Ontologies for Data Science: On Its Application to Data Pipelines

Sicilia, Miguel–Ángel; García‐Barriocanal, Elena; Sánchez‐Alonso, Salvador; Mora‐Cantallops, Marçal; Cuadrado, Juan

doi:10.1007/978-3-030-14401-2_16

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…To address the unique challenges posed by Data Applications, a dedicated monitoring framework must be established for Data pipelines. This framework should go beyond traditional application monitoring and focus on detecting and alerting not only pipeline unavailability but also issues such as corrupted or redundant data [5]. Real-time monitoring plays a critical role in this framework, allowing stakeholders to promptly identify and address Data Downtime events.…”

Section: Data Pipelinesmentioning

confidence: 99%

Real-Time Monitoring of Data Pipelines: Exploring and Experimentally Proving that the Continuous Monitoring in Data Pipelines Reduces Cost and Elevates Quality

Narayanan,

Zephan

2024

ICST Transactions on Scalable Information Systems

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Data pipelines are crucial for processing and transforming data in various domains, including finance, healthcare, and e-commerce. Ensuring the reliability and accuracy of data pipelines is of utmost importance to maintain data integrity and make informed business decisions. In this paper, we explore the significance of continuous monitoring in data pipelines and its contribution to data observability. This work discusses the challenges associated with monitoring data pipelines in real-time, propose a framework for real-time monitoring, and highlight its benefits in enhancing data observability. The findings of this work emphasize the need for organizations to adopt continuous monitoring practices to ensure data quality, detect anomalies, and improve overall system performance.

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Section: Data Pipelinesmentioning

confidence: 99%

Real-Time Monitoring of Data Pipelines: Exploring and Experimentally Proving that the Continuous Monitoring in Data Pipelines Reduces Cost and Elevates Quality

Narayanan,

Zephan

2024

ICST Transactions on Scalable Information Systems

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“…While generic enough for various ontology engineering projects and methodologies, it is fit for a specific type of project only. In [26], the authors proposed semantically annotating the manipulation and analysis of data in data processing "pipelines". Their efforts are much closer to the execution level of a particular data processing purpose.…”

Section: B Relation With Other Vocabulariesmentioning

confidence: 99%

An Ontology for Representing and Annotating Data Flows to Facilitate Compliance Verification

Debruyne

Riggio

Troyer

et al. 2019

2019 13th International Conference on Research Challenges in Information Science (RCIS)

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