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.
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.
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.
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.
The goal of this chapter is to shed light on different types of big data applications needed in various industries including healthcare, transportation, energy, banking and insurance, digital media and e-commerce, environment, safety and security, telecommunications, and manufacturing. In response to the problems of analyzing large-scale data, different tools, techniques, and technologies have bee developed and are available for experimentation.
The rapid development of digital technologies, IoT products and connectivity platforms, social networking applications, video, audio and geolocation services has created opportunities for collecting/accumulating a large amount of data. While in the past corporations used to deal with static, centrally stored data collected from various sources, with the birth of the web and cloud services, cloud computing is rapidly overtaking the traditional in-house system as a reliable, scalable and cost-efective IT solution.
Although each government in Europe with their public administration services can be treated as a big data ecosystem, the opportunities of interconnecting, integrating and processing the data on EU level presents a real challenge nowadays. Discussions on public benefit of integrating and opening the data can be found in our previous work, where we examine the use of Linked Data Approach in European e-Government Systems.
Big Data technologies are often used in domains where data is generated, stored and processes with rates that cannot be efficiently processed by one computer. One of those domains is definitely the domain of energy. Here, the processes of energy generation, transmission, distribution and use have to be concurrently monitored and analyzed in order to assure system stability without brownouts or blackouts. The transmission systems (grids) that transport electric energy are in general very large and robust infrastructures that are accompanied with an abundance of monitoring equipment.