On Uncertain Probabilistic Data Modeling

Uncertainty in data is caused by various reasons including data itself, data mapping, and data policy. For data itself, data are uncertain because of various reasons. For example, data from a sensor network, Internet of Things or Radio Frequency Identification is often inaccurate and uncertain because of devices or environmental factors. For data mapping, integrated data from various heterogonous data sources is commonly uncertain because of uncertain data mapping, data inconsistency, missing data, and dirty data. For data policy, data is modified or hided for policies of data privacy and data confidentiality in an organization. But traditional deterministic data management mainly deals with deterministic data which is precise and certain, and cannot process uncertain data. Modeling uncertain data is a foundation of other technologies for further processing data, such as indexing, querying, searching, mapping, integrating, and mining data, etc. Probabilistic data models of relational databases, XML data and graph data are widely used in many applications and areas today, such as World Wide Web, semantic web, sensor networks, Internet of Things, mobile ad-hoc networks, social networks, traffic networks, biological networks, genome databases, and medical records, etc. This paper presents a survey study of different probabilistic models of uncertain data in relational databases, XML data, and graph data, respectively. The advantages and disadvantages of each kind of probabilistic modes are analyzed and compared. Further open topics of modeling uncertain probabilistic data such as semantic and computation aspects are discussed in the paper. Criteria for modeling uncertain data, such as expressive power, complexity, efficiency, extension are also proposed in the paper.