«Abstract. At last decades people have to accumulate more and more data in different areas. Nowadays a lot of organizations are able to solve the ...»
Lasagna processes are relatively structured and the cases flowing through such processes are handled in a controlled manner. Therefore it’s possible to apply all of th
PM techniques presented in the preceding chapters. Definition of Lasagna process is:
a process is a Lasagna process if with limited efforts it is possible to create an agreedupon process model that has a fitness of at least 0.8, i.e., more than 80% of the events happen as planned and stakeholders confirm the validity of the model. In figure below presented example of Lasagna process by help Petri net and BPMN.
Easy to discover, but it is less interesting to show the "real" process. (close to expectation) Whole process mining toolbox can be applied.
Added value is predominantly in more advanced forms of process mining based on aligning log and model.
2.11.2. Spaghetti Processes Spaghetti processes are less structured than Lasagna processes, only some of process mining techniques can be applied. Figure below shows why unstructured processes are called Spaghetti processes. There are different approaches to get valuable analyze from such kind of processes. For example, method Divide and Conquer (by clustering of cases) or showing only the most frequencies paths and activities (Disco).
Figure 20. Example of Spaghetti process 2.
11.3. Applications of both types of model In figure 21 depicted overview of the different functional areas in a typical organization. Lasagna processes are typically encountered in production, finance/accounting, procurement, logistics, resource management, and sales/CRM.
Spaghetti processes are typically encountered in product development, service, resource management, and sales/CRM.
Figure 21. Applications of Spaghetti (violet cells), Lasagna (blue cells) processes and both (pink cells).
Nevertheless, Spaghetti processes are very interesting from the viewpoint of PM as they often allow for various improvements. A highly-structured well-organized process is often less interesting in this respect; it’s easy to apply PM techniques but there is also little improvement potential.
3. Conclusions PM is important tool for modern organizations that need to manage nontrivial operational processes. Data mining techniques aim to describe and understand reality based on historic data, but it’s low level of analyze, because this techniques are nor process-centric. Unlike most BPM approaches, PM is driven by factual event data rather than hand-made models. That’s why PM is called a bridge between BPM and Data Mining.
PM is not limited to process discover. By connecting event log and process model, new ways for analyzing are opened. Discovered process model can be also extended by information from various perspectives.
Nevertheless, as enough new approach PM has a lot of unsolved challenges. Some
of them are following:
There are no negative examples (i.e., a log shows what has happened but does not show what could not happen).
Preprocessing of Event log (problems with Noise and Incompleteness) Concept drift [13, 14] There is no clear how to correct recognize attributes of event log Due to concurrency, loops, and choices the search space has a complex structure and the log typically contains only a fraction of all possible behaviors.
There is no clear relation between the size of a model and its behavior (i.e., a smaller model may generate more or less behavior although classical analysis and evaluation methods typically assume some monotonicity property).
Improving the Representational Bias Used for Process Discovery Balancing Between Quality Criteria such as Fitness, Simplicity, Precision, and Generalization Improving Usability and Understandability for NonExperts Cross-Organizational Mining etc.
- Moreover, PM can be used off-line and online. And from a technological point of view online PM may be challenged.
Even so mature PM techniques and tools are available and it successfully used in over organizations.
References  Wil M.P. van der Aalst Process Mining: Discovery, Conformance and Enhancement of Business Processes ISBN: 978-3-642-19344-6, DOI 10.1007/978-3-642-19345-3, Springer Heidelberg Dordrecht London New York, 2002  Massive Open Online Course: "Process Mining: Data science in Action". Wil van der Aalst Eindhoven University of Technology, Department Methematics & Computer Science  IEEE CIS Task Force on Process Mining. Process Mining Manifesto  Wil van der Aalst :“Process Mining: X-Ray Your Business Processes“, Process Mining, Communications of the ACM CACM Volume 55 Issue 8 (August 2012) Pages 76- 83, http://doi.acm.org/10.1145/2240236.2240 257.
 Anne Rozinat1, Wil van der Aalst Process Mining: The Objectification of Gut Instinct - Making Business Processes More Transparent Through Data Analysis  Website www.habrahabr.ru/post/244879/, article Introduction in Process Mining  Michael Palmer Data is the New Oil  D.Harel and R.Marelly. Come, Let’s play: Scenari-Based Programming Using LSCs and Play-Engine. Springer, Berlin, 2003.
 Website www.processmining.org  Website www.fluxicon.com J.C. Bose, R. Mans, and W. van der Aalst. Wanna improve process mining
results? Computational Intelligence and Data Mining (CIDM 2013), doi:
10.1109/CIDM.2013.6597227  Burattin Andrea. Applicability of Process Mining techniques in Business Environments Christian Günter. Process Mining in Flexible Environment.
 Arjel Bautista, Lalit Wangikar, S.M. Kumail Akbar “Process Mining-Driven Optimization of a Consumer Loan Approvals Process” CKM Advisors, 711 Third Avenue, Suite 1806, NY, USA