Extension of the Relational Algebra to Probabilistic Complex Values

Eiter, Thomas; Lukasiewicz, Thomas; Walter, Michael

doi:10.1007/3-540-46564-2_7

Cited by 24 publications

(32 citation statements)

References 13 publications

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“…An alternative to the fuzzy sets framework exists in the form of probabilistic methods (e.g., [19,39]). A probabilisticbased approach assumes that one has incomplete knowledge about the portion of the real world being modeled.…”

Section: Modeling Alternativesmentioning

confidence: 99%

A framework for modeling and evaluating automatic semantic reconciliation

et al. 2005

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Abstract. The introduction of the Semantic Web vision and the shift toward machine understandable Web resources has unearthed the importance of automatic semantic reconciliation. Consequently, new tools for automating the process were proposed. In this work we present a formal model of semantic reconciliation and analyze in a systematic manner the properties of the process outcome, primarily the inherent uncertainty of the matching process and how it reflects on the resulting mappings. An important feature of this research is the identification and analysis of factors that impact the effectiveness of algorithms for automatic semantic reconciliation, leading, it is hoped, to the design of better algorithms by reducing the uncertainty of existing algorithms. Against this background we empirically study the aptitude of two algorithms to correctly match concepts. This research is both timely and practical in light of recent attempts to develop and utilize methods for automatic semantic reconciliation.

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Section: Modeling Alternativesmentioning

confidence: 99%

A framework for modeling and evaluating automatic semantic reconciliation

et al. 2005

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“…They also study the issues of completeness and closure under various alternative models for representing uncertainty, though their notion of uncertainty does not currently include probabilities. In recent years, there has also been renewed interest in probabilistic extensions of object-relational [16] and XML data models [29,24].…”

Section: Related Workmentioning

confidence: 99%

Representing and Querying Correlated Tuples in Probabilistic Databases

Sen

Deshpande

2007

2007 IEEE 23rd International Conference on Data Engineering

162

186

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Probabilistic databases have received considerable attention recently due to the need for storing uncertain data produced by many real world applications. The widespread use of probabilistic databases is hampered by two limitations: (1) current probabilistic databases make simplistic assumptions about the data (e.g., complete independence among tuples) that make it difficult to use them in applications that naturally produce correlated data, and (2) most probabilistic databases can only answer a restricted subset of the queries that can be expressed using traditional query languages. We address both these limitations by proposing a framework that can represent not only probabilistic tuples, but also correlations that may be present among them. Our proposed framework naturally lends itself to the possible world semantics thus preserving the precise query semantics extant in current probabilistic databases. We develop an efficient strategy for query evaluation over such probabilistic databases by casting the query processing problem as an inference problem in an appropriately constructed probabilistic graphical model. We present several optimizations specific to probabilistic databases that enable efficient query evaluation. We validate our approach by presenting an experimental evaluation that illustrates the effectiveness of our techniques at answering various queries using real and synthetic datasets.

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“…Another approach towards combination strategies, which concentrates especially on a clean probabilistic semantics is given in [Eiter et al, 2001]. Both approaches generalise the well known concepts of triangular norms and co-norms which are used for similar purposes.…”

Section: Given Two Probability Intervalsmentioning

confidence: 99%

Heterogeneous temporal probabilistic agents

Dix

Kraus

Subrahmanian

2006

ACM Trans. Comput. Logic

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To date, there has been no work on temporal probabilistic agent reasoning on top of heterogeneous legacy databases and software modules. We will define the concept of a heterogeneous temporal probabilistic (HTP) agent. Such agents can be built on top of existing databases, data structures, and software code bases without explicitly accessing the internal code of those systems and can take actions compatible with a policy or operating principles specified by an agent developer. We will develop a formal semantics for such agents through the notion of a feasible temporal probabilistic status interpretation (FTPSI for short). Intuitively, an FTPSI specifies what all an HTP agent is permitted/forbidden/obliged to do at various times t. As changes occur in the environment, the HTP agent must compute a new FTPSI. HTP agents continuously compute FTPSI's in order to determine what they should do and hence, the problem of computing FTPSI's is very important. We give a sound and complete algorithm to compute FTPSI's for a very large class of HTP agents called strict HTP agents. In a given state, many FTPSI's may exist. These represent alternative courses of action that the HTP agent can take. We provide a notion of an optimal FTPSI that selects an FTPSI optimising an objective function and give a sound and complete algorithm to compute an optimal FTPSI.

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Extension of the Relational Algebra to Probabilistic Complex Values

Cited by 24 publications

References 13 publications

A framework for modeling and evaluating automatic semantic reconciliation

A framework for modeling and evaluating automatic semantic reconciliation

Representing and Querying Correlated Tuples in Probabilistic Databases

Heterogeneous temporal probabilistic agents

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