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PXML: A probabilistic semistructured data model and algebra
 In ICDE
, 2003
"... ehung,getoor,vs£ Despite the recent proliferation of work on semistructured data models, there has been little work to date on supporting uncertainty in these models. In this paper, we propose a model for probabilistic semistructured data (PSD). The advantage of our approach is that it supports a fl ..."
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Cited by 59 (4 self)
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ehung,getoor,vs£ Despite the recent proliferation of work on semistructured data models, there has been little work to date on supporting uncertainty in these models. In this paper, we propose a model for probabilistic semistructured data (PSD). The advantage of our approach is that it supports a flexible representation that allows the specification of a wide class of distributions over semistructured instances. We provide two semantics for the model and show that the semantics are probabilistically coherent. Next, we develop an extension of the relational algebra to handle probabilistic semistructured data and describe efficient algorithms for answering queries that use this algebra. Finally, we present experimental results showing the efficiency of our algorithms. 1
Conditionalization for Interval Probabilities
 PROC. WORKSHOP ON CONDITIONALS, INFORMATION, AND INFERENCE
, 2002
"... Conditionalization, i.e., computation of a conditional probability distribution given a joint probability distribution of two or more random variables is an important operation in some probabilistic database models. While the computation of the conditional probability distribution is straightforward ..."
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Cited by 5 (3 self)
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Conditionalization, i.e., computation of a conditional probability distribution given a joint probability distribution of two or more random variables is an important operation in some probabilistic database models. While the computation of the conditional probability distribution is straightforward when the exact point probabilities are involved, it is often the case that such exact point probability distributions of random variables are not known, but are known to lie in a particular interval. This paper investigates the conditionalization operation for interval probability distribution functions under a possible world semantics. In particular, given a joint probability distribution of two or more random variables, where the probability of each outcome is represented as an interval, we (i) provide formal modeltheoretic semantics; (ii) define the operation of conditionalization and (iii) provide a closed form solution/efficient algorithm to compute the conditional probability distribution.
ProbSem: A Probabilistic Semistructured Database Model
"... Recent interest in semistructured data has led to a proliferation of XMLbased standards which encompass applications ranging from multimedia applications and sensor data processing applications to financial applications and myriads of other more traditional applications. When semistructured paradig ..."
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Cited by 2 (0 self)
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Recent interest in semistructured data has led to a proliferation of XMLbased standards which encompass applications ranging from multimedia applications and sensor data processing applications to financial applications and myriads of other more traditional applications. When semistructured paradigms are used to store sensor data and multimedia (e.g. image) data, we need to be able to handle uncertainty as sensor readings and image processing methods often yield uncertain results. In this paper, we propose the concept of probabilistic semistructured (PS) databases. We propose a global notion of consistency as well as a local one and show that the two coincide.
Conditionalization for Interval Probabilities
, 2002
"... Abstract Conditionalization, i.e., computation of a conditional probability distribution given a joint probabilitydistribution of two or more random variables is an important operation in some probabilistic database models. While the computation of the conditional probability distribution is straigh ..."
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Abstract Conditionalization, i.e., computation of a conditional probability distribution given a joint probabilitydistribution of two or more random variables is an important operation in some probabilistic database models. While the computation of the conditional probability distribution is straightforward when theexact point probabilities are involved, it is often the case that such exact point probability distributions of random variables are not known, but are known to lie in a particular interval.This paper investigates the conditionalization operation for interval probability distribution functions under a possible world semantics. In particular, given a joint probability distribution of two or morerandom variables, where the probability of each outcome is represented as an interval, we (i) provide formal modeltheoretic semantics; (ii) define the operation of conditionalization and (iii) provide a closedform solution/efficient algorithm to compute the conditional probability distribution.