A holistic approach to trust and reputation management in big data - Leonit Zeynalvand (Nanyang Technological University, 2021)
Big Data is becoming an increasingly important part of everyday life. In this dissertation we consider two types of Big Data, in particular user-generated data and IoT-generated data. We refer to the Big Data generated by users of online services as user-generated data . Ratings submitted by users for online purchases are examples of user-generated data. We refer to the Big Data generated by devices in the Internet of Things (IoT) as IoT-generated data. The key difference between these two types of Big Data is the density of data. Density here means with what frequency an entity perceives information about another entity. Hence, user-generated data is sparse while IoT-generated data is dense due to the higher frequency of interactions between IoT entities compared to that of social entities. On one hand a sparse dataset results in less accurate knowledge extraction compared to a dense dataset. On the other hand a dense dataset risks more privacy exposure compared to a sparse dataset. Regardless of the sparsity, a dataset may contain subjective information which affects the knowledge extraction. Common examples of knowledge extraction are trust and reputation management (TRM) systems.