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.
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.  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.
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