LAMBDA - Learning, Applying, Multiplying Big Data Analytics

Information systems are most often the main focus when considering applications of Big Data technology. However, the energy domain is more than suitable also given the worldwide coverage of electrification. Additionally, the energy sector has been recognized to be in dire need of modernization, which would include tackling (i.e. processing, storing and interpreting) a vast amount of data. The motivation for including a case study on the applications of big data technologies in the energy domain is clear, and is thus the purpose of this chapter. An application of linked data and post-processing energy data has been covered, whilst a special focus has been put on the analytical services involved, concrete methodologies and their exploitation.

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. In our analysis, we focused on literature (review articles) accessible via the Elsevier ScienceDirect service and the Springer Link service from more recent years, mainly from the last two decades. For the selected industries, this chapter also discusses challenges that can be addressed and overcome using the semantic processing approaches and knowledge reasoning approaches discussed in this book.