Querying uncertain data has become a prominent application due to the proliferation of user-
generated content from social media and of data streams from sensors. When data …

Probabilistic inference over large data sets is a challenging data management problem
since exact inference is generally# P-hard and is most often solved approximately with …

This paper proposes a new approach for approximate evaluation of# P-hard queries with
probabilistic databases. In our approach, every query is evaluated entirely in the database …

Probabilistic inference over large data sets is an increasingly important data management
challenge. The central problem is that exact inference is generally# P-hard, which limits the …

In previous years, the world has gone through several natural disasters like Tsunami,
earthquakes, floods, tornadoes, hurricanes, cyclones, etc., and manufactured disasters such …

Modern recommendation systems leverage some forms of collaborative user or crowd
sourced collection of information. For instance, services like TripAdvisor, Airbnb and …

Many practical scenarios make it necessary to evaluate top-k queries over data items with
partially unknown values. This paper considers a setting where the values are taken from a …

This empirical research seeks to demonstrate that we can decentralize, crowd source and
aggregate signals of uncertainty coming from market agents and organizations, by using the …

The monograph is devoted to the development of mathematical and software for controlling
the movement of tasks in distributed intermodular interaction based on mixed priority …

Top-k Querying of Incomplete Data under Order Constraints Page 1 Introduction Approach
Complexity results Conclusion Top-k Querying of Incomplete Data under Order Constraints …