# Research Trends, Enabling Technologies and Application Areas for Big Data
## 3 Application Areas for Big Data  
Big Data is becoming increasingly important in many industrial and commercial application areas [4; 5]. One example of this is the _telecommunication_ domain [6; 7]. Today, phones in a mobile network continuously generate Call Detail Records (CDRs) and Internet Protocol Detailed Records (IPDRs). These huge amounts of data can be used to create value for the telecom operators in different ways. [PERSON] et al. showed that Big Data analytics can be used for churn prediction in telecommunication networks [8]. In [9; 10], [PERSON] et al. showed that this kind of mobility data makes it possible to predict and balance the load in telecommunication networks. Since this is a Big Data application, one not only needs to find methods that can extract the business value from the data, but it is equally important to have proper database systems for handling large amounts of mobility data in an efficient way. This aspect was explored by [PERSON] and [PERSON] [11]. Two conclusions from that and similar studies were that the Cassandra NoSQL database is very efficient in many cases, and many providers of industrial and commercial Big Data applications use virtualized cloud systems for storing and processing huge amounts of data in an efficient and scalable way. The performance characteristics of cloud-based storage and Cassandra in virtualized environments were investigated by [PERSON] et al. [12; 13]. In [14], [PERSON] et al. discussed the fact that context-aware mobile applications are emerging as a relevant technology to improve user satisfaction in telecommunication networks. That paper emphasizes the importance of developing methods that can support us to find value in huge amounts of data and at the same time provide ways of handling the data in an efficient way. Another industrial and commercial application area where Big Data plays an important role is _manufacturing_[15; 16]. In [17], [PERSON] et al. presented a systematic literature mapping of different areas in manufacturing where Big Data analytics have been applied. The authors identified the following eight areas: process and planning; enterprise; maintenance and diagnosis; supply chain; transport and logistics; environment, health and safety; product design and quality. The study by [PERSON] et al. list these eight areas in a decreasing order related to the amount of research being performed (i.e., the largest amount of research was conducted in the process and planning area, and the smallest amount of research was in the area of quality). The pharmaceutical industry is heavily regulated, and it is therefore important that production lines are auditable. In [18], [PERSON] at al. suggested an architecture for smart pharmaceutical manufacturing using blockchain properties and smart contracts to ensure data authenticity, transparency and immutability. In [19], [PERSON] and [PERSON] performed a review of the existing research on Big Data in manufacturing. In their paper, they identified 16 barriers that need to be addressed before the manufacturing industry can fully benefit from Big Data analytics. The most critical barrier is the lack of commitment from top management. Big Data has been used in the application area of _smart cities_[20]. Smart cities typically include huge numbers of different sensors and Internet of Things devices, which continuously produce large amounts of data regarding human behavior and mobility in a smart city [21]. By using Big Data analytics on such data, one can improve sustainability and the quality of life [22]. One way of handling Big Data efficiently in smart cities is to process the data close to the sources using edge or fog computing [23]. In [24], [PERSON] et al. presented the approach to Big Data analytics developed in an industry academia project in Italy (the SIBDA project). The paper discusses the elements of Big Data tackled in the three different subareas in the project, namely document processing, mass e-mail applications and Internet of Things sensor networks.