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Authors: Michalis Pingos and Andreas S. Andreou

Affiliation: Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus

Keyword(s): Smart Data Processing, Deep Insight, Data Lakes, Heterogeneous Data Sources, Metadata Mechanism, 5Vs Big Data Characteristics, Data Blueprints.

Abstract: One of the greatest challenges in Smart Big Data Processing nowadays revolves around handling multiple heterogeneous data sources that produce massive amounts of structured, semi-structured and unstructured data through Data Lakes. The latter requires a disciplined approach to collect, store and retrieve/ analyse data to enable efficient predictive and prescriptive modelling, as well as the development of other advanced analytics applications on top of it. The present paper addresses this highly complex problem and proposes a novel standardization framework that combines mainly the 5Vs Big Data characteristics, blueprint ontologies and Data Lakes with ponds architecture, to offer a metadata semantic enrichment mechanism that enables fast storing to and efficient retrieval from a Data Lake. The proposed mechanism is compared qualitatively against existing metadata systems using a set of functional characteristics or properties, with the results indicating that it is indeed a promising approach. (More)

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Paper citation in several formats:
Pingos, M. and Andreou, A. (2022). A Data Lake Metadata Enrichment Mechanism via Semantic Blueprints. In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-568-5; ISSN 2184-4895, SciTePress, pages 186-196. DOI: 10.5220/0011080400003176

@conference{enase22,
author={Michalis Pingos. and Andreas S. Andreou.},
title={A Data Lake Metadata Enrichment Mechanism via Semantic Blueprints},
booktitle={Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2022},
pages={186-196},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011080400003176},
isbn={978-989-758-568-5},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - A Data Lake Metadata Enrichment Mechanism via Semantic Blueprints
SN - 978-989-758-568-5
IS - 2184-4895
AU - Pingos, M.
AU - Andreou, A.
PY - 2022
SP - 186
EP - 196
DO - 10.5220/0011080400003176
PB - SciTePress