Home  | Publications | MTO+23

FEEED: Feature Extraction From Event Data

MCML Authors

Link to Profile Thomas Seidl PI Matchmaking

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

The analysis of event data is largely influenced by the effective characterization of descriptors. These descriptors serve as the building blocks of our understanding, encapsulating the behavior described within the event data. In light of these considerations, we introduce FEEED (Feature Extraction from Event Data), an extendable tool for event data feature extraction. FEEED represents a significant advancement in event data behavior analysis, offering a range of features to empower analysts and data scientists in their pursuit of insightful, actionable, and understandable event data analysis. What sets FEEED apart is its unique capacity to act as a bridge between the worlds of data mining and process mining. In doing so, it promises to enhance the accuracy, comprehensiveness, and utility of characterizing event data for a diverse range of applications.

inproceedings

Doctoral Consortium @ICPM 2023

Doctoral Consortium at the 5th International Conference on Process Mining. Rome, Italy, Oct 23-27, 2023.

Authors

A. Maldonado • G. M. Tavares • R. Oyamada • P. Ceravolo • T. Seidl

Links

PDF

Research Area

 A3 | Computational Models

BibTeXKey: MTO+23

Back to Top