A framework for management of semistructured probabilistic data

Wenzhong Zhao; Alex Dekhtyar; Judy Goldsmith

doi:10.1007/s10844-005-0197-8

A framework for management of semistructured probabilistic data

Wenzhong Zhao
, Alex Dekhtyar
, Judy Goldsmith

Producción científica: Article › revisión exhaustiva

11 Citas (Scopus)

Resumen

This paper describes the theoretical framework and implementation of a database management system for storing and manipulating diverse probability distributions of discrete random variables with finite domains, and associated information. A formal Semistructured Probabilistic Object (SPO) data model and a Semistructured Probabilistic Query Algebra (SP-algebra) are proposed. The SP-algebra supports standard database queries as well as some specific to probabilities, such as conditionalization and marginalization. Thus, the Semistructured Probabilistic Database may be used as a backend to any application that involves the management of large quantities of probabilistic information, such as building stochastic models. The implementation uses XML encoding of SPOs to facilitate communication with diverse applications. The database management system has been implemented on top of a relational DBMS. The translation of SP-algebra queries into relational queries are discussed here, and the results of initial experiments evaluating the system are reported.

Idioma original	English
Páginas (desde-hasta)	293-332
Número de páginas	40
Publicación	Journal of Intelligent Information Systems
Volumen	25
N.º	3
DOI	https://doi.org/10.1007/s10844-005-0197-8
Estado	Published - nov 2005

Nota bibliográfica

Funding Information:
This work was partially supported by NSF grants CCR-0100040, ITR-0325063, and ITR-0219924. We’d like to thank the anonymous reviewers of our conference papers, whose suggestions improved this paper. We also want to thank V.S. Subrahmanian for helpful comments and the students working in the Bayesian Advisor group for their input at various stages, and their fabulous energy.

Financiación

This work was partially supported by NSF grants CCR-0100040, ITR-0325063, and ITR-0219924. We’d like to thank the anonymous reviewers of our conference papers, whose suggestions improved this paper. We also want to thank V.S. Subrahmanian for helpful comments and the students working in the Bayesian Advisor group for their input at various stages, and their fabulous energy.

Financiadores	Número del financiador
National Science Foundation Arctic Social Science Program	ITR-0219924, CCR-0100040, ITR-0325063

ASJC Scopus subject areas

Software
Information Systems
Hardware and Architecture
Computer Networks and Communications
Artificial Intelligence

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10.1007/s10844-005-0197-8

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title = "A framework for management of semistructured probabilistic data",

abstract = "This paper describes the theoretical framework and implementation of a database management system for storing and manipulating diverse probability distributions of discrete random variables with finite domains, and associated information. A formal Semistructured Probabilistic Object (SPO) data model and a Semistructured Probabilistic Query Algebra (SP-algebra) are proposed. The SP-algebra supports standard database queries as well as some specific to probabilities, such as conditionalization and marginalization. Thus, the Semistructured Probabilistic Database may be used as a backend to any application that involves the management of large quantities of probabilistic information, such as building stochastic models. The implementation uses XML encoding of SPOs to facilitate communication with diverse applications. The database management system has been implemented on top of a relational DBMS. The translation of SP-algebra queries into relational queries are discussed here, and the results of initial experiments evaluating the system are reported.",

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N2 - This paper describes the theoretical framework and implementation of a database management system for storing and manipulating diverse probability distributions of discrete random variables with finite domains, and associated information. A formal Semistructured Probabilistic Object (SPO) data model and a Semistructured Probabilistic Query Algebra (SP-algebra) are proposed. The SP-algebra supports standard database queries as well as some specific to probabilities, such as conditionalization and marginalization. Thus, the Semistructured Probabilistic Database may be used as a backend to any application that involves the management of large quantities of probabilistic information, such as building stochastic models. The implementation uses XML encoding of SPOs to facilitate communication with diverse applications. The database management system has been implemented on top of a relational DBMS. The translation of SP-algebra queries into relational queries are discussed here, and the results of initial experiments evaluating the system are reported.

AB - This paper describes the theoretical framework and implementation of a database management system for storing and manipulating diverse probability distributions of discrete random variables with finite domains, and associated information. A formal Semistructured Probabilistic Object (SPO) data model and a Semistructured Probabilistic Query Algebra (SP-algebra) are proposed. The SP-algebra supports standard database queries as well as some specific to probabilities, such as conditionalization and marginalization. Thus, the Semistructured Probabilistic Database may be used as a backend to any application that involves the management of large quantities of probabilistic information, such as building stochastic models. The implementation uses XML encoding of SPOs to facilitate communication with diverse applications. The database management system has been implemented on top of a relational DBMS. The translation of SP-algebra queries into relational queries are discussed here, and the results of initial experiments evaluating the system are reported.

KW - Data models

KW - Probabilistic databases

KW - Query algebras

KW - Semistructured data

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A framework for management of semistructured probabilistic data

Resumen

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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Huella

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