Orthogonal Operators for User-Defined Symbolic Periodicities

Egidi, Lavinia; Terenziani, Paolo

doi:10.1007/978-3-540-30106-6_14

Cited by 7 publications

(28 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Essentially, one must require that the periodicity can't be split in a finite number of periodicities that have adjacent intervals (cf. [14,15]). …”

Section: Propertiesmentioning

confidence: 96%

“…This way one can represent structures like in Leban et al's approach (see [22]) where, through nested applications of dicing operators such as F<_, infinite periodicities can be introduced at any level of nesting. 3 We choose not to show here the corresponding PrA formula because we question the usefulness of such collections [14,15].…”

Section: More Complex Periodicitiesmentioning

confidence: 98%

“…For instance, the periodical pattern can be nested within the collection. We show a formula more general than (14) in which the repeating pattern is an order-k subcollection of an order n collection:…”

Section: More Complex Periodicitiesmentioning

confidence: 99%

“…Before we start discussing the semantics, we recall here a set of five properties of periodicities that we have identified in [12,15] via an analysis of many approaches in the literature and a large corpus of examples (see also [14]). The introduction of these properties allows a clean and systematic treatment of periodicities.…”

Section: Propertiesmentioning

confidence: 99%

“…For a more detailed presentation, see [14,15]. In the same paper it is also shown that Orthogonality allows a modular approach, in that the problems posed and the features added by each one of the properties can be dealt with separately.…”

Section: Propertiesmentioning

confidence: 99%

See 4 more Smart Citations

A mathematical framework for the semantics of symbolic languages representing periodic time

Egidi

Terenziani

2006

Ann Math Artif Intell

Self Cite

View full text Add to dashboard Cite

In several areas, including Temporal DataBases (TDB), Presburger arithmetic has been chosen as a standard reference for the semantics of languages representing periodic time, and to study their expressiveness. On the other hand, the proposal of most symbolic languages in the AI literature has not been paired with an adequate semantic counterpart, making the task of studying the expressiveness of such languages and of comparing them a very complex one. In this paper, we first define a representation language which enables us to handle each temporal point as a complex object enriched with all the structure it is immersed in, and then we use it in order to provide a Presburger semantics for classes of languages coping with periodicity. Finally, we use the semantics to compare a few AI and TDB approaches.

show abstract

“…Essentially, one must require that the periodicity can't be split in a finite number of periodicities that have adjacent intervals (cf. [14,15]). …”

Section: Propertiesmentioning

confidence: 96%

Section: More Complex Periodicitiesmentioning

confidence: 98%

Section: More Complex Periodicitiesmentioning

confidence: 99%

Section: Propertiesmentioning

confidence: 99%

Section: Propertiesmentioning

confidence: 99%

See 3 more Smart Citations

A mathematical framework for the semantics of symbolic languages representing periodic time

Egidi

Terenziani

2006

Ann Math Artif Intell

Self Cite

View full text Add to dashboard Cite

show abstract

An intensional approach for periodic data in relational databases

et al. 2013

Self Cite

View full text Add to dashboard Cite

Periodic data play a major role in many application domains, spanning from manufacturing to office automation, from scheduling to data broadcasting. In many of such domains, the huge number of repetitions make the goal of explicitly storing and accessing such data very challenging. In this paper, we propose a new methodology, based on an implicit representation of periodic data. The representation model we propose captures the notion of periodic granularity provided by the temporal database glossary, and is a consistent extension of the TSQL2 temporal relational data model. On top of our new data model, we propose a suitable indexing technique. We define the algebraic operators, and introduce access algorithms to cope with them, proving that they are correct and complete with respect to the traditional explicit approach. We also propose an experimental evaluation of our approach. Keywords temporal databases • periodic data • implicit representation 1 Introduction Periodic events seem to be an intrinsic part of our life, and of our way of perceiving reality. Day and nights repeat at regular periodic patterns, as well as seasons,

show abstract

Knowledge Representation, Reasoning and Integration Using Temporal Logic with Clocks

2008

View full text Add to dashboard Cite

Representation, reasoning about and integrating knowledge based on multiple time granularities in knowledge-based systems is important, especially when talking about events that take place in the real world. Formal approaches based on temporal logics have been successfully applied in many application domains of knowledge-based systems where the evolution of a system and its environment through time is central. This paper presents a methodology based on temporal logic to deal with knowledge based on multiple time granularities in knowledge-based systems. The temporal logic we consider is especially suitable for modelling events with different rates and/or scales of progress. The methodology includes an approach to the representation of timing systems, a method used for representing facts and rules in a knowledge-based system that involve multiple time granularities using temporal logic, and several deductive reasoning techniques.

show abstract

Orthogonal Operators for User-Defined Symbolic Periodicities

Cited by 7 publications

References 8 publications

A mathematical framework for the semantics of symbolic languages representing periodic time

A mathematical framework for the semantics of symbolic languages representing periodic time

An intensional approach for periodic data in relational databases

Knowledge Representation, Reasoning and Integration Using Temporal Logic with Clocks

Contact Info

Product

Resources

About