Panos Pardalos
[intermediate/advanced] Data Analytics for Massive Networks
Summary
Data analytics for networks involves the use of advanced techniques and tools to extract insights and knowledge from large and complex datasets generated by network devices, applications, and services. This process involves collecting, storing, processing, and analyzing large amounts of data to identify patterns, trends, and anomalies that can provide valuable information for network operators. By leveraging data analytics, network researchers can make informed decisions about network planning, capacity management, service delivery, and customer experience. Additionally, data analytics can help network operators to detect and respond to security threats and attacks, by analyzing network traffic, identifying abnormal behavior, and detecting potential vulnerabilities. Overall, data analytics is a critical component of massive networks, enabling network researchers to extract valuable insights from massive datasets and improve network performance, efficiency, and security.
Syllabus
- Introduction to networks – a historical perspective and major advances
- Critical elements of networks and applications
- Networks in neuroscience
References
Thang N. Dinh, Ying Xuan, My T. Thai, Panos M. Pardalos, Taieb Znati, On New Approaches of Assessing Network Vulnerability: Hardness and Approximation. IEEE/ACM Trans. Netw. 20(2): 609-619 (2012).
Jose L. Walteros, Alexander Veremyev, Panos M. Pardalos, Eduardo L. Pasiliao, Detecting critical node structures on graphs: A mathematical programming approach. Networks 73(1): 48-88 (2019).
Schieber, T., Carpi, L., Díaz-Guilera, A. et al. Quantification of network structural dissimilarities. Nat Commun 8, 13928 (2017). https://doi.org/10.1038/ncomms13928
Pappas I., Pardalos P. (2015) Identifying Cognitive States Using Regularity Partitions. PLoS ONE 10(8): e0137012.
https://doi.org/10.1371/journal.pone.0137012
Frank M. Skidmore, Dmytro Korenkevych, Y. Liu, G. He, Edward T. Bullmore, Panos M. Pardalos, Connectivity brain networks based on wavelet correlation analysis in Parkinson fMRI data. Neuroscience Letters (2011), vol 499, pp. 47-51.
James Abello, Panos M. Pardalos, Mauricio G. C. Resende (Eds), Handbook of Massive Data Sets, Springer (2002).
https://link.springer.com/book/10.1007/978-1-4615-0005-6
Pre-requisites
Basic knowledge of optimization and computer sciences.
Short bio
Panos M. Pardalos is a Distinguished Emeritus Professor in the Department of Industrial and Systems Engineering at the University of Florida. He is a Fellow of AAAS, AAIA, AIMBE, EUROPT, and INFORMS. He has been awarded the 2013 EURO Gold Medal Prize bestowed by the Association for European Operational Research Societies. This Medal is the Preeminent European Award given to Operations Research (OR) Professionals for “Scientific Contributions that Stand the Test of Time.” He has been awarded a prestigious Humboldt Research Award (2018-2019). The Humboldt Research Award is granted in recognition of a researcher’s entire achievements to date – fundamental discoveries, new theories, insights that have had significant impact on their discipline.
