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Comparison of process mining techniques application to flexible and unstructured processes

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par HANANE ARIOUAT
Université Paris est créteil - Master 2 2014
  

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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.

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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.

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