Complexity of Reasoning over Temporal Data Models

Artale, Alessandro; Kontchakov, Roman; Ryzhikov, Vladislav; Zakharyaschev, Michael

doi:10.1007/978-3-642-16373-9_13

Cited by 7 publications

(12 citation statements)

References 20 publications

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“…We refer to[Artale et al 2010] for a detailed explanation of the various evolution constraints and their naming convention.ACM Transactions on Computational Logic, Vol. V, No.…”

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confidence: 99%

A Cookbook for Temporal Conceptual Data Modelling with Description Logics

Artale

Kontchakov

Ryzhikov

et al. 2014

ACM Trans. Comput. Logic

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We design temporal description logics suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z, <), satisfying the constant domain assumption. Concept and role inclusions of the TBox hold at all moments of time (globally) and data assertions of the ABox hold at specified moments of time. To express temporal constraints of conceptual data models, the languages are equipped with flexible and rigid roles, standard future and past temporal operators on concepts and operators 'always' and 'sometime' on roles. The most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turns out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we construct logics whose complexity ranges between NLOGSPACE and PSPACE. These positive results are obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models.

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“…We refer to[Artale et al 2010] for a detailed explanation of the various evolution constraints and their naming convention.ACM Transactions on Computational Logic, Vol. V, No.…”

mentioning

confidence: 99%

A Cookbook for Temporal Conceptual Data Modelling with Description Logics

Artale

Kontchakov

Ryzhikov

et al. 2014

ACM Trans. Comput. Logic

Self Cite

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show abstract

“…It transpired that, despite their low complexity, DL-Lite logics were capable of representing basic conceptual data modelling constructs [7,2], and gave rise to the W3C standard ontology language OWL 2 QL for OBDA. One possible application of the results obtained in this paper lies in temporal conceptual modelling and temporal OBDA [5]. Temporal description logics (and other many-dimensional logics) are notorious for their bad computational properties [16,19].…”

Section: Discussionmentioning

confidence: 99%

“…So, suppose conditions (L 1 )-(L 2 ) hold. We construct an interpretation satisfying (5). By (L 1 ), both Ψ ∧ ✷ * Φ and n Ψ ′ ∧ ✷ * Φ are consistent.…”

Section: The Clausal Normal Form For Ltlmentioning

confidence: 99%

The Complexity of Clausal Fragments of LTL

Artale

Kontchakov

Ryzhikov

et al. 2013

Logic for Programming, Artificial Intelligence, and Reasoning

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Abstract. We introduce and investigate a number of fragments of propositional temporal logic LTL over the flow of time (Z, <). The fragments are defined in terms of the available temporal operators and the structure of the clausal normal form of the temporal formulas. We determine the computational complexity of the satisfiability problem for each of the fragments, which ranges from NLogSpace to PTime, NP and PSpace.

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“…As Artale et al [2010] point out for temporary entities, events may also have constraints on their the length of their lifetime or the largest locality where they may occur or some combination of both. For example, one can have the constraint: "for events e 1 , e 2 , causes(e 1 , e 2 ) ⇒ follows(e 2 , e 1 )."…”

Section: Coveragementioning

confidence: 99%

Managing Event Information: Modeling, Retrieval, and Applications

Gupta¹,

Jain²

2011

Synthesis Lectures on Data Management

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means-electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. Managing Event Information: Modeling, Retrieval, and Applications ABSTRACTWith the proliferation of citizen reporting, smart mobile devices, and social media, an increasing number of people are beginning generate information about events they observe and participate in. A significant fraction of this information contain multimedia data to share the experience with their audience. A systematic information modeling and management framework is necessary to capture this widely heterogeneous, schemaless, potentially humongous information produced by many different people. This book is an attempt to examine the modeling, storage, querying, and applications of such an event management system in a holistic manner. It uses a semantic-web style graph-based view of events, and shows how this event model, together with its query facility, can be used toward emerging applications like semi-automated storytelling.

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Complexity of Reasoning over Temporal Data Models

Cited by 7 publications

References 20 publications

A Cookbook for Temporal Conceptual Data Modelling with Description Logics

A Cookbook for Temporal Conceptual Data Modelling with Description Logics

The Complexity of Clausal Fragments of LTL

Managing Event Information: Modeling, Retrieval, and Applications

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