Automation of the analysis of process flows to support recommendations for optimisation.

• discovery of a process flow model, from the data available in an event log, at various levels of granularity

• performance analysis:  understanding of the strengths and weaknesses in process flows via performance metrics (such as frequencies, time delays, bottlenecks)
• conformance checking: identification of deviations from what is considered normal system behaviour
• business decision support by combining domain knowledge with the  picture of what is happening versus what should happen in the system (according to official norms or unofficial wishes): to improve performance indicator values such as cost, time delays and resource allocation. 

• directly from the data available in the event log
• at various levels of granularity

• analysis templates configuration | selection for process discovery
• dataset statistics that could help you decide what to use for analysis

Automated analysis and understanding of the “highways” in the processes, in terms of:

• outliers concerning the main Key Performance Indicators (group sizes, end-to-end time delays, fitness values, pathway lengths)
• (potentially) disruptive | unusual pathway behavior (e.g unusually large end to end waiting times for unusually large group sizes)
• mismatches between expectations vs. reality (actions that were / were not expected to happen, but didn’t / did)

• Slice through your dataset as you wish before process discovery.
• Filter the discovered pathways, depending on average frequency, end-to-end time delays, model fitness, group size.

Identification of the deviations in the process model and quantification of their frequencies:

• tasks in the model that should have occurred but did not occur in reality
• tasks that have occurred in reality although they were not anticipated in the model.
• frequency of deviations

Get insights into your data structure. Automatically get a visual display of:

• the overall structure of your dataset.
• the distribution of values per column.

Easily keep track of all of all of your datasets. Add, remove , update as you wish.