Christophe Billiet

Bipolar fuzzy querying of temporal databases

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.

A relational model for the possibilistic valid-time approach

In real world, it is very common that some objects or concepts have properties with a time-variant or time-related 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.

A Possibilistic Valid-Time Model

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.

The Role of Computational Intelligence in Temporal Information Retrieval: A Survey of Imperfect Time in Information Systems

In existing literature, many proposals have concerned the modeling or handling of imperfection in time in information systems. However, although reviews, surveys and overviews about either imperfection or time in information systems exist, no reviews, surveys or overviews about imperfection in time in information systems seem to exist. The main contribution of the work presented in this paper is to attempt to fill this void by presenting a survey of some existing scientific contributions dealing with time in information systems. A more modest contribution is an attempt at identifying some open research challenges or opportunities concerning imperfection in time in information systems.

Aspects of Dealing with Imperfect Data in Temporal Databases

In reality, some objects or concepts have properties with a time-variant or time-related nature. Modelling these kinds 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. Therefore, temporal database models have been proposed to deal with this. Time itself can be at the source of imprecision, vagueness and uncertainty, since existing time measuring devices are inherently imperfect. Accordingly, human beings manage time using temporal indications and temporal notions, which may contain imprecision, vagueness and uncertainty. However, the imperfection in human-used temporal indications is supported by human interpretation, whereas information systems need extraordinary support for this. Several proposals for dealing with such imperfections when modelling temporal aspects exist. Some of these proposals consider the basis of the system to be the conversion of the specificity of temporal notions between used temporal expressions. Other proposals consider the temporal indications in the used temporal expressions to be the source of imperfection. In this chapter, an overview is given, concerning the basic concepts and issues related to the modelling of time as such or in (relational) database models and the imperfections that may arise during or as a result of this modelling. Next to this, a novel and currently researched technique for handling some of these imperfections is presented.

Evaluating Possibilistic Valid-Time Queries

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.

Combining Uncertainty and Vagueness in Time Intervals

Database systems contain data representing properties of real-life objects or concepts. Many of these data represent time indications and such time indications are often subject to imperfections. Although several existing proposals deal with the modeling of uncertainty or vagueness in time indications in database systems, only a few of them summarily examine the interpretation and semantics of such imperfections. The work presented in this paper starts at a more thorough examination of the semantics and modeling of uncertainty or vagueness in time intervals in database systems and presents methods to model combinations of uncertainty and vagueness in time intervals in database systems, based on examinations of their requisite interpretations.

A Comparison of Approaches to Model Uncertainty in Time Intervals

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.

A comparison technique for ill-known time intervals

Currently, many existing information systems contain large amounts of data. Prior research shows that many of these data represent time (domain) intervals and many of those may be subject to uncertainty. Moreover, great attention has been attracted by defining, finding or using qualitative temporal relationships between time (domain) intervals. In this context, the framework of Allen relationships is one of the most notable proposals. As a consequence, many existing proposals consider qualitative temporal relationships between time (domain) intervals subject to uncertainty on one hand and regular time (domain) intervals on the other. One of the most novel approaches in this context is given by the ill-known constraints framework. However, this framework does not yet support comparisons between time (domain) intervals subject to uncertainty. In this paper, a proposal is presented to expand this ill-known constraints framework in order to allow it to support the aforementioned temporal comparison, based on the Allen relationships.

Combining quantifications for flexible query result ranking

Databases contain data and database systems governing such databases are often intended to allow a user to query these data. On one hand, these data may be subject to imperfections, on the other hand, users may employ imperfect query preference specifications to query such databases. All of these imperfections lead to each query answer being accompanied by a collection of quantifications indicating how well (part of) a group of data complies with (part of) the users query. A fundamental question is how to present the user with the query answers complying best to his or her query preferences. The work presented in this paper first determines the difficulties to overcome in reaching such presentation. Mainly, a useful presentation needs the ranking of the query answers based on the aforementioned quantifications, but it seems advisable to not combine quantifications with different interpretations. Thus, the work presented in this paper continues to introduce and examine a novel technique to determine a query answer ranking. Finally, a few aspects of this technique, among which its computational efficiency, are discussed.