Uncertain Data Algorithms and Applications
In recent years, a number of indirect data collection methodologies have led to the proliferation of uncertain data. Such databases are much more complex because of the additional challenges of representing the probabilistic information. In this paper, the authors provide uncertain data mining and management applications. They will explore the various models utilized for uncertain data representation. In the field of uncertain data management, they will examine traditional database management methods such as join processing; query processing, selectivity estimation, OLAP queries, and indexing.