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A Probabilistic Relational Algebra for the Integration of Information Retrieval and Database Systems
 ACM Transactions on Information Systems
, 1994
"... We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. Here tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Based on intensional semantics, the tuple weights of the result of a PRA expression ..."
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Cited by 173 (30 self)
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We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. Here tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Based on intensional semantics, the tuple weights of the result of a PRA expression always confirm to the underlying probabilistic model. We also show for which expressions extensional semantics yields the same results. Furthermore, we discuss complexity issues and indicate possibilities for optimization. With regard to databases, the approach allows for representing imprecise attribute values, whereas for information retrieval, probabilistic document indexing and probabilistic search term weighting can be modelled. As an important extension, we introduce the concept of vague predicates which yields a probabilistic weight instead of a Boolean value, thus allowing for queries with vague selection conditions. So PRA implements uncertainty and vagueness in combination with the...
ProTDB: Probabilistic data in XML
 In Proceedings of the 28th VLDB Conference
, 2002
"... Abstract Whereas traditional databases manage onlydeterministic information, many applications that use databases involve uncertain data.This paper presents a Probabilistic Tree Data Base (ProTDB) to manage probabilistic data,represented in XML. Our approach differs from previous effortsto develop p ..."
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Cited by 44 (2 self)
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Abstract Whereas traditional databases manage onlydeterministic information, many applications that use databases involve uncertain data.This paper presents a Probabilistic Tree Data Base (ProTDB) to manage probabilistic data,represented in XML. Our approach differs from previous effortsto develop probabilistic relational systems in that we build a probabilistic XML database.This design is driven by application needs that involve data not readily amenable to a relational representation. XML data poses several modeling challenges: due to its structure, dueto the possibility of uncertainty association at multiple granularities, and due to the possibility of missing and repeated subelements. We present a probabilistic XML model thataddresses all of these challenges. We devise an implementation of XML query operationsusing our probability model, and demonstrate the efficiency of our implementation experimentally. We have used ProTDB to manage data fromtwo application areas: protein chemistry data from the bioinformatics domain, and information extraction data obtained from the web using a natural language analysis system. Wepresent a brief case study of the latter to demonstrate the value of probabilistic XMLdata management.
Probabilistic Object Bases
 ACM Transactions on Database Systems
, 2001
"... There are many applications where an object oriented data model is a good way of representing and querying data. However, current object database systems are unable to handle the case of objects whose attributes are uncertain. In this paper, extending previous pioneering work by Kornatzky and Shi ..."
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Cited by 23 (7 self)
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There are many applications where an object oriented data model is a good way of representing and querying data. However, current object database systems are unable to handle the case of objects whose attributes are uncertain. In this paper, extending previous pioneering work by Kornatzky and Shimony, we develop an extension of the relational algebra to the case of object bases with uncertainty. We propose concepts of consistency for such object bases, together with an NPcompleteness result, and classes of probabilistic object bases for which consistency is polynomially checkable. In addition, as certain operations involve conjunctions and disjunctions of events, and as the probability of conjunctive and disjunctive events depends both on the probabilities of the primitive events involved as well as on what is known (if anything) about the relationship between the events, we show how all our algebraic operations may be performed under arbitrary probabilistic conjunction and ...
A Data Model and Algebra for Probabilistic Complex Values
 Annals of Mathematics and Artificial Intelligence
, 2000
"... We present a probabilistic data model for complex values. More precisely, we introduce probabilistic complex value relations, which combine the concept of probabilistic relations with the idea of complex values in a uniform framework. We elaborate a modeltheoretic definition of probabilistic combina ..."
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Cited by 17 (4 self)
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We present a probabilistic data model for complex values. More precisely, we introduce probabilistic complex value relations, which combine the concept of probabilistic relations with the idea of complex values in a uniform framework. We elaborate a modeltheoretic definition of probabilistic combination strategies, which has a rigorous foundation on probability theory. We then define an algebra for querying database instances, which comprises the operations of selection, projection, renaming, join, Cartesian product, union, intersection, and difference. We prove that our data model and algebra for probabilistic complex values generalizes the classical relational data model and algebra. Moreover, we show that under certain assumptions, all our algebraic operations are tractable. We finally show that most of the query equivalences of classical relational algebra carry over to our algebra on probabilistic complex value relations. Hence, query optimization techniques for classical relational algebra can easily be applied to optimize queries on probabilistic complex value relations. Keywords: Complex value databases, probabilistic databases, data model, relational algebra, query languages. AMS Subject classification: Primary 68P15, 68P20; Secondary 68T30, 68T37 1.
Efficient and Flexible Search Using Text and Metadata
, 2000
"... Digital libraries and intranets often include a wide mix of document types, ranging from catalog entries consisting entirely of metadata, to documents with both substantial text content and thorough metadata tagging, to documents with hardly any metadata at all. This paper describes a retrieval mode ..."
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Cited by 5 (3 self)
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Digital libraries and intranets often include a wide mix of document types, ranging from catalog entries consisting entirely of metadata, to documents with both substantial text content and thorough metadata tagging, to documents with hardly any metadata at all. This paper describes a retrieval model for use in such environments which can accommodate searches in a variety of metadata schemata. It uses both content search and threevalued satisfaction of metadata constraints (yes, no and maybe). In this model queries may include explicit metadata constraints as well as free text terms. The latter may impose additional implicit constraints as well as contributing to content relevance scores. Result lists are presented in tiers, in which the top tier corresponds to full satisfaction of all constraints and subsequent tiers are derived by progressive relaxation of constraints. An efficient and scalable implementation of the model has been developed and has been in production use on the Australian National University intranet since July 1999. Details of the implementation are given and performance results are presented.
Extension of the Relational Algebra to Probabilistic Complex Values
 In Proceedings of the International Symposium on Foundations of Information and Knowledge Systems (FoIKS 2000), volume 1762 of LNCS
, 2000
"... We present a probabilistic data model for complex values. ..."