Context for Ubiquitous Data Management

In response to the advance of ubiquitous computing technologies, we believe that for computer systems to be ubiquitous, they must be context-aware. In this paper, we address the impact of context-awareness on ubiquitous data management. To do this, we overview different characteristics of context in order to develop a clear understanding of context, as well as its implications and requirements for context-aware data management. References to recent research activities and applicable techniques are also provided

An Approach to Temporal-Aware Procurement of Web Services

Es también una ponencia de: International Conference on Service-Oriented Computing: ICSOC 2005: Service-Oriented Computing - ICSOC 2005 pp 170-184. book ISBN: 978-3-540-30817-1 e-ISBN: 978-3-540-32294-8In the context of web service procurement (WSP), temporal–awareness refers to managing service demands and offers which are subject to validity periods, i.e. their evaluation depends not only on quality of service (QoS) values but also on time. For example, the QoS of some web services can be considered critical in working hours (9:00 to 17:00 from Monday to Friday) and irrelevant at any other moment. Until now, the expressiveness of such temporal–aware specifications has been quite limited. As far as we know, most proposals have considered validity periods to be composed of a single temporal interval. Other proposals, which could allow more expressive time–dependent specifications, have not performed a detailed study about all the underlying complexities of such approach, in spite of the fact that dealing with complex expressions on temporality is not a trivial task at all. As a matter of fact, it requires a special design of the so–called procurement tasks (consistency and conformance checking, and optimal selection). In this paper, we present a constraint–based approach to temporal–aware WSP. Using constraints allows a great deal of expressiveness, so that not only demands and offers can be assigned validity periods but also their conditions can be assigned (possibly multiple) validity temporal subintervals. Apart from revising the semantics of procurement tasks, which we previously presented in the first edition of the ICSOC conferences, we also introduce the notion of the covering set of a demand, a topic which is closely related to temporality.Ministerio de Ciencia y Tecnología TIC2003-02737-C02-0

Representing Imprecise Time Intervals in OWL 2

International audienceRepresenting and reasoning on imprecise temporal information is a common requirement in the field of Semantic Web. Many works exist to represent and reason on precise temporal information in OWL; however, to the best of our knowledge, none of these works is devoted to imprecise temporal time intervals. To address this problem, we propose two approaches: a crisp-based approach and a fuzzy-based approach. (1) The first approach uses only crisp standards and tools and is modelled in OWL 2. We extend the 4D-fluents model, with new crisp components, to represent imprecise time intervals and qualitative crisp interval relations. Then, we extend the Allen’s interval algebra to compare imprecise time intervals in a crisp way and inferences are done via a set of SWRL rules. (2) The second approach is based on fuzzy sets theory and fuzzy tools and is modelled in Fuzzy-OWL 2. The 4D-fluents approach is extended, with new fuzzy components, in order to represent imprecise time intervals and qualitative fuzzy interval relations. The Allen’s interval algebra is extended in order to compare imprecise time intervals in a fuzzy gradual personalized way. Inferences are done via a set of Mamdani IF-THEN rules

A Temporal Web Ontology Language

The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies on the Semantic Web. In this paper, we present a temporal extension of the very expressive fragment SHIN(D) of the OWL-DL language resulting in the tOWL language. Through a layered approach we introduce 3 extensions: i) Concrete Domains, that allows the representation of restrictions using concrete domain binary predicates, ii) Temporal Representation, that introduces timepoints, relations between timepoints, intervals, and Allen’s 13 interval relations into the language, and iii) TimeSlices/Fluents, that implements a perdurantist view on individuals and allows for the representation of complex temporal aspects, such as process state transitions. We illustrate the expressiveness of the newly introduced language by providing a TBox representation of Leveraged Buy Out (LBO) processes in financial applications and an ABox representation of one specific LBO

A cookbook for temporal conceptual data modelling with description logic

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. In the temporal dimension, they capture future and past temporal operators on concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were 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