loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Raluca Portase ; Ramona Tolas and Rodica Potolea

Affiliation: Technical University of Cluj-Napoca, Cluj, Romania

Keyword(s): Data Analysis, Big Data, Preprocessing Methodology, Knowledge Extraction, Real Industrial Data, Metadata Extraction.

Abstract: Hidden and unexpected value can be found in the vast amounts of data generated by IoT devices and industrial sensors. Extracting this knowledge can help on more complex tasks such as predictive maintenance or remaining useful time prediction. Manually inspecting the data is a slow, expensive, and highly subjective task that made automated solutions very popular. However, finding the value inside Big Data is a difficult task with many complexities. We present a general preprocessing methodology (MEDIS- MEthdology for preprocessing Data with multiple complexitIeS) consisting of a set of techniques and approaches which address such complexities.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.204.56.97

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Portase, R.; Tolas, R. and Potolea, R. (2021). MEDIS: Analysis Methodology for Data with Multiple Complexities. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, ISBN 978-989-758-533-3; ISSN 2184-3228, pages 191-198. DOI: 10.5220/0010655100003064

@conference{kdir21,
author={Raluca Portase. and Ramona Tolas. and Rodica Potolea.},
title={MEDIS: Analysis Methodology for Data with Multiple Complexities},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR,},
year={2021},
pages={191-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010655100003064},
isbn={978-989-758-533-3},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR,
TI - MEDIS: Analysis Methodology for Data with Multiple Complexities
SN - 978-989-758-533-3
IS - 2184-3228
AU - Portase, R.
AU - Tolas, R.
AU - Potolea, R.
PY - 2021
SP - 191
EP - 198
DO - 10.5220/0010655100003064