Process Discovery

The Process Discovery component is initialised by the GUI when a calculation on a log file has to be performed. It takes as input a list of CSV logs previously recorded and a multiprocessing queue used to communicate with the main process.

Decision points

class modules.decisionPoints.DecisionPoints(df: DataFrame, status_queue: Queue)

Decision points class

__generateKeywordsDataframe(dataframe: DataFrame)

Generate keywords dataframe, used in GUI when selecting decision points

Parameters:

dataframe – pandas dataframe

Returns:

keywords dataframe with duplicates removed and values sorted

__init__(df: DataFrame, status_queue: Queue)

Decision points class

Parameters:

• df – pandas dataframe of entire process

• status_queue – queue to print values in the GUI

generateDecisionDataframe() → DataFrame

Find decision points in dataframe, ask user which decisions to take and generate final trace built from decisions.

Returns:

dataframe built from user decisions

handle_df()

Pre-process dataframe.

• filter irrelevant rows

• add hostname column

• mark duplicated rows

Returns:

processed dataframe df1

number_of_decision_points()

Calculates the number of decision points in a trace

Returns:

number of decision points

Note

The it-h row of a routine trace is considered as duplicated if it includes an event that is exactly the same in the it-h row of all the other routine traces. We evaluate two events as identical if the following data fields have the same value for the event in the it-h row in all the recorded routine traces:

        self.duplication_subset = ['category', 'application', 'concept:name', 'event_src_path', 'event_dest_path',
                                   'browser_url_hostname', 'xpath']  # 'tag_value', 'clipboard_content', 'cell_range'

Most frequent routine

Once a log composed by different traces of execution is captured and analysed, the routine that was repeated most often is selected and passed to RPA tool for script creation.

Warning

This method has been deprecated after the introduction of decision points analysis

modules.mostFrequentRoutine.selectMostFrequentCase(dataframe: DataFrame, status_queue: Queue, flattened=False, threshold=90)

Select the most frequent routine in the process by using levenhstein distance to calculate similarity between strings

Parameters:

• dataframe – low level pandas dataframe of process

• status_queue – queue to print messages in GUI

• flattened –

• threshold – threshold of similarity, traces are considered similar if they are equal by at least 90%

Returns:

most frequent trace

Deprecated since version 1.2.0: Replaced by decision points

Diagrams

Different high-level diagrams are generated during the analysis of the log file:

1. Directly-Follows Graphs (DFG) of the entire process model;

2. Business Process Model and Notation (BPMN) of the most frequent routine;

3. Petri Net of the most frequent routine.

class modules.process_mining.ProcessMining(filepath: list, status_queue: Queue, merged=False)

Process Discovery component is initialised by the GUI when a calculation on a log file needs to be performed.

__init__(filepath: list, status_queue: Queue, merged=False)

Parameters:

• filepath – path of the csv file

• status_queue – queue to print messages on GUI

• merged – true if class has been called when merging multiple files

_createDFG(log=None, parameters=None, high_level=False)

create df using dataframe with all traces

Parameters:

• log – low-level event log

• parameters – ooptional parameters to generate image

_createImageParameters(log=None, high_level=False)

Create parameters for diagrams that needs to ben generated. Parameters include source and target nodes as well as file format (diagrams are saved as pdf).

Parameters:

• log – event log

• high_level – boolean, if true generate high level diagram

_create_directories()

Creates directories inside RPA folder where processed files will be saved. Directories include event_log, SW_robot, process_discovery.

_create_image(gviz, img_name, verbose=False)

Create image file of the generated diagram (DFG,BPMN,Petr net)

Parameters:

• gviz – image file generated by pm4py

• img_name – name of the image to be saved

• verbose – display log while generating images

_create_petri_net(remove_duplicates=False)

Generate low level petri net

Parameters:

remove_duplicates –

Returns:

petri net

Deprecated since version 1.2.0: Not in use anymore

_getSourceTargetNodes(log=None, high_level=False)

Identifies the first and the last line in the event log, and mark them as source and target node. Used to color first and last node in the generated diagrams.

Parameters:

• log – low level event log

• high_level –

Returns:

source and target nodes

highLevelDFG()

Create high level DFG of entire process

highLevelPetriNet()

Create high level petri net of entire process

save_dfg(name='DFG', high_level=False)

Save DFG to file

Parameters:

• name – optional name of dfg file

• high_level – generate high level dfg

Deprecated since version 1.2.0: Not in use anymore

save_petri_net(name)

Save low level petri net in pdf format from low level event log

Parameters:

name – name of the generated petri net

Deprecated since version 1.2.0: Not in use anymore

class modules.flowchart.Flowchart(df: DataFrame)

Generate high level flowchart diagram (BPMN) from a given trace

__init__(df: DataFrame)

Parameters:

df – high level pandas dataframe of a trace

__make_link(a_node, b_node, label=None, width=1, style='dashed')

Make an edge between two nodes

Parameters:

• a_node – first node

• b_node – second node

• label – label between nodes

• width – width of the link

• style – link style (dashed or straight)

Returns:

edge

__make_node(name, shape=None)

Generate pydot node

Parameters:

• name – name of the node

• shape – shape of the node (default is square)

Returns:

pydot node

__pairwise(iterable)

Generate list of pairs from a given list. Used to connect nodes and generate diagram.

Parameters:

iterable – list of values

Returns:

list of pairs

generateFlowchart(path: str, name: Optional[str] = None)

Generate flowchart between all the nodes in a trace

Parameters:

• path – where to save diagram

• name – name of generated diagram