Abstract. Temporal databases handle temporal aspects of the objects they describe with an eye to maintaining consistency regarding these temporal aspects. Several techniques have allowed these temporal aspects, along with the regular aspects of the objects, to be defined and queried in an imprecise way. In this paper, a new technique is proposed, which allows using both positive and negative -possibly imprecise-information in querying relational temporal databases. The technique is discussed and the issues which arise are dealt with in a consistent way.

In real world, it is very common that some objects or concepts have properties with a time-variant or timerelated nature. Modelling this kind of objects or concepts in a (relational) database schema is possible, but time-variant and time-related attributes have an impact on the consistency of the entire database and must be appropriately managed. Therefore, temporal database models have been proposed to deal with this problem in the literature. Time can be affected by imprecision, vagueness and / or uncertainty, since existing time measuring devices are inherently imperfect. Additionally, human beings manage time using temporal indications and temporal notions, which may also be imprecise. However, the imperfection in human-used temporal indications is supported by human interpretation, whereas information systems need appropriate support in order to accomplish this task. Several proposals for dealing with such imperfections when modelling temporal data exist. Some of these proposals transform the temporal data into a compact representation but there is not a formal model for managing and handling uncertainty regarding temporal information. In this work we present a novel model to deal with imprecision in valid-time databases together with the definition and implementation of the data manipulation language, DML.

Abstract. Information in databases can be imperfect and this imperfection has several forms and causes. In some cases, a single value should be stored, but it is (partially) unknown. The uncertainty about which value to store leads to the aforementioned imperfection. In temporal databases, uncertainty can arise, concerning which temporal notion needs to be stored. Because in temporal databases, temporal notions influence the consistency with which the database models the reality, this uncertainty has a direct impact on the consistency of the model. To represent this temporal uncertainty, previous works have adapted fuzzy sets with conjunctive interpretation, an approach that might prove misleading. This work presents a model that represents the uncertainty using possibility and necessity measures, which are fuzzy sets with disjunctive interpretations.

Abstract. A temporal database schema models objects or concepts with time-related or time-variant properties and a derived database contains measurements or descriptions concerning these temporal properties. The modelling of temporal information in a temporal database has an impact on the consistency of the database. Of course, this temporal information can be queried. In some cases, information in the query's temporal demand is (partially) unknown. This paper presents a technique for querying a valid-time relation using, among others, a temporal constraint in which the time indication in the temporal expression contains uncertainty and a method for evaluating such queries. This should allow querying using a (partially) unknown temporal demand.

Information systems model parts of reality by representing properties of real-world objects or concepts. As real objects or concepts often have temporal aspects, temporal notions such as time intervals are often represented. However, these may contain imperfections like uncertainties, complicating their representations. A very important purpose of information systems is to be able to query them to retrieve information, but representations of temporal notions containing uncertainties severely complicate querying. Thus, several soft computing techniques have been proposed to represent time intervals subject to uncertainties in a semantically sound way and to reason with them in a semantically sound and useful way. In the presented work, two frameworks designed for this are compared. It is found that, despite slight differences in the way these frameworks represent intervals, they provide the same results when reasoning about time intervals subject to uncertainty.