Expressiveness of Temporal Query Languages: On the Modelling of Intervals, Interval Relationships and States

Storing and retrieving time-related information are important, or even critical, tasks on many areas of Computer Science (CS) and in particular for Artificial Intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: it Templog in the context of temporal deductive databases, and it TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts

An AI Approach to Temporal Indeterminacy in Relational Databases

Time is pervasive of the human way of approaching reality, so that it has been widely studied in many research areas, including Artificial Intelligence (AI) and relational Temporal Databases (TDB). Indeed, while thousands of TDB papers have been devoted to the treatment of determinate time, only few approaches have faced temporal indeterminacy (i.e., \u201cdon\u2019t know exactly when\u201d indeterminacy). In this paper, we propose a new AI-based methodology to approach temporal indeterminacy in relational DBs. We show that typical AI techniques, such as studying the semantics of the representation formalism, and adopting symbolic manipulation techniques based on such a semantics, are very important in the treatment of indeterminate time in relational databases

Discovering the Context of WWW Pages to Improve the Effectiveness of Local Search Engines

International audienceThis work proposes a method of searching for information in hypertext systems representing WWW sites. The method is based on the creation of a 2-level index. The first level of the index is related to information located only inside the nodes. The second level of the index relates to information which is not restricted to one node but encompasses a set of related nodes. The second level is based on the context hierarchy which is a hierarchical organization of the main themes dealt with by the information contained in the site and gives a notion of context to the pages. This notion permits a new operator named context: to be added to the query language allowing the user to better express his information need

GREEN GROCERS: A VIABLE WHOLESALE OUTLET FOR SMALL-VOLUME FRUIT AND VEGETABLE GROWERS?

this paper is to introduce several aspects of time in the heterogeneous world of Informatics and define ontologies for time in different domains of computers and their applications, and not to discuss the very nature of time by itself, a philosophical problem which is to remain open forever. However, in sections 1.1 and 1.2 we are going to give some philosophical and physical background, because, from the richness of often contrasting ideas, developed in the framework of these disciplines, many useful concepts have been derive