Type: Package
Title: Discovery of Process Models with the Heuristics Miner
Version: 0.3.0
Description: Provides the heuristics miner algorithm for process discovery as proposed by Weijters et al. (2011) <doi:10.1109/CIDM.2011.5949453>. The algorithm builds a causal net from an event log created with the 'bupaR' package. Event logs are a set of ordered sequences of events for which 'bupaR' provides the S3 class eventlog(). The discovered causal nets can be visualised as 'htmlwidgets' and it is possible to annotate them with the occurrence frequency or processing and waiting time of process activities.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp, BH
SystemRequirements: C++
Depends: R (≥ 2.10)
Imports: bupaR, processmapR (≥ 0.3.1), rlang, magrittr, dplyr, tidyr, DiagrammeR (≥ 1.0.0), petrinetR (≥ 0.3.0), purrr, scales, Rcpp, ggplot2, ggthemes, data.table, stringr
Suggests: eventdataR, svgPanZoom, DiagrammeRsvg
RoxygenNote: 7.2.3
URL: https://github.com/bupaverse/heuristicsmineR
BugReports: https://github.com/bupaverse/heuristicsmineR/issues
NeedsCompilation: yes
Packaged: 2023-04-04 12:51:28 UTC; lucp8407
Author: Felix Mannhardt [aut, cre], Gert Janssenswillen [ctb]
Maintainer: Felix Mannhardt <f.mannhardt@tue.nl>
Repository: CRAN
Date/Publication: 2023-04-04 13:20:06 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Heuristics miner example log #1

Description

Artificial eventlog for illustrating Heuristics Miner published as supplementary material to the book ⁠Process Mining: Discovery, Conformance and Enhancement of Business Processes⁠.

Usage

L_heur_1

Format

Eventlog containing 40 cases

References

Process Mining: Discovery, Conformance and Enhancement of Business Processes by W.M.P. van der Aalst, Springer Verlag, 2011 (ISBN 978-3-642-19344-6).

Heuristics miner example log #2

Description

Artificial eventlog for illustrating Heuristics Miner published as supplementary material to the book ⁠Process Mining: Discovery, Conformance and Enhancement of Business Processes⁠.

Usage

L_heur_2

Format

Eventlog containing 85 cases

References

Process Mining: Discovery, Conformance and Enhancement of Business Processes by W.M.P. van der Aalst, Springer Verlag, 2011 (ISBN 978-3-642-19344-6).

Converts the object to a Petrinet

Description

Converts the object to a Petrinet

Usage

as.petrinet(obj)

Arguments

obj

The event log to be used. An object of class

Examples

data(L_heur_1)
cn <- causal_net(L_heur_1, threshold = .8)
pn <- as.petrinet(cn)
petrinetR::render_PN(pn)

Compute input and output bindings

Description

Computes the input- and output bindings for use in a causal map. Several heuristics may be used to determine the activities that are activated or consumed by an event. The Flexible Heuristic Miner (FHM) paper describes a heuristic that looks ahead (or looks back) until the end of the trace and determines those activities as activated for which no other cause (activity in a causal dependency) is found. This approach is implemented as type nearest.

Usage

causal_bindings(eventlog, dependencies, type = c("nearest"))

Arguments

eventlog

The bupaR event log.

dependencies

A dependency matrix obtained, for example, through dependency_matrix.

type

The heuristic used to determine the bindings. Currently only nearest is available.

Value

A data frame

Examples

causal_bindings(L_heur_1,
                dependencies = dependency_matrix(L_heur_1))

Custom map profile

Description

Function to create a custom map profile based on some event log attribute.

Usage

causal_custom(
  FUN = mean,
  attribute,
  units = "",
  color_scale = "RdPu",
  color_edges = "red4",
  ...
)

Arguments

FUN

A summary function to be called on the process time of a specific activity, e.g. mean, median, min, max

attribute

The name of the case attribute to visualize (should be numeric)

units

Character to be placed after values (e.g. EUR for monetary euro values)

color_scale

Name of color scale to be used for nodes. Defaults to RdPu See Rcolorbrewer::brewer.pal.info() for all options.

color_edges

The color used for edges. Defaults to red4.

...

Additional arguments forwarded to FUN

Details

If used for edges, it will show the attribute values which related to the out-going node of the edge.

Examples

causal_net(L_heur_1,
           type_nodes = causal_custom(attribute = "timestamp"),
           type_edges = causal_custom(attribute = "timestamp"))

Frequency map profile

Description

Function to create a frequency profile for a process map.

Usage

causal_frequency(
  value = c("absolute", "relative"),
  color_scale = "PuBu",
  color_edges = "dodgerblue4"
)

Arguments

value

The type of frequency value to be used: absolute, relative (percentage of activity instances).

color_scale

Name of color scale to be used for nodes. Defaults to PuBu. See Rcolorbrewer::brewer.pal.info() for all options.

color_edges

The color used for edges. Defaults to dodgerblue4.

Examples

causal_net(L_heur_1,
           type = causal_frequency("relative"))

Create a Causal net (also Heuristics net)

Description

Creates a Causal net, also known as Heuristics net. This is similar to a processmapR process map. However, the causal map deals with parallelism by trying to identifying causal dependencies between activities by using different heuristics as documented in dependency_matrix.

Usage

causal_net(
  eventlog = NULL,
  dependencies = dependency_matrix(eventlog = eventlog, threshold = threshold,
    threshold_frequency = threshold_frequency, ...),
  bindings = causal_bindings(eventlog, dependencies),
  threshold = 0.9,
  threshold_frequency = 0,
  type = causal_frequency("absolute"),
  sec = NULL,
  type_nodes = type,
  type_edges = type,
  sec_nodes = sec,
  sec_edges = sec,
  ...
)

Arguments

eventlog

The event log for which a causal map should be computed. Can be left NULL for more control if parameters dependencies and bindings are provided directly.

dependencies

A dependency matrix created for the event log, for example, by dependency_matrix.

bindings

Causal bindings created by causal_bindings.

threshold

The dependency threshold to be used when using the default dependency matrix computation.

threshold_frequency

The frequency threshold to be used when using the default dependency matrix computation.

type

A causal map type. For example, causal_frequency or causal_performance.

sec

A causal process map type. Values are shown between brackets.

type_nodes

A causal map type to be used for nodes only.

type_edges

A causal map type to be used for edges only.

sec_nodes

A secondary causal map type for nodes only.

sec_edges

A secondary causal map type for edges only.

...

Further parameters forwarded to the default dependency_matrix function.

Details

Warning: Projected frequencies are heuristically determined and counts may not add up.

Value

A DiagrammeR graph of the causal map.

Examples

# Causal map with default parameters
causal_net(L_heur_1)

# Causal map with lower dependency treshold
causal_net(L_heur_1, threshold = .8)

# For even more control omit the `eventlog` parameter
# and provide `dependencies` and `bindings` directly.
d <- dependency_matrix(L_heur_1, threshold = .8)
causal_net(dependencies = d,
           bindings = causal_bindings(L_heur_1, d, "nearest"))

# The returned DiagrammeR object can be further augmented with
# panning and zooming before rendering:

library(magrittr)
causal_net(L_heur_1) %>%
 render_causal_net(render = TRUE) %>%
 DiagrammeRsvg::export_svg() %>%
 svgPanZoom::svgPanZoom()

Performance map profile

Description

Function to create a performance profile for a causal map.

Usage

causal_performance(
  FUN = mean,
  units = c("mins", "secs", "hours", "days", "weeks", "months", "quarters", "semesters",
    "years"),
  color_scale = "Reds",
  color_edges = "red4",
  ...
)

Arguments

FUN

A summary function to be called on the process time of a specific activity, e.g. mean, median, min, max

units

The time unit in which processing time should be presented (mins, hours, days, weeks, months, quarters, semesters, years. A month is defined as 30 days. A quarter is 13 weeks. A semester is 26 weeks and a year is 365 days

color_scale

Name of color scale to be used for nodes. Defaults to Reds. See Rcolorbrewer::brewer.pal.info() for all options.

color_edges

The color used for edges. Defaults to red4.

...

Additional arguments forwarded to FUN

Examples

causal_net(L_heur_1,
           type = causal_performance())

Create a dependency matrix

Description

Creates a dependency matrix from a precedence matrix (precedence_matrix) based on different approaches.

Usage

dependency_matrix(
  eventlog = NULL,
  dependency_type = dependency_type_fhm(threshold_dependency = threshold,
    threshold_frequency = threshold_frequency, ...),
  threshold = 0.9,
  threshold_frequency = 0,
  ...
)

Arguments

eventlog

A bupaR event log, may be NULL when a precedence matrix is provided.

dependency_type

Which approach to use for calculation of the dependency matrix. Currently only (dependency_type_fhm) is available.

threshold

A dependency threshold, usually in the interval ⁠[0,1]⁠, filtering out dependencies below the threshold.

threshold_frequency

An absolute frequency threshold filtering dependencies which are observed infrequently.

...

Parameters forwarded to (dependency_type_fhm).

Value

A square matrix with class dependency_matrix containing the computed dependency values between all activities.

See Also

precedence_matrix

Examples

d <- dependency_matrix(L_heur_1)
print(d)
as.matrix(d)

Dependency type based on Flexible Heuristics Miner (FHM)

Description

Computes the dependencies based on the approach known as Flexible Heuristics Miner.

Usage

dependency_type_fhm(
  threshold_dependency = 0.9,
  threshold_l1 = threshold_dependency,
  threshold_l2 = threshold_dependency,
  threshold_frequency = 0,
  all_connected = FALSE,
  endpoints_connected = FALSE
)

Arguments

threshold_dependency

A dependency threshold, usually in the interval ⁠[0,1]⁠, filtering out dependencies below the threshold.

threshold_l1

A dependency threshold, usually in the interval ⁠[0,1]⁠, filtering out self-loop dependencies below the threshold.

threshold_l2

A dependency threshold, usually in the interval ⁠[0,1]⁠, filtering out length-two loop dependencies below the threshold.

threshold_frequency

An absolute frequency threshold filtering dependencies which are observed infrequently.

all_connected

If TRUE the best antecedent and consequent (as determined by the dependency measure) are going to be added regardless of the threshold value.

endpoints_connected

If TRUE the start/end activity is added as antecedent/consequent when an activity would not be connected according to the threshold value.

Value

A dependency type.

References

A. J. M. M. Weijters and J. T. S. Ribeiro, "Flexible Heuristics Miner (FHM)," 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, 2011, pp. 310-317. doi: 10.1109/CIDM.2011.5949453

Examples

dependency_matrix(L_heur_1,
                  dependency_type = dependency_type_fhm(all_connected = TRUE))

Dependency type based on time intervals

Description

Computes the dependencies based on the approach taking into account activity durations based on life-cycle transitions.

Usage

dependency_type_lifecycle(
  threshold_dependency = 0.9,
  threshold_l1 = threshold_dependency,
  threshold_frequency = 0,
  all_connected = FALSE,
  endpoints_connected = FALSE
)

Arguments

threshold_dependency

A dependency threshold, usually in the interval ⁠[0,1]⁠, filtering out dependencies below the threshold.

threshold_l1

A dependency threshold, usually in the interval ⁠[0,1]⁠, filtering out self-loop dependencies below the threshold.

threshold_frequency

An absolute frequency threshold filtering dependencies which are observed infrequently.

all_connected

If TRUE the best antecedent and consequent (as determined by the dependency measure) are going to be added regardless of the threshold value.

endpoints_connected

If TRUE the start/end activity is added as antecedent/consequent when an activity would not be connected according to the threshold value.

Value

A dependency type.

References

A. Burattin and A. Sperduti, “Heuristics Miner for Time Intervals,” in ESANN 2010, 18th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 28-30, 2010, Proceedings, 2010.

Examples

dependency_matrix(L_heur_1,
                  dependency_type = dependency_type_fhm(all_connected = TRUE))

Hospital example event log capturing multi-perspectives

Description

Sample of 10 000 traces from an artificial eventlog from the PhD thesis 'Multi-perspective Process Mining' used to illustrate the Data-aware Heuristic Miner algorithm.

Usage

hospital_multi_perspective

Format

Eventlog containing a sample of 10 000 cases

Source

doi:10.4121/uuid:32cad43f-8bb9-46af-8333-48aae2bea037

References

Mannhardt, F. (Felix) (2016) Data-driven Process Discovery - Artificial Event Log. Eindhoven University of Technology. Dataset. https://doi.org/10.4121/uuid:32cad43f-8bb9-46af-8333-48aae2bea037

Parallel Matrix with Lifecycle

Description

Parallel Matrix with Lifecycle

Usage

parallel_matrix_lifecycle(eventlog)

Arguments

eventlog

The event log object to be used.

Examples

parallel_matrix_lifecycle(L_heur_1)

Dependency matrix plot

Description

Visualize a dependency matrix. A generic plot function for dependency matrices.

Usage

## S3 method for class 'dependency_matrix'
plot(x, ...)

Arguments

x

Dependency matrix

...

Additional parameters

Value

A ggplot object, which can be customized further, if deemed necessary.

Precedence Matrix

Description

Construct a precedence matrix, showing how activities are followed by each other. This is a performance improved variant of precedence_matrix in the processmapR package.

Usage

precedence_matrix(
  eventlog,
  type = c("absolute", "relative", "relative-antecedent", "relative-consequent",
    "relative-case")
)

Arguments

eventlog

The event log object to be used

type

The type of precedence matrix, which can be absolulte, relative, relative-antecedent or relative-consequent. Absolute will return a matrix with absolute frequencies, relative will return global relative frequencies for all antecedent-consequent pairs. Relative-antecedent will return relative frequencies within each antecendent, i.e. showing the relative proportion of consequents within each antecedent. Relative-consequent will do the reverse.

Examples

m <- precedence_matrix(hospital_multi_perspective, type = "absolute")
print(m)
as.matrix(m)

Precedence Matrix

Description

Construct a precedence matrix, showing how activities are followed by each other. This function computes the precedence matrix directly in C++ for efficiency. Only the type absolute of (precedence_matrix) is supported.

Usage

precedence_matrix_absolute(eventlog, lead = 1)

Arguments

eventlog

The event log object to be used.

lead

The distance between activities following/preceding each other.

Examples

library(eventdataR)
data(traffic_fines)
m <- precedence_matrix_absolute(traffic_fines)
print(m)
as.matrix(m)

Length Two Loop Precedence Matrix

Description

Construct a precedence matrix counting how often pattern aba occurs.

Usage

precedence_matrix_length_two_loops(eventlog)

Arguments

eventlog

The event log object to be used.

Examples

m <- precedence_matrix_length_two_loops(hospital_multi_perspective)
print(m)
as.matrix(m)

Precedence Matrix with Lifecycle

Description

Precedence Matrix with Lifecycle

Usage

precedence_matrix_lifecycle(eventlog)

Arguments

eventlog

The event log object to be used.

Examples

precedence_matrix_lifecycle(L_heur_1)

Generic print function for a Causal net

Description

Generic print function for a Causal net

Usage

## S3 method for class 'causal_net'
print(x, ...)

Arguments

x

Causal net object

...

Additional Arguments

Generic print function for a dependency matrix

Description

Generic print function for a dependency matrix

Usage

## S3 method for class 'dependency_matrix'
print(x, ...)

Arguments

x

dependency matrix object

...

Additional Arguments

Renders a Causal net as graph

Description

Renders a Causal net as graph

Usage

render_causal_net(
  causal_net,
  rankdir = "LR",
  layout = "dot",
  render = T,
  fixed_edge_width = F,
  fixed_node_pos = NULL,
  ...
)

Arguments

causal_net

A causal net created by causal_net

rankdir

Rankdir to be used for DiagrammeR.

layout

Layout to be used for DiagrammeR.

render

Whether to directly render the DiagrammeR graph or simply return it.

fixed_edge_width

If TRUE, don't vary the width of edges.

fixed_node_pos

When specified as a data.frame with three columns 'act', 'x', and 'y' the position of nodes is fixed. Note that his can only be used with the 'neato' layout engine.

...

Further parameters forwarded to the DiagrammeR render function.

Value

A DiagrammeR graph of the Causal net.

Examples

render_causal_net(causal_net(L_heur_1))

Renders a dependency matrix as dependency graph

Description

Creates a dependency graph visualizing the contents of a dependency matrix.

Usage

render_dependency_matrix(
  dependencies,
  rankdir = "LR",
  layout = "dot",
  render = T
)

Arguments

dependencies

A dependency matrix created by dependency_matrix

rankdir

Rankdir to be used for DiagrammeR.

layout

Layout to be used for DiagrammeR.

render

Whether to directly render the DiagrammeR graph or simply return it.

Value

A DiagrammeR graph of the (filtered) dependency matrix.

Examples

render_dependency_matrix(dependency_matrix(L_heur_1))