Dynamic Domain Sizes in Temporal Probabilistic Relational Models.

Nils Finke; Tanya Braun; Marcel Gehrke; Ralf Möller

doi:10.32473/flairs.v34i1.128466

Dynamic Domain Sizes in Temporal Probabilistic Relational Models.

Nils Finke, Tanya Braun, Marcel Gehrke, Ralf Möller

Abstract

Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.

Originalsprache	Englisch
Titel	FLAIRS Conference
Erscheinungsdatum	2021
DOIs	https://doi.org/10.32473/flairs.v34i1.128466
Publikationsstatus	Veröffentlicht - 2021

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title = "Dynamic Domain Sizes in Temporal Probabilistic Relational Models.",

abstract = "Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.",

author = "Nils Finke and Tanya Braun and Marcel Gehrke and Ralf M{\"o}ller",

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AU - Braun, Tanya

AU - Gehrke, Marcel

AU - Möller, Ralf

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PY - 2021

Y1 - 2021

N2 - Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.

AB - Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.

U2 - 10.32473/flairs.v34i1.128466

DO - 10.32473/flairs.v34i1.128466

M3 - Conference contribution

BT - FLAIRS Conference

ER -

Dynamic Domain Sizes in Temporal Probabilistic Relational Models.

Abstract

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