Uncertain data management has seen a revival in interest in recent years because of a number of new fields which utilize this kind of data. For example, in fields such as privacy-preserving data mining, additional errors may be added to data in order to mask the identity of the records. Often the data may be imputed using statistical methods such as forecasting. In such cases, the data is uncertain in nature. Such data sets may often be probabilistic in nature. In other cases, databases may show existential uncertainty in which one or more records may be present or absent from the data set. Such data sets lead to a number of unique challenges in processing and managing the underlying data.
The field of uncertain data management has been studied in the traditional database literature, but the field has seen a revival in recent years because of new ways of collecting data. The field of uncertain data management presents a number of challenges in terms of collecting, modeling, representing, querying, indexing and mining the data. We further note that many of these issues are inter-related and cannot easily be addressed independently. While many of these issues have been addressed in recent research, the research in this area is often quite varied in its scope. For example, even the underlying assumptions of uncertainty are different across different papers. It is often difficult for researchers and students to find a single place containing a coherent discussion on the topic.