Uncertainty accompanies our life processes and covers almost all fields of scientific studies.
Two general categories of uncertainty, namely, aleatory uncertainty and epistemic …

Uncertain data have already widely existed in many practical applications recently, such as
sensor networks, RFID networks, location-based services, and mobile object management …

Complex networks, such as biological, social, and communication networks, often entail
uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of …

The two most common paradigms to identify records of preference in a multi-objective
setting rely either on dominance (eg, the skyline operator) or on a utility function defined …

The general problem of answering top-k queries can be modeled using lists of data items
sorted by their local scores. The main algorithm proposed so far for answering top-k queries …

The dramatic growth in the number of application domains that naturally generate
probabilistic, uncertain data has resulted in a need for efficiently supporting complex …

We study the problem of clustering probabilistic graphs. Similar to the problem of clustering
standard graphs, probabilistic graph clustering has numerous applications, such as finding …

We study here fundamental issues involved in top-k query evaluation in probabilistic
databases. We consider simple probabilistic databases in which probabilities are associated …

The problem of modeling and managing uncertain data has received a great deal of interest,
due to its manifold applications in spatial, temporal, multimedia and sensor databases …

Traditional rank-aware processing assumes a dataset that contains available options to
cover a specific need (eg, restaurants, hotels, etc) and users who browse that dataset via top …