Search Results for author: Natalia Sidorova

Found 11 papers, 0 papers with code

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

no code implementations3 Nov 2017 Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst

We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques.

Guided Interaction Exploration in Artifact-centric Process Models

no code implementations7 Jun 2017 Maikel L. van Eck, Natalia Sidorova, Wil M. P. van der Aalst

For example, we are able to highlight strongly correlated behaviours in different artifacts.

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

no code implementations25 May 2017 Niek Tax, Emin Alasgarov, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma

Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models.

Mining Process Model Descriptions of Daily Life through Event Abstraction

no code implementations25 May 2017 Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst

However, events recorded in smart home environments are on the level of sensor triggers, at which process discovery algorithms produce overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts.

The Imprecisions of Precision Measures in Process Mining

no code implementations3 May 2017 Niek Tax, Xixi Lu, Natalia Sidorova, Dirk Fahland, Wil M. P. van der Aalst

In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log.

Interest-Driven Discovery of Local Process Models

no code implementations21 Mar 2017 Niek Tax, Benjamin Dalmas, Natalia Sidorova, Wil M. P. van der Aalst, Sylvie Norre

Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes.

Heuristic Approaches for Generating Local Process Models through Log Projections

no code implementations10 Oct 2016 Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma

Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i. e. subsets of possible events are taken into account to create so-called local process models.

On Generation of Time-based Label Refinements

no code implementations12 Sep 2016 Niek Tax, Emin Alasgarov, Natalia Sidorova, Reinder Haakma

Finding the right event labels to enable application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing).

Event Abstraction for Process Mining using Supervised Learning Techniques

no code implementations23 Jun 2016 Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst

We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity.

Log-based Evaluation of Label Splits for Process Models

no code implementations23 Jun 2016 Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst

We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective.

Mining Local Process Models

no code implementations20 Jun 2016 Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst

The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining.

Model Discovery Sequential Pattern Mining

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