SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

IoS³ - Institut für Smart Systems und Services, Pforzheim University, Tiefenbronner Straße 65, 75175 Pforzheim, Germany

Context (please use complete words only)

Field

Sort by

1 Papers

Development of a Procedure for the Processing of Raw Sensor Data from Smart Devices for Utilisation in Process Mining

Topics: Business-It Alignment; Data Visualization; Smart Devices; Industrial/Manufacturing; Business Processes; Process Modeling; Data-Driven Value Creation; Data Collection; Business Analytics ; Sensor Networks and Software

Authors: Rebecca Bulander , Bernhard Kölmel and Marcel Rath

Affiliation: IoS³ - Institut für Smart Systems und Services, Pforzheim University, Tiefenbronner Straße 65, 75175 Pforzheim, Germany

Digital Object Identifier: 10.5220/0012122700003552

Proceedings of ICSBT 2023, ISBN: 978-989-758-667-5

Quick Abstract

This research paper proposes a novel approach to address the blind spot in process mining. The method involves collecting raw sensor data using a smartphone application and transforming it into an event log using Microsoft Power Query. The experimental process is then discovered using process mining to reduce the blind spot in process mining. The study demonstrates the potential of process mining in previously unexplored areas and shows how enterprises that could not previously benefit from process mining can now do so. The paper concludes by highlighting the importance of this research in bridging the gap in process mining and making it accessible to a wider range of businesses.