License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.TIME.2021.4
URN: urn:nbn:de:0030-drops-147804
URL: https://drops.dagstuhl.de/opus/volltexte/2021/14780/
Go to the corresponding LIPIcs Volume Portal


Cuzzocrea, Alfredo

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

pdf-format:
LIPIcs-TIME-2021-4.pdf (0.5 MB)


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.

BibTeX - Entry

@InProceedings{cuzzocrea:LIPIcs.TIME.2021.4,
  author =	{Cuzzocrea, Alfredo},
  title =	{{Temporal Big Data Analytics: New Frontiers for Big Data Analytics Research}},
  booktitle =	{28th International Symposium on Temporal Representation and Reasoning (TIME 2021)},
  pages =	{4:1--4:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-206-8},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{206},
  editor =	{Combi, Carlo and Eder, Johann and Reynolds, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14780},
  URN =		{urn:nbn:de:0030-drops-147804},
  doi =		{10.4230/LIPIcs.TIME.2021.4},
  annote =	{Keywords: Big Data Analytics, Big Data Management, Temporal Big Data Analytics, Temporal Big Data Management}
}

Keywords: Big Data Analytics, Big Data Management, Temporal Big Data Analytics, Temporal Big Data Management
Collection: 28th International Symposium on Temporal Representation and Reasoning (TIME 2021)
Issue Date: 2021
Date of publication: 16.09.2021


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI