Home // ICSEA 2022, The Seventeenth International Conference on Software Engineering Advances // View article

A Smart Manufacturing Data Lake Metadata Framework for Process Mining

Authors:
Michalis Pingos
Andreas S. Andreou

Keywords: Smart Manufacturing; Data Lakes; Heterogeneous Data Sources; Metadata Mechanism; Data Blueprints; Process Mining

Abstract:
The fourth industrial revolution consists of a new level of organization and control of the entire production process. Smart manufacturing ecosystem and especially Cyber Physical Systems are evolving rapidly. They constitute an environment with multiple heterogeneous sources that produce high volumes of data. This data need to be stored in a storage system that can handle raw, unprocessed, relational, and nonrelational data types, such as Data Lakes, in order to be processed when needed and bring insight. This paper introduces a Data Lake-based metadata framework, which utilizes the concept of blueprints to characterize the data sources and the data itself to facilitate process mining tasks. The applicability and effectiveness of the proposed framework is validated through a real-world smart manufacturing casestudy, namely a poultry meat production factory, which offers operational support and business workflow analysis.

Pages: 1 to 8

Copyright: Copyright (c) IARIA, 2022

Publication date: October 16, 2022

Published in: conference

ISSN: 2308-4235

ISBN: 978-1-61208-997-3

Location: Lisbon, Portugal

Dates: from October 16, 2022 to October 20, 2022