Conclusion and Future Works
The work presented in this thesis is motivated by the
omnipresence of event logs and the relevance of good processes models. An
increasing number of processes and systems are being monitored. For example,
any enterprise information system is recording business events, moreover also
professional and consumer systems are recording events.
Process discovery aims to reconstruct process models from
event logs. It is a collection of tools and techniques used to define, map and
analyze an organization's existing business processes. Along with business
process modeling, it is widely viewed as critical for successful Business
Process Management (BPM) initiatives, in fact, it is often an initial step in
BPM. The basic idea is to extract knowledge from event logs recorded by an
information system. Without these techniques it is difficult to obtain a
valuable information. So, process models are readily available and their
importance is increasing. Moreover, process models are used for all kinds of
analysis (e.g. simulation). The availability of event logs and the need for
high-quality process models has been the starting point of this thesis.
It has been noticed in many different works in literature that
process mining techniques can deliver valuable, factual insights into how
processes are being executed in real life. This makes them especially important
for analyzing flexible environments, where actors and even process owners are
often unaware of how exactly the process is structured. In such domains,
however, process mining algorithms tend to generate complex, unstructured
process models, which are hard to understand, and thus of limited value. One
reason for these problems is diversity, i.e. processes which can generate a set
of cases that are structured very differently. However, there are many process
mining algorithms with different theoretical foundations and aims, which leads
to different mining results. Existing techniques perform well on structured
processes, but still have problems discovering and visualizing less structured
ones. In reality, there is a lot of diversity leading to complex models that
are difficult to interpret.
75
Conclusion and Future Works
Trace clustering is one of very promising technique to best
mining results. Trace clustering approach aims at splitting the event log into
homogeneous subsets and for each subset a process model is created.
In this thesis, we have introduced an approach for traces
clustering in order get better mined models. We have presented a generic
methodology for trace clustering, which can effectively resolve
diversity-related problems, by dividing the log into smaller, more homogeneous
subsets of traces. We were inspired by the work of song et al. [32] who have
introduced the concept of trace profiles, which are a suitable means for
characterizing and comparing traces.
Unlike their work, Trace profile is composed only of binary
values. Traces are characterized by profiles (vectors) composed of n
items : Profile_V ectori = (a1,
a2, ..., an).
We proposed a new function for measuring the distance between
two cases and to calculate new clusters. As it is an "exclusive OR", we use
XOR to calculate the distance between two points (represented by their
profiles). It only returns a "true" value (1) if the two values are exclusive,
i.e. they are both different. So, applying XOR between two profiles
provides a serie of 1 for each two different items of the traces profiles. This
serves as a mean to calculate the difference degree of two profiles, and
consequently, to collect similar traces together.
To build new clusters, we use the logical AND, which
is an operator on two logical values that produces a value of true if and only
if both of its operands are true. We apply the logical AND on the
values of two traces profiles among all the traces in the same cluster to build
a new cluster center.
We have demonstrated the applicability of our approach with a
real process logs. We have implemented it with JAVA, experimented and the
obtained results were compared with an existing approach. The comparison showed
that we obtained improvement, especially, in the fitness metric.
We conclude this section, and therefore this thesis, with the
expectation that trace clustering, in the near future, will play an important
role in the improvement of the results of any process mining algorithm. As the
traces clustering techniques operate on event logs, using them will be very
benefit to construct and build more homogenous set of traces, and therefore,
more precise process models. So, traces clustering is a promising approach
which can be used to improve the results of any process mining algorithm.
Future work will focus on:
· Implementing our approach in the context of the ProM
framework,
· Finding a solution for loops.
76
Bibliography
[1] D.and Agrawal, R.and Gunopulos and F. Leymann. Mining
process models from workflow logs. In Schek, H., Saltor, F., Ramos, I., and
Alonso, G., editors, Proceedings of the 6th International Conference on
Extending Database Technology (EDBT'98),, 1377 of Lecture Notes in
Computer Science,:469-483., 1998.
[2] A. K. Alves de Medeiros, W. M. P. van Dongen, B. F.and
van der Aalst, and A. J. M. M. Weijters. Process mining: Extending the
alpha-algorithm to mine short loops. BETA working paper series 113,
Eindhoven University of Technology, 2004.
[3] A.Karla: A.Medeiros. Genetic process mining. phd
thesis, University Press Facilities, Eindhoven.
[4] W. A.Rozinat, van der Aalst. Decision mining in prom.
In Dustdar, S., Fiadeiro, J., Sheth, A., eds.: BPM 2006.of Lecture Notes in
Computer Science., Springer-Verlag, ,.
[5] J. Bae, L. Liu, J.Caverlee, L.J. Zhan, and H. Bae.
Development of distance measures for process mining, discovery, and
integration. International Journal of Web Services Research,, 4 ( 4 ),
2007.
[6] B.Dongen. Process mining and verification. Phd
thesis, by University Press Facilities, Eindhoven, 2007.
[7] J. Cook and A. Wolf. Discovering models of software
processes from event-based data. ACM Transactions on Software Engineering
and Methodology,, 7(3):215-249., 1998.
[8] J. Cortadella, Lavagno L. Kishinevsky, M., and A.:
Yakovlev. Deriving petri nets from finite transition systems. IEEE
Transactions on Computers,, 47(8) 1998.
77
Bibiography
[9] R. Dijkman, M. Dumas, B.van Dongen, R. Kaarik, and
J.Mendling. Similarity of business process models: Metrics and evaluation.
Information Systems,, 36(2), 2011.
[10] Zacarias M. Malheiros M. Ferreira P. Ferreira, D.
Approaching process mining with sequence clustering: Experiments and findings.
In: Proceedings of the 5th International Conference on Business Process
Management (BPM 2007)., 4714:360-374, 2007.
[11] J. Gama and J. Carmona. A fresh look at precision in
process conformance. In Business Process Management, Springer Berlin /
Heidelberg,:211 - 226, 2010.
[12] Guzzo A. Pontieri L. Sacca D. Greco, G. Mining
expressive process models by clustering workflow traces. In Dai, H.,
Srikant, R., Zhang, C., eds.: PAKDD., Springer, 3056:52-62, 2004.
[13] M. Hammer and J. Champy. Reengineering the corporation:
A manifesto for business revolution. Harper Business, New York, NY, USA,,
1993.
[14] Boudewijn F. van Dongen H.M.W. Verbeek, Joos C.A.M.
Buijs and Wil M.P. van der Aalst. Xes, xesame, and prom 6. P. Soffer and E.
Proper (Eds.): CAiSE Forum 2010,Springer-Verlag Berlin Heidelberg,
72:60-75, 2010.
[15] W.M.P. van der Aalst M. Dumas and A.H.M. ter Hofstede.
Process-aware information systems: Bridging people and software through process
technology. Wiley and Sons,.
[16] Mirjam Minor, Alexander Tartakovski, and Ralph Bergmann.
Representation and structure-based similarity assessment for agile workflows.
In Case-Based Reasoning Research and Development,.
[17] Tadao Murata. Petri nets: Properties, analysis and
applications. Proceedings of the IEEE,, 77 ( 4 ):541 - 580, 1989.
[18] J. NAKATUMBA and W.M.P. VAN DER AALST. Analyzing
resource behavior using process mining. In Alonso, G., Dadam, P., Rosemann,
M., eds.: International Conference on Business Process Management,of Lecture
Notes in Computer Science., Springer-Verlag.
[19] N.C.Russell. Foundations of process-aware information
systems. PhD thesis, Faculty of Information Technology, Queensland
University of Technology Brisbane, Australia,, 2007.
[20] M. NETJES and H.A.. REIJERS. Supporting the bpm
life-cycle with filenet.
78
Bibiography
[21] Wil M. P, van der Aalst, Ton A. J. M. M. Weijters, and
Ana Karla Alves de Medeiros. Process mining with the heuristics
miner-algorithm. BETA Working Paper Series, WP 166 , Eindhoven University
of Technology, Eindhoven.
[22] S.S. Pinter and M. Golani. Discovering workflow models
from activities' lifespans. comput. Ind.,, 2004.
[23] Reijers, Weijters H.A., Dongen A.J.M.M., M edeiros
B.F.V., A.K.A.D., M. Song, Ver-beek, and H.M.W. Business process mining: An
industrial application. information systems,. Wiley and Sons,.
[24] M. Dumas Remco M. Dijkman and C. Ouyang. Semantics and
analysis of business process models in bpmn. Information and Soft-ware
Technology, 50(12):1281-1294, 2008.
[25] S. Rindele and W. Van Dar Alaast. Life-cycle support for
staff assignment rules in process-aware information systems. Department of
Mathematics and Computing Science, Eindhoven University of Technology,.
[26] S. Rinderle, M. Reichert, and P. Dadam. Evaluation of
correctness criteria for dynamic workflow changes. In Aalst et al.
[AHW03],.
[27] A. Rozinat, A.K. Alves de Medeiros, C.W. Gunther,
A.J.M.M. Weijters, and W.M.P. van der Aalst. Towards an evaluation framework
for process mining algorithms. IEEE Transactions on Knowledge and Data
Engineering,, 2007.
[28] A. Rozinat, A.K. Alves de Medeiros, Christian W.,
Gunther Ton A, J. M. M. Weijters, and Wil M. P. van der Aalst. Towards an
evaluation framework for process mining algorithms. BPM Center Report
BPM-07-06,.
[29] A. Rozinat and W.M.P. van der Aalst. Conformance
checking of processes based on monitoring real behavior,. Information
Systems,, 2008.
[30] Wichian P. Sawitree W., Parham P. Process mining
a-algorithm as a tool (a case study of student registration). IEEE,
pages 4673-2317, 2012.
[31] G. Schimm. Process miner - a tool for mining process
schemes from event-based data. In S. Flesca and G. Ianni, editors,
Proceedings of the 8th European Conference on Artificial Intelligence (JELIA),,
2002.
[32] M. Song, C.W. Gunther, and W.M.P. van der Aalst. Trace
clustering in process mining. Eindhoven University of Technology P.O.Box
513, NL-5600 MB, Eindhoven, The Netherlands.
79
Bibiography
[33] PHD DEREK MIERS STEPHEN A. WHITE. BPMN Modeling and
Reference Guide: Understanding and Using BPMN. Published by Future
Strategies Inc., Book Division, 2010.
[34] MacLennan J. Tang, Z. 8. in: Data mining with sql server
2005. Wiley Publishing, Inc.,, page 209-227, 2005.
[35] van der Aalst, Weijters W. M. P., A. J. M. M., and L
Maruster. Workflow mining: Discovering process models from event logs. IEEE
Transactions on Knowledge and Data Engineering,, 16(9):1128-1142, 2004.
[36] de Medeiros A.A. Weijters A.: van der Aalst, W. Genetic
process mining an experimental evaluation. Data Mining and Knowledge
Discovery,., 14(2), 2007.
[37] REIJERS H.A. VAN DER AALST, W.M.P. and M.: SONG.
Discovering social networks from event logs. Comput. Supported Coop.
Work,.
[38] V.and van Dongen B. F.and Kindler E. van der Aalst, W.
M. P.and Rubin and C. W.: Gunther. Process mining: A two-step approach using
transition systems and regions. BPM Center Report,.
[39] W. M. P. van der Aalst and A. H. M. ter Hofstede. Yawl:
Yet another workflow language. information systems. 30(4):245-275, 2005.
[40] W.M.P. van der Aalst. The application of petri nets to
workflow management. Department of Mathematics and Computing Science,
Eindhoven University of Technology,.
[41] W.M.P. van der Aalst. Process mining discovery,
conformance and enhancement of business processes. Department of
Mathematics and Computing Science,Eindhoven University of Technology,.
[42] W.M.P. van der Aalst. Woflan: A petri-net-based workflow
analyzer. Department of Mathematics and Computing Science,Eindhoven
University of Technology,.
[43] W.M.P. van der Aalst. Verification of workflow nets,.
In P. Azema and G. Balbo, editors, Application and Theory of Petri Nets,
1248 of Lecture Notes in Computer Science:407-426, 1997.
[44] W.M.P. van der Aalst, A. Adriansyah, and B.F. van
Dongen. Causal nets: a modeling language tailored towards process discovery.
In Proceedings of the 22nd International Conference on Concurrency Theory,
CONCUR'11,Springer-Verlag., 2011.
80
Bibiography
[45] W.M.P. van der Aalst, V. Rubin, H. Verbeek, B.F. van
Dongen, E. Kindler, and C.W.Gunther. Process mining: a two-step approach to
balance between underfitting and over-fitting. Software and Systems
Modeling, 2(16) 2009.
[46] W.M.P. van der Aalst and K.M. van Hee. Workflow
management: Models, methods, and systems. MIT press, Cambridge, MA,,
2002.
[47] de Medeiros A.A. Verbeek H. Weijters A. van der Aalst W.
van Dongen, B. The prom framework: A new era in process mining tool support.
In Ciardo, G., Darondeau, P., eds.: Application and Theory of Petri Nets
2005. Lecture Notes in Computer Science., Springer-Verlag, Berlin,
3536:444-454, 2005.
[48] van der Aalst W. van Dongen, B. A meta model for process
mining data. In Casto, J., Teniente, E., eds.: Proceedings of the CAiSE'05
Workshops (EMOI-INTEROP Workshop). FEUP, Porto, Portugal,, page 309-320,
2005.
[49] H.M.W. Verbeek. Verification of wf-nets. phd thesis,
eindhoven university of technology, eindhoven, the netherlands,. 2004.
[50] van der Aalst W. de Medeiros A.A. Weijters, A. Process
mining with the heuristics miner algorithm. BETA Working Paper Series WP
166, Eindhoven University of Technology, 2006.
[51] L. Wen, J. Wang, W.M.P. van der Aalst, Z. Wang, , and J.
Sun. A novel approach for process mining based on event types. BETA Working
Paper Series, Eindhoven University of Technology, Eindhoven, page 118,
2004.
[52] L. Wen, J. Wang, W.M.P. van der Aalst, Z. Wang, , and J.
Sun. Mining process models with non-free-choice constructs. Data Mining and
Knowledge Discovery,, 2007.
[53] I. H. Witten and E. Frank. Data mining: practical
machine learning tools and techniques with java implementations.,. Morgan
Kaufmann Publishers Inc.,San Francisco, CA, USA., 2000.
[54] M. Zerari. Une approche pour l'amélioration de la
flexibilité des processus métier basée sur les techniques
du process mining. Thèse, Faculté des Sciences et de
l'ingénieur, université de Constantine, Algérie,
2012.
[55] R. Zurawski. Petri nets and industrial applications: A
tutorial. IEEE Trans-actions on Industrial Electronics,, 41 ( 6 ):567
- 583, 1994.
|
|
