Stable Semantics of Temporal Deductive Databases

We define a preferential semantics based on stable generated models for a very general class of temporal deductive databases. We allow two kinds of temporal information to be represented and queried: timepoint and timestamp formulas, and show how each of them can be translated into the other. Because of their generality, our formalism and our semantics can serve as a basis for comparing and extending other temporal deductive database frameworks

The Events method for temporal integrity constraint handling in bitemporal deductive databases

A bitemporal deductive database is a deductive database that supports valid and transaction time. A temporal integrity constraint deals with only valid time, only transaction time or both times. A set of facts to be einserted and deleted in a bitemporal deductive database can be done in a past, present or future valid time and at current transaction time. The temporal integrity constraint handling in bitemporal deductive databases causes that the maintenance of consistency becomes more complex than another databases. The $events method$is based on applying transition and event rules, which explicitly define the insertions and deletions given by a database update. In the conceptual model, we augment the database with temporal transition and event rules and then standard SLDNF-resolution can be used to verify that a transaction does not violate any temporal integrity constraint. In the representational data model, we use time point-based intervals to store temporal information. In this paper, we adapt the$$$events method$$$ for handling temporal integrity constraints. Finally, we present the interaction between the above-mentioned conceptual and representational data models.Postprint (published version

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

Restriccions d'integritat temporals en bases de dades deductives bitemporals

The aim of this report is to introduce a taxonomy of temporal integrity constraints, focused on the bitemporal deductive database area, to get a better understanding of why they are required, their behavior and the best way to define them using first order logic. To meet these goals, we have analyzed temporal integrity constraints taxonomies existing on the temporal database area and deeply related areas as multiversion databases. Thus, the mentioned legacy work has been adapted and developed to cover the scope of the bitemporal deductive databases.Postprint (published version

A logic programming framework for modeling temporal objects

Published versio

Complex Event Processing (CEP)

Event-driven information systems demand a systematic and automatic processing of events. Complex Event Processing (CEP) encompasses methods, techniques, and tools for processing events while they occur, i.e., in a continuous and timely fashion. CEP derives valuable higher-level knowledge from lower-level events; this knowledge takes the form of so called complex events, that is, situations that can only be recognized as a combination of several events. 1 Application Areas Service Oriented Architecture (SOA), Event-Driven Architecture (EDA), cost-reductions in sensor technology and the monitoring of IT systems due to legal, contractual, or operational concerns have lead to a significantly increased generation of events in computer systems in recent years. This development is accompanied by a demand to manage and process these events in an automatic, systematic, and timely fashion. Important application areas for Complex Event Processing (CEP) are the following

Towards Intelligent Databases

This article is a presentation of the objectives and techniques of deductive databases. The deductive approach to databases aims at extending with intensional definitions other database paradigms that describe applications extensionaUy. We first show how constructive specifications can be expressed with deduction rules, and how normative conditions can be defined using integrity constraints. We outline the principles of bottom-up and top-down query answering procedures and present the techniques used for integrity checking. We then argue that it is often desirable to manage with a database system not only database applications, but also specifications of system components. We present such meta-level specifications and discuss their advantages over conventional approaches

Cooperative answers in database systems

A major concern of researchers who seek to improve human-computer communication involves how to move beyond literal interpretations of queries to a level of responsiveness that takes the user's misconceptions, expectations, desires, and interests into consideration. At Maryland, we are investigating how to better meet a user's needs within the framework of the cooperative answering system of Gal and Minker. We have been exploring how to use semantic information about the database to formulate coherent and informative answers. The work has two main thrusts: (1) the construction of a logic formula which embodies the content of a cooperative answer; and (2) the presentation of the logic formula to the user in a natural language form. The information that is available in a deductive database system for building cooperative answers includes integrity constraints, user constraints, the search tree for answers to the query, and false presuppositions that are present in the query. The basic cooperative answering theory of Gal and Minker forms the foundation of a cooperative answering system that integrates the new construction and presentation methods. This paper provides an overview of the cooperative answering strategies used in the CARMIN cooperative answering system, an ongoing research effort at Maryland. Section 2 gives some useful background definitions. Section 3 describes techniques for collecting cooperative logical formulae. Section 4 discusses which natural language generation techniques are useful for presenting the logic formula in natural language text. Section 5 presents a diagram of the system