Rialto Process features

Data Acquisition/Output
Import (MySQL, OpenSQL, MS Data Server, SAP Hana, …), CSV files, XES files (OpenXES 2.0), Process Variant LogCube, Case LogCube
Export (MySQL, OpenSQL, MS Data Server, SAP Hana, …)),CSV files, XES files (OpenXES 2.0), Process Variant LogCube, Case LogCube
Data Set Enrichment
Add start and end activities to data sets
Anonymization option for case IDs, resources, attributes
Various Field Format transformations (Condition Based Parsing, Concatenating, …)
Missing value addition (based on data interpolation) (tbc)
SQL Joins (allowing combination of historic/new-fresh datasets)
Data Blending
Blend and ‘Connect’ data from various sources to a single dataset for subsequent process-data-text analytics and modelling
Blend historic event, case, process variant data with new data
Event Filtering/Tagging
Data Filtering

  • filter cases based on presence or absence of specific attribute value
  • Remove events with undesired values
  • using
    • Wildcarting (*, ?, …)
    • Condition Based (find ‘XYZ’)
    • Rule Based (if then else, Case With)
Process Filtering/Tagging

  • Process Flow Family Detection
    • Repetitive activity combination clustering
  • Activity filtering
    • High Frequency, Low Frequency
  • Performance filtering (Case level)
    • Short throughput, long throughput (Performance)
  • Rule Based filtering
    • Tag events based on activity sequence rules (starting event, preceeding event, following event, concurrent event, ending event)
  • Time Filtering/Trimming
    • Set Period for analyses (start – end)
    • All cases must fall within (start – end)(focus on completed cases only)
    • All cases end within period (focus on incomplete end case scenario’s)
    • All case starting within period (focus on incomplete start case scenario’s)
  • Process Mining Key Field Determination and Selection
    • Case ID, TimeStamp, Activity, Resource
Data Quality Analyses

  • Data Field/Attribute Statistics to detect missing values (and other)