IntelliSys: Intelligent System for Road Safety
This is an Invited Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Debasis Das, Indian Institutes of Technology(IIT) Jodhpur.
Chronorobotics - Spatio-temporal models for social and service robots
This is a Lecture delivered at Big the Data Analytics Summer School 2020 by Dr. Tomas Krajnik, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague.
Soft computing for Transparent synthesis of Geo Big Data
This is a Keynote Lecture delivered at the Big Data Analytics Summer School 2020 by Dr Gloria Bordogna, Italian National Research Council IREA
Semantic information infrastructures from business information delivery to water management
This is a Keynote Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Mariana Damova, Mozajka.
Data for AI: Foresight
This is a Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Simon Scerri, Fraunhofer IAIS.
Predictive Analytics in Renewable Energy Systems
With the aim of improving ecological interest, the share of renewable energy sources (RES) in energy production has to be increased. Nonetheless, that growth adversely influences the grid’s instability, as a result of the dependency between the RES production and weather conditions.
Survey on Big Data Tools
This introductory lecture discusses the Big Data processing pipeline and the Big Data Landscape from the following perspectives
- Big Data Frameworks
- NoSQL Platforms and Knowledge Graphs
- Stream Processing Data Engines
- Big Data Preprocessing
- Big Data Analytics
- Big Data Visualization Tools.
Overview and Comparison of Machine Learning Algorithms
Big Data Analytics is a crucial component of the Big data paradigm and refers to the process of extracting useful knowledge from large datasets or streams of data. Due to enormity, high dimensionality, heterogeneous, and distributed nature of data, traditional techniques of data mining may be unsuitable to work with big data.
SCADA Intrusion Detection Systems
Specific intrusion detection systems (IDSs) are needed to secure modern supervisory control and data acquisition (SCADA) systems due to their architecture, stringent real-time requirements, network traffic features and specific application layer protocols. This lecture aims to contribute to assess the state-of-the-art, identify the open issues and provide an insight for future study areas. To achieve these objectives, we start from the factors that impact the design of dedicated intrusion detection systems in SCADA networks and focus on network-based IDS solutions.
AI and Knowledge Graphs
This is a Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Emanuel Sallinger, University of Oxford.
