Document Open Access Logo

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

Author Alfredo Cuzzocrea

Thumbnail PDF


  • Filesize: 0.52 MB
  • 7 pages

Document Identifiers

Author Details

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


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)


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
  • Big Data Analytics
  • Big Data Management
  • Temporal Big Data Analytics
  • Temporal Big Data Management


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. Divyakant Agrawal, Amr El Abbadi, Sudipto Das, and Aaron J Elmore. Database scalability, elasticity, and autonomy in the cloud. In International Conference on Database Systems for Advanced Applications, pages 2-15. Springer, 2011. Google Scholar
  2. Sara Alghunaim and Heyam H Al-Baity. On the scalability of machine-learning algorithms for breast cancer prediction in big data context. IEEE Access, 7:91535-91546, 2019. Google Scholar
  3. Rasim M Alguliyev, Ramiz M Aliguliyev, and Fargana J Abdullayeva. Privacy-preserving deep learning algorithm for big personal data analysis. Journal of Industrial Information Integration, 15:1-14, 2019. Google Scholar
  4. Safaa Alwajidi and Li Yang. Multi-resolution hierarchical structure for efficient data aggregation and mining of big data. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM), pages 153-159. IEEE, 2019. Google Scholar
  5. Ladjel Bellatreche, Alfredo Cuzzocrea, and Soumia Benkrid. ℱ&𝒜: A methodology for effectively and efficiently designing parallel relational data warehouses on heterogenous database clusters. In International Conference on Data Warehousing and Knowledge Discovery, pages 89-104. Springer, 2010. Google Scholar
  6. Boualem Benatallah, Hamid Reza Motahari-Nezhad, et al. Scalable graph-based olap analytics over process execution data. Distributed and Parallel Databases, 34(3):379-423, 2016. Google Scholar
  7. Alina Campan, Alfredo Cuzzocrea, and Traian Marius Truta. Fighting fake news spread in online social networks: Actual trends and future research directions. In 2017 IEEE International Conference on Big Data (Big Data), pages 4453-4457. IEEE, 2017. Google Scholar
  8. Michelangelo Ceci, Alfredo Cuzzocrea, and Donato Malerba. Effectively and efficiently supporting roll-up and drill-down olap operations over continuous dimensions via hierarchical clustering. Journal of Intelligent Information Systems, 44(3):309-333, 2015. Google Scholar
  9. Badrish Chandramouli, Jonathan Goldstein, and Songyun Duan. Temporal analytics on big data for web advertising. In 2012 IEEE 28th international conference on data engineering, pages 90-101. IEEE, 2012. Google Scholar
  10. Yubo Chen, Carson K Leung, Siyuan Shang, and Qi Wen. Temporal data analytics on covid-19 data with ubiquitous computing. In 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pages 958-965. IEEE, 2020. Google Scholar
  11. Alfredo Cuzzocrea. Accuracy control in compressed multidimensional data cubes for quality of answer-based olap tools. In 18th International Conference on Scientific and Statistical Database Management (SSDBM'06), pages 301-310. IEEE, 2006. Google Scholar
  12. Alfredo Cuzzocrea. Retrieving accurate estimates to olap queries over uncertain and imprecise multidimensional data streams. In International Conference on Scientific and Statistical Database Management, pages 575-576. Springer, 2011. Google Scholar
  13. Alfredo Cuzzocrea. Analytics over big data: Exploring the convergence of datawarehousing, olap and data-intensive cloud infrastructures. In 2013 IEEE 37th Annual Computer Software and Applications Conference, pages 481-483. IEEE, 2013. Google Scholar
  14. Alfredo Cuzzocrea. Privacy and security of big data: current challenges and future research perspectives. In Proceedings of the first international workshop on privacy and secuirty of big data, pages 45-47, 2014. Google Scholar
  15. Alfredo Cuzzocrea. Aggregation and multidimensional analysis of big data for large-scale scientific applications: models, issues, analytics, and beyond. In Proceedings of the 27th International Conference on Scientific and Statistical Database Management, pages 1-6, 2015. Google Scholar
  16. Alfredo Cuzzocrea, Carmen De Maio, Giuseppe Fenza, Vincenzo Loia, and Mimmo Parente. Olap analysis of multidimensional tweet streams for supporting advanced analytics. In Proceedings of the 31st Annual ACM Symposium on Applied Computing, pages 992-999, 2016. Google Scholar
  17. Alfredo Cuzzocrea, Carson Kai-Sang Leung, and Richard Kyle MacKinnon. Mining constrained frequent itemsets from distributed uncertain data. Future Generation Computer Systems, 37:117-126, 2014. Google Scholar
  18. Alfredo Cuzzocrea and Il-Yeol Song. Big graph analytics: the state of the art and future research agenda. In Proceedings of the 17th International Workshop on Data Warehousing and OLAP, pages 99-101, 2014. Google Scholar
  19. Alfredo Cuzzocrea, Il-Yeol Song, and Karen C Davis. Analytics over large-scale multidimensional data: the big data revolution! In Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP, pages 101-104, 2011. Google Scholar
  20. Alfredo Cuzzocrea and Wei Wang. Approximate range-sum query answering on data cubes with probabilistic guarantees. Journal of Intelligent Information Systems, 28(2):161-197, 2007. Google Scholar
  21. Timothée Dubuc, Frederic Stahl, and Etienne B Roesch. Mapping the big data landscape: technologies, platforms and paradigms for real-time analytics of data streams. IEEE Access, 9:15351-15374, 2020. Google Scholar
  22. Diego García-Gil, Sergio Ramírez-Gallego, Salvador García, and Francisco Herrera. A comparison on scalability for batch big data processing on apache spark and apache flink. Big Data Analytics, 2(1):1-11, 2017. Google Scholar
  23. Rihan Hai, Christoph Quix, and Chen Zhou. Query rewriting for heterogeneous data lakes. In European Conference on Advances in Databases and Information Systems, pages 35-49. Springer, 2018. Google Scholar
  24. Weigang Hou, Zhaolong Ning, Lei Guo, and Xu Zhang. Temporal, functional and spatial big data computing framework for large-scale smart grid. IEEE Transactions on Emerging Topics in Computing, 7(3):369-379, 2017. Google Scholar
  25. Gang-Hoon Kim, Silvana Trimi, and Ji-Hyong Chung. Big-data applications in the government sector. Communications of the ACM, 57(3):78-85, 2014. Google Scholar
  26. Chin-Ho Lin, Liang-Cheng Huang, Seng-Cho T Chou, Chih-Ho Liu, Han-Fang Cheng, and I-Jen Chiang. Temporal event tracing on big healthcare data analytics. In 2014 IEEE International Congress on Big Data, pages 281-287. IEEE, 2014. Google Scholar
  27. Ives Cavalcante Passos, Benson Mwangi, and Flávio Kapczinski. Big data analytics and machine learning: 2015 and beyond. The Lancet Psychiatry, 3(1):13-15, 2016. Google Scholar
  28. Philip Russom et al. Big data analytics. TDWI best practices report, fourth quarter, 19(4):1-34, 2011. Google Scholar
  29. Michael Stonebraker. Sql databases v. nosql databases. Communications of the ACM, 53(4):10-11, 2010. Google Scholar
  30. Allen Van Gelder. The well-founded semantics of aggregation. In Proceedings of the Eleventh ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 127-138, 1992. Google Scholar
  31. Xiaochun Yun, Guangjun Wu, Guangyan Zhang, Keqin Li, and Shupeng Wang. Fastraq: A fast approach to range-aggregate queries in big data environments. IEEE Transactions on Cloud Computing, 3(2):206-218, 2014. Google Scholar
  32. Junsheng Zhang, Changqing Yao, Yunchuan Sun, and Zengquan Fang. Building text-based temporally linked event network for scientific big data analytics. Personal and Ubiquitous Computing, 20(5):743-755, 2016. Google Scholar
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail