016). Process-mining enabled feedback: «Tell me what I did wrong» vs. «tell me how to do it right». Computers in Human Behavior, 57, 352-376. doi:10.1016/j.chb.2015.12.040Schoor, C., & Bannert, M. (2012). Exploring regulatory processes during a computer-supported collaborative learning task using process mining. Computers in Human Behavior, 28(4), 1321-1331. doi:10.1016/j.chb.2012.02.016Werner, M., & Gehrke, N. (2015). Multilevel Process Mining for Financial Audits. IEEE Transactions on Services Computing, 8(6), 820-832. doi:10.1109/tsc.2015.2457907De Weerdt, J., Schupp, A., Vanderloock, A., & Baesens, B. (2013). Process Mining for the multi-faceted analysis of business processes—A case study in a financial services organization. Computers in Industry, 64(1), 57-67. doi:10.1016/j.compind.2012.09.010Herbert, L., Hansen, Z. N. L., Jacobsen, P., & Cunha, P. (2014). Evolutionary Optimization of Production Materials Workflow Processes. Procedia CIRP, 25, 53-60. doi:10.1016/j.procir.2014.10.010Yim, J., Jeong, S., Gwon, K., & Joo, J. (2010). Improvement of Kalman filters for WLAN based indoor tracking. Expert Systems with Applications, 37(1), 426-433. doi:10.1016/j.eswa.2009.05.047Delafontaine, M., Versichele, M., Neutens, T., & Van de Weghe, N. (2012). Analysing spatiotemporal sequences in Bluetooth tracking data. Applied Geography, 34, 659-668. doi:10.1016/j.apgeog.2012.04.003Frisby, J., Smith, V., Traub, S., & Patel, V. L. (2017). Contextual Computing : A Bluetooth based approach for tracking healthcare providers in the emergency room. Journal of Biomedical Informatics, 65, 97-104. doi:10.1016/j.jbi.2016.11.008Yoshimura, Y., Krebs, A., & Ratti, C. (2017). Noninvasive Bluetooth Monitoring of Visitors’ Length of Stay at the Louvre. IEEE Pervasive Computing, 16(2), 26-34. doi:10.1109/mprv.2017.33Cao, Q., Jones, D. R., & Sheng, H. (2014). Contained nomadic information environments: Technology, organization, and environment influences on adoption of hospital RFID patient tracking. Information & Management, 51(2), 225-239. doi:10.1016/j.im.2013.11.007Larson, J. S., Bradlow, E. T., & Fader, P. S. (2005). An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22(4), 395-414. doi:10.1016/j.ijresmar.2005.09.005Fernandez-Llatas, C., Martinez-Millana, A., Martinez-Romero, A., Benedi, J. M., & Traver, V. (2015). Diabetes care related process modelling using Process Mining techniques. Lessons learned in the application of Interactive Pattern Recognition: coping with the Spaghetti Effect. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2015.7318809Conca, T., Saint-Pierre, C., Herskovic, V., Sepúlveda, M., Capurro, D., Prieto, F., & Fernandez-Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. Journal of Medical Internet Research, 20(4), e127. doi:10.2196/jmir.8884De Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery, 14(2), 245-304. doi:10.1007/s10618-006-0061-7Heyer, L. J. (1999). Exploring Expression Data: Identification and Analysis of Coexpressed Genes. Genome Research, 9(11), 1106-1115. doi:10.1101/gr.9.11.1106Yang, W.-S., & Hwang, S.-Y. (2006). A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications, 31(1), 56-68. doi:10.1016/j.eswa.2005.09.00

Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining

[EN] In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes and help to answer these questions. However, ER experts require certain guidelines in order to carry out process mining effectively. This article proposes a number of solutions, including a classification of the frequently-posed questions about ER processes, a data reference model to guide the extraction of data from the information systems that support these processes and a question-driven methodology specific for ER. The applicability of the latter is illustrated by means of a case study of an ER service in Chile, in which ER experts were able to obtain a better understanding of how they were dealing with episodes related to specific pathologies, triage severity and patient discharge destinations.This project was partially funded by Fondecyt Grants 1150365 and 11130577 from the Chilean National Commission on Scientific and Technological Research (CONICYT), the Ph.D. Scholarship Program of CONICYT Chile (CONICYT-Doctorado Nacional/2014-63140180), the Ph.D. Scholarship Program of CONICIT Costa Rica and by Universidad de Costa Rica Professor Fellowships.Rojas, E.; Sepúlveda, M.; Munoz-Gama, J.; Capurro, D.; Traver Salcedo, V.; Fernández Llatas, C. (2017). Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining. Applied Sciences. 7(3):1-29. https://doi.org/10.3390/app7030302S12973Welch, S. J., Asplin, B. R., Stone-Griffith, S., Davidson, S. J., Augustine, J., & Schuur, J. (2011). Emergency Department Operational Metrics, Measures and Definitions: Results of the Second Performance Measures and Benchmarking Summit. Annals of Emergency Medicine, 58(1), 33-40. doi:10.1016/j.annemergmed.2010.08.040Jansen-Vullers, M., & Reijers, H. (2005). Business Process Redesign in Healthcare: Towards a Structured Approach. INFOR: Information Systems and Operational Research, 43(4), 321-339. doi:10.1080/03155986.2005.11732733Grol, R., & Grimshaw, J. (1999). Evidence-Based Implementation of Evidence-Based Medicine. The Joint Commission Journal on Quality Improvement, 25(10), 503-513. doi:10.1016/s1070-3241(16)30464-3Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Radnor, Z. J., Holweg, M., & Waring, J. (2012). Lean in healthcare: The unfilled promise? Social Science & Medicine, 74(3), 364-371. doi:10.1016/j.socscimed.2011.02.011Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Neumuth, T., Jannin, P., Schlomberg, J., Meixensberger, J., Wiedemann, P., & Burgert, O. (2010). Analysis of surgical intervention populations using generic surgical process models. International Journal of Computer Assisted Radiology and Surgery, 6(1), 59-71. doi:10.1007/s11548-010-0475-yFernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Partington, A., Wynn, M., Suriadi, S., Ouyang, C., & Karnon, J. (2015). Process Mining for Clinical Processes. ACM Transactions on Management Information Systems, 5(4), 1-18. doi:10.1145/2629446Basole, R. C., Braunstein, M. L., Kumar, V., Park, H., Kahng, M., Chau, D. H. (Polo), … Thompson, M. (2015). Understanding variations in pediatric asthma care processes in the emergency department using visual analytics. Journal of the American Medical Informatics Association, 22(2), 318-323. doi:10.1093/jamia/ocu016Suriadi, S., Andrews, R., ter Hofstede, A. H. M., & Wynn, M. T. (2017). Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs. Information Systems, 64, 132-150. doi:10.1016/j.is.2016.07.011De Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery, 14(2), 245-304. doi:10.1007/s10618-006-0061-7Aalst, W. M. P. van der. (2005). Business alignment: using process mining as a tool for Delta analysis and conformance testing. Requirements Engineering, 10(3), 198-211. doi:10.1007/s00766-005-0001-xVan der Aalst, W., Adriansyah, A., & van Dongen, B. (2012). Replaying history on process models for conformance checking and performance analysis. WIREs Data Mining and Knowledge Discovery, 2(2), 182-192. doi:10.1002/widm.1045Song, M., & van der Aalst, W. M. P. (2008). Towards comprehensive support for organizational mining. Decision Support Systems, 46(1), 300-317. doi:10.1016/j.dss.2008.07.00

An intelligent recommendation system framework for student relationship management

In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification