activity
20142026
most citedPredictive Business Process Monitoring with LSTM Neural Networks

527 citations · 967 across the 30 of their papers we have counts for

collaborators
Showing 2017Show all

7 papers · 1 filter

cs.DS2017

Mining Non-Redundant Local Process Models From Sequence Databases

Niek Tax, Marlon Dumas

Sequential pattern mining techniques extract patterns corresponding to frequent subsequences from a sequence database. A practical limitation of these techniques is that they overl…

cs.DB2017★ 65 cited

Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities

Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst

Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs…

cs.LG2017★ 28 cited

Mining Process Model Descriptions of Daily Life through Event Abstraction

Niek Tax, Natalia Sidorova, Reinder Haakma +1

Process mining techniques focus on extracting insight in processes from event logs. Process mining has the potential to provide valuable insights in (un)healthy habits and to contr…

cs.DB2017★ 67 cited

The Imprecisions of Precision Measures in Process Mining

Niek Tax, Xixi Lu, Natalia Sidorova +2

In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed…

cs.DB2017★ 5 cited

Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models

Felix Mannhardt, Niek Tax

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…

cs.LG2017

Generating Time-Based Label Refinements to Discover More Precise Process Models

Niek Tax, Emin Alasgarov, Natalia Sidorova +2

Process mining is a research field focused on the analysis of event data with the aim of extracting insights related to dynamic behavior. Applying process mining techniques on data…

cs.DB2017★ 23 cited

Interest-Driven Discovery of Local Process Models

Niek Tax, Benjamin Dalmas, Natalia Sidorova +2

Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to gener…