In today’s uncertain world, imprecision in probabilistic information is often specified by probability intervals. We present here a new database framework for the efficient storage and manipulation of interval probability distribution functions and their associated contextual information. While work on interval probabilities and on probabilistic databases, has appeared before, ours is the first to combine these into a coherent and mathematically sound framework including both standard relational queries and queries based on probability theory. In particular, our query algebra allows the user not only to query existing interval probability distributions, but also to construct new ones by means of conditionalization and marginalization, as we...
There has been a longstanding interest in building systems that can handle uncertain data. Tradition...
Large-scale probabilistic knowledge bases are becoming increasingly important in academia and indust...
Conditionalization, i.e., computation of a conditional probability distribution given a joint probab...
We present a database framework for the efficient storage and manipulation of interval prob-ability ...
A new model for probabilistic databases, using interval-valued conditional probability assessments, ...
A new model for probabilistic databases, using interval-valued conditional probability assessments, ...
Abstract. This paper describes the theoretical framework and implementation of a database management...
ABSTRACT OF DISSERTATION Probabilistic Databases and Their Applications Probabilistic reasoning in d...
Probabilistic databases are motivated by a large and diverse set of applications that need to query ...
Probabilistic reasoning in databases has been an active area of research during the last twodecades....
We define a new query language for data and knowledge bases. This language is able to deal with wei...
Probabilistic databases have received considerable attention recently due to the need for storing un...
Abstract—The inherent uncertainty of data present in numer-ous applications such as sensor databases...
One of the core problems in soft computing is dealing with uncertainty in data. In this paper, we re...
Probabilistic databases are commonly known in the form of the tuple-independent model, where the val...
There has been a longstanding interest in building systems that can handle uncertain data. Tradition...
Large-scale probabilistic knowledge bases are becoming increasingly important in academia and indust...
Conditionalization, i.e., computation of a conditional probability distribution given a joint probab...
We present a database framework for the efficient storage and manipulation of interval prob-ability ...
A new model for probabilistic databases, using interval-valued conditional probability assessments, ...
A new model for probabilistic databases, using interval-valued conditional probability assessments, ...
Abstract. This paper describes the theoretical framework and implementation of a database management...
ABSTRACT OF DISSERTATION Probabilistic Databases and Their Applications Probabilistic reasoning in d...
Probabilistic databases are motivated by a large and diverse set of applications that need to query ...
Probabilistic reasoning in databases has been an active area of research during the last twodecades....
We define a new query language for data and knowledge bases. This language is able to deal with wei...
Probabilistic databases have received considerable attention recently due to the need for storing un...
Abstract—The inherent uncertainty of data present in numer-ous applications such as sensor databases...
One of the core problems in soft computing is dealing with uncertainty in data. In this paper, we re...
Probabilistic databases are commonly known in the form of the tuple-independent model, where the val...
There has been a longstanding interest in building systems that can handle uncertain data. Tradition...
Large-scale probabilistic knowledge bases are becoming increasingly important in academia and indust...
Conditionalization, i.e., computation of a conditional probability distribution given a joint probab...