-
Mining Insights from Weakly-Structured Event Data
This thesis focuses on process mining on event data where such a normati...
read it
-
From Low-Level Events to Activities -- A Session-Based Approach (Extended Version)
Process-Mining techniques aim to use event data about past executions to...
read it
-
Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities
Process Discovery is concerned with the automatic generation of a proces...
read it
-
Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction
Processes, such as patient pathways, can be very complex, comprising of ...
read it
-
Heuristic Approaches for Generating Local Process Models through Log Projections
Local Process Model (LPM) discovery is focused on the mining of a set of...
read it
-
Abstracting spreadsheet data flow through hypergraph redrawing
We believe the error prone nature of traditional spreadsheets is due to ...
read it
-
Business Process Variant Analysis based on Mutual Fingerprints of Event Logs
Comparing business process variants using event logs is a common use cas...
read it
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of granularity, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and then use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs.
READ FULL TEXT
Comments
There are no comments yet.