Maria Luisa Sapino
Maria Luisa Sapino
Maria Luisa Sapino is a Full Professor at the University of Torino and, since 2006, Adjunct Professor at the Ira A Fulton School of Computing, Informatics, Decision Systems Engineering (CIDSE) at the Arizona State University. At ASU she is also affiliated with the Center for Assured and Scalable Data Engineering (CASCADE). Her initial contributions to computer science were in the area of logic programming and artificial intelligence, specifically in the semantics of negation in logic programming, and in the abductive extensions of logic programs. Since mid-90s she has been applying these techniques to the challenges associated with data security and access control, and with heterogeneous data management. Her research contributions are in the area of data management and analysis, with strong emphasis on tackling the so called “Big Data challenges”, including aspects related to the development of efficient techniques for tensor based big data analysis, pattern detection in voluminous and heterogeneous data collections, with special focus on various aspects related to indexing, classification and querying of (possibly multivariate) time series. Maria Luisa Sapino co-authored more than 100 research papers, most of them published on top ranked journals and conference proceedings. Her recent research includes interdisciplinary projects focusing on securing and optimizing health and infrastructure (study of the infectious disease propagation, study of real-time evacuation solutions in case of disaster management) as well as cyber-physical systems (building energy systems) that can benefit from “smart data oriented” fundamental technological innovations.
Abstract of the Presentation:
Leveraging Big Data Analysis to understand emerging phenomena in complex systems
Big data analysis is increasingly critical for understanding spatio-temporal dynamics of emerging phenomena, securing cyber-resources, detecting and predicting geo-temporal evolution of cyber-attacks, and helping protect against financial fraud.
The key technical challenge underlying these is that they often require one to track 10s or 100s of inter-dependent system components, spanning multiple information layers and spatio-temporal frames, affected by complex dynamic processes operating at different resolutions. Consequently, the key characteristics of data sets and models relevant to big data analysis of cyber-security events often include the following: (a) noisy, (b) multi-variate, (c) multi-resolution, (d) spatio-temporal, and (e) inter-dependent. Because of the volume and complexity of the data, the varying spatial and temporal scales at which relevant observations are made, today security experts lack the means to adequately and systematically interpret these observations and understand the underlying events and processes. In this talk, I will introduce computational challenges that arise from the need to process, index, search, and analyze, in a scalable manner, large volumes of multivariate data and present recent solutions.