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Temporal Big Data Analytics: New Frontiers for Big Data Analytics Research (Panel Description)

Author Alfredo Cuzzocrea

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Author Details

Alfredo Cuzzocrea
  • iDEA Lab, University of Calabria, Rende, Italy
  • LORIA, Nancy, France

Funding

This research has been partially supported by the French PIA project "Lorraine Université d’Excellence", reference ANR-15-IDEX-04-LUE.

Acknowledgements

The open access publication of this article was supported by the Alpen-Adria-Universität Klagenfurt, Austria.

Cite AsGet BibTex

Alfredo Cuzzocrea. Temporal Big Data Analytics: New Frontiers for Big Data Analytics Research (Panel Description). In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 206, pp. 4:1-4:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.TIME.2021.4

Abstract

Big data analytics is an emerging research area with many sophisticated contributions in the actual literature. Big data analytics aims at discovering actionable knowledge from large amounts of big data repositories, based on several approaches that integrate foundations of a wide spectrum of disciplines, ranging from data mining to machine learning and artificial intelligence. Among the concrete innovative topics of big data analytics, temporal big data analytics covers a first-class role and it is attracting the attention of larger and larger communities of academic and industrial researchers. Basically, temporal big data analytics aims at modeling, capturing and analyzing temporal aspects of big data during analytics phase, including specialized tasks such as big data versioning over time, building temporal relations among ad-hoc big data structures (such as nodes of big graphs) and temporal queries over big data. It is worth to notice that temporal big data analytics research is characterized by several open challenges, which range from foundations, including temporal big data representation and processing, to applications, including smart cities and bio-informatics tools. Inspired by these considerations, this paper focuses on models, paradigms, techniques and future challenges of temporal big data analytics, by reporting on state-of-the-art results as well as emerging trends, with also criticisms on future work that we should expect from the community.

Subject Classification

ACM Subject Classification
  • Information systems → Temporal data
  • Information systems → Data analytics
Keywords
  • Big Data Analytics
  • Big Data Management
  • Temporal Big Data Analytics
  • Temporal Big Data Management

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