Colloquium IS Shaya Pourmirza: Correlation Mining: Mining Process Orchestrations without Case Identifiers

Speaker:
Shaya Pourmirza, Remco Dijkman, Paul Grefen

Title:
Correlation Mining: Mining Process Orchestrations without Case Identifiers

Abstract:
Process discovery algorithms aim to capture process orchestration models from event logs. These algorithms have been designed for logs in which events that belong to the same case are related to each other – and to that case – by means of a unique case identifier. However, in service oriented systems these case identifiers are usually not stored beyond request-response pairs, which makes it hard to relate events that belong to the same case. This is known as the correlation challenge. This paper addresses the correlation challenge by introducing a new process discovery algorithm, called the correlation miner, that facilitates process discovery when events are not associated with a case identifier. Experiments performed on both synthetic and real-world event logs show the applicability of the correlation miner.

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PhD defense Jan Claes

Cover_JanMore information can be found on: http://janclaes.info/phd.php

In order to download the thesis, follow: http://janclaes.info/pdf/Claes2015PhD.pdf

 

 

 

 

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Summary

Lately, the focus of organizations is changing fundamentally. Where they used to spend almost exclusively attention to results, in terms of goods, services, revenue and costs, they are now concerned about the efficiency of their business processes. Each step of the business processes needs to be known, controlled and optimized. This explains the huge effort that many organizations currently put into the mapping of their processes in so-called (business) process models.

Unfortunately, sometimes these models do not (completely) reflect the business reality or the reader of the model does not interpret the represented information as intended. Hence, whereas on the one hand we observe how organizations are attaching increasing importance to these models, on the other hand we notice how the quality of process models in companies often proves to be insufficient.

The doctoral research makes a significant contribution in this context. This work investigates in detail how people create process models and why and when this goes wrong. A better understanding of current process modeling practice will form the basis for the development of concrete guidelines that result in the construction of better process models in the future.  The first study investigated how we can represent the approach of different modelers in a cognitive effective way, in order to facilitate knowledge building. For this purpose the PPMChart was developed. It represents the different operations of a modeler in a modeling tool in such a way that patterns in their way of working can be detected easily. Through the collection of 704 unique modeling executions (a joint contribution of several authors in the research domain), and through the development of a concrete implementation of the visualization, it became possible to gather a great amount of insights about how different people work in different situations while modeling a concrete process.

The second study explored, based on the discovered modeling patterns of the first study, the potential relations between how process models were being constructed and which quality was delivered. To be precise, three modeling patterns from the previous study were investigated further in their relation with the understandability of the produced process model. By comparing the PPMCharts that show these patterns with corresponding process models, a connection was found in each case. It was noticed that when a process model was constructed in consecutive blocks (i.e., in a structured way), a better understandable process model was produced. A second relation stated that modelers who (frequently) moved (many) model elements during modeling usually created a less understandable model. The third connection was found between the amount of time spent at constructing the model and a declining understandability of the resulting model. These relations were established graphically on paper, but were also confirmed by a simple statistical analysis.

The third study selected one of the relations from the previous study, i.e., the relation between structured modeling and model quality, and investigated this relation in more detail. Again, the PPMChart was used, which has lead to the identification of different ways of structured process modeling. When a task is difficult, people will spontaneously split up this task in sub-tasks that are executed consecutively (instead of simultaneously). Structuring is the way in which the splitting of tasks is handled. It was found that when this happens consistently and according to certain logic, modeling became more effective and more efficient. Effective because a process model was created with less syntactic and semantic errors and efficient because it took less time and modeling operations. Still, we noticed that splitting up the modeling in sub-tasks in a structured way, did not always lead to a positive result. This can be explained by some people structuring the modeling in the wrong way. Our brain has cognitive preferences that cause certain ways of working not to fit. The study identified three important cognitive preferences: does one have a sequential or a global learning style, how context-dependent one is and how big one”s desire and need for structure is. The Structured Process Modeling Theory was developed, which captures these relations and which can form the basis for the development of an optimal individual approach to process modeling. In our opinion the theory has the potential to also be applicable in a broader context and to help solving various types of problems effectively and efficiently.

New Employee: Jonnro Erasmus

JErasmus_I was born and raised in Pretoria, South Africa. I’ve unfortunately never had a lion or a giraffe for a pet though. In 2008 I earned a Bachelor of industrial engineering from the University of Pretoria and in 2012 I completed a Master of Engineering Management from the University of Johannesburg. I’ve spent about six years as an industrial engineer in the electricity and manufacturing industry sectors. My recent research looked into the way Product Lifecycle Management technology can be used to enable collaboration across the product value chain. I’m interested in anything related to business processes, systems engineering and complexity theory. I now aim to make use of my experience and knowledge to contribute as much as possible to the HORSE project (I still don’t know what the acronym is for).

As a PhD student at the Technische Universiteit Eindhoven I have two primary objectives: learning as much as possible and making a meaningful contribution. Oh, perhaps a third objective is also in order: earning a PhD from this wonderful institution. Additionally, I wish to experience the various cultures to be found in and around the university and I want to learn to speak Dutch properly. I look forward to the many interesting discussions we are bound to have here and perhaps even working together in the not too distant future.