Showing 8 of total 8 results (show query)
bupaverse
processmapR:Construct Process Maps Using Event Data
Visualize event logs using directed graphs, i.e. process maps. Part of the 'bupaR' framework.
Maintained by Gert Janssenswillen. Last updated 7 months ago.
9 stars 7.64 score 169 scripts 3 dependentsbupaverse
processanimateR:Process Map Token Replay Animation
Provides animated process maps based on the 'procesmapR' package. Cases stored in event logs created with with 'bupaR' S3 class eventlog() are rendered as tokens (SVG shapes) and animated according to their occurrence times on top of the process map. For rendering SVG animations ('SMIL') and the 'htmlwidget' package are used.
Maintained by Felix Mannhardt. Last updated 2 years ago.
animationbuparbusiness-processevent-logshtmlwidgetsprocess-mapprocessminingsvg-animations
66 stars 7.54 score 106 scriptsbupaverse
processcheckR:Rule-Based Conformance Checking of Business Process Event Data
Check compliance of event-data from (business) processes with respect to specified rules. Rules supported are of three types: frequency (activities that should (not) happen x number of times), order (succession between activities) and exclusiveness (and and exclusive choice between activities).
Maintained by Gert Janssenswillen. Last updated 2 years ago.
4 stars 5.58 score 32 scripts 1 dependentsbupaverse
bupaverse:Easily Install and Load the 'bupaverse'
The 'bupaverse' is an open-source, integrated suite of R-packages for handling and analysing business process data, developed by the Business Informatics research group at Hasselt University, Belgium. Profoundly inspired by the 'tidyverse' package, the 'bupaverse' package is designed to facilitate the installation and loading of multiple 'bupaverse' packages in a single step. Learn more about 'bupaverse' at the <https://bupar.net> homepage.
Maintained by Gert Janssenswillen. Last updated 2 years ago.
2 stars 4.32 score 21 scriptsbupaverse
heuristicsmineR:Discovery of Process Models with the Heuristics Miner
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.
Maintained by Felix Mannhardt. Last updated 3 years ago.
buparevent-logheuristics-minerpetri-netprocess-miningcpp
14 stars 4.08 score 17 scriptsbupaverse
daqapo:Data Quality Assessment for Process-Oriented Data
Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...).
Maintained by Niels Martin. Last updated 3 years ago.
6 stars 3.78 score 9 scriptsbupaverse
processmonitR:Building Process Monitoring Dashboards
Functions for constructing dashboards for business process monitoring. Building on the event log objects class from package 'bupaR'. Allows the use to assemble custom shiny dashboards based on process data.
Maintained by Gert Janssenswillen. Last updated 7 years ago.
1 stars 3.48 score 30 scripts
gertjanssenswillen
processpredictR:Process Prediction
Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple ways to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <arXiv:2104.00721>.
Maintained by Gert Janssenswillen. Last updated 2 years ago.
3.15 score 28 scripts