Abstract: defined as a collection of activities or

 

Abstract:

Process
Mining is a Business Process Management technique that allows extracting the
knowledge from Event Logs with Process Model. It is commonly available in
today’s Business information system. The Business Process Management (BPM) is a
process that involving any combination of Modeling, Automation, Execution and
Control the measurement of Business Process. It Optimize the Business Process
Model with Event Log. The Comprehensive Knowledge of a BPM mechanism is depending
on perspective for good practice and effective of out coming source process
model.  A Conformance Checking is one of
technique that used in the Process Mining. It is a detecting and measuring
difference between observed and modeled behavior. The Conformance is increase
and improves the availability of Event Log.  The Event Log makes the task highly relevant
for Process Analysis and Improvement.  It
is especially in the Banking System and the Industries where efficiency and
effectiveness have recently been becoming more and more important. Hence, there
is a need of Conformance Checking (CC) is more and more effective from the BPM.
In this survey, the various literature works based on the CC are reviewed. The
Conformance based on the quality assurance procedure that provides measures of
the distance between a process model and its originating event log. Further,
the purpose of Process Mining, Simulation Platform and the Performance metrics of
each paper is discussed in this survey. The survey suggests some major future
scope to the research based on the challenges and the research gaps present in
the reviewed papers.

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Keywords: Process Mining (PM), Business
Process Management (BPM). Conformance Checking (CC),  

                   Event
Log, Process Model

 

Introduction:

 

Today’s
Business Process Management (BPM) are challenged to make their processes more
efficient and effective, costs and response times need to be reduced in all
todays industries.  It is defined as a
collection of activities or tasks that have different dependencies of each
other. So, each of the activity can be identified with automated event, which
is executed by a certain person or may be an automated task that is executed by
a system. Process Mining (PM) is growing due to increasing needs for
automatically extracting the knowledge from events logs recorded by an
information system. It is relatively young research discipline that sits
between computational intelligence and data mining, on the other hand. This
Knowledge, in the form of business process model and can be extracted by the PM
algorithms. All Business Process Management (BPM) can be used for the purpose
of process compliance and refining the current business models.  It provides an important role between BI and
BPM, Data mining and Workflow. It includes automated process discovery that is
extracting process model from an event log.

There
are three types of Process Mining:

i.                   
Process
Discovery

ii.                 
Conformance
Checking

iii.               
Process
Enhancement

1.1.Process Discovery:

The application of Process
Discovery is one of the three major techniques that follow in the PM. It
related to Business Process Management and Process Mining is a set of
techniques that manually or automatically construct a representation of organization’s
current business processes and its major process variations.

Figure 1.1.Example
of Process Discovery

The
technique is use the evidence that
found in the existing BPM methods of Process Model. Then run the Business
Process within the Business Management by using the documentation and the
technology of stored information system.  In this case, there is no prior Process Model;
the Model is discovered based on Event Logs. It
creates a process master which complements Business Process Analysis (BPA). BPA
tools and methodologies are well suited to top-down hierarchical process
decomposition, and analysis of to-be processes. BPD provides a bottoms-up
analysis that marries to the top-down to provide a complete business process,
organized hierarchically by BPA. From the Figure 1.1 it illustrates         the
Business Process Discovery technology that is required today. The Automated
Process Discovery captures the required data and transforms it into a
structured dataset for the actual diagnosis. 
A main idea is the grouping of repetitive actions from the users into
meaningful events. Next, these Process Discovery tools propose probabilistic
process models. Probabilistic behavior is essential for the monitoring and the
diagnosing of the processes. The following shows an example where a
probabilistic repair-process is recovered from user actions. The Process Model
shows exactly where the discomfort is in this business. Five percent faulty
repairs is a bad sign, but worse, the repetitive fixes that are needed to
complete those repairs are cumbersome.

Figure 1.2.Process Discovery

A deeper
analysis of the Event Log may reveal which are the faulty parts that are
responsible for the overall behavior in this example. It may lead to the
discovery of subgroups of repairs that actually need management focus for
improvement. In this case, it would become evident that the faulty parts are
also responsible for the repetitive resolutions. Similar applications have been
documented, such as a Healthcare Insurance Provider case where in 4 months the
Region of Interest. Business Process Analysis was earned from precisely
comprehending its claims handling process and discovering the faulty parts.

1.2.Conformance Checking (CC)

Conformance
Checking (CC) is one of the techniques in the Process Mining approach. It compares
the Process Model with an Event Log of the same Process. The Conformance is
used to heck if the actual execution of a business process, as recorded in the
Event Log, conforms to the model and vice versa. The CC is techniques take as
input a Process Model and Event Log and reappearance a set of differences
between the performance captured in the Process Model and the Performance captured
in the Event Log. These modifications may be characterized visually (e.g.
overlaid on top of the process model) or Textually as lists of natural language
statements (e.g., activity x is executed multiple times in the log, but this is
not allowed according to the model). Some techniques may also produce a
normalized measures (between 0 and 1) indicating to what extent the process
model and the event log match each other