Combining Uncertainty and Vagueness in Time Intervals

Abstract

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.

Combining quantifications for flexible query result ranking

“…Often, a set of data exists, each of which could be the intended datum. In those cases, it is said that the datum is subject to uncertainty [1], [2], [5], [8], [12], [21], [24]- [27]. Usually, for a datum subject to uncertainty, an attempt is made to model the available knowledge about the intended datum, by assigning each of the data which could be the intended datum a degree of confidence an agent has that the corresponding datum is the intended datum.…”

“…Depending on the source of the circumstances of the uncertainty, such confidence can take different forms, where different forms have different interpretations and different handling rules. When the source is variability, confidence usually takes the form of chance and probability theory is used [1], [12], [26], [27]. When the source is a (partial) lack of knowledge, confidence usually takes the form of possibility and possibility theory is used [1], [8], [12], [21], [24], [25].…”

“…In the last decades, researchers have started to acknowledge the observation that such human-made data, information or knowledge can be subject to imperfections, often due to the imperfect nature of humans and human reasoning or to imperfections in measuring equipment. Such imperfections may take the form of uncertainties [1]- [9], imprecisions [1], [8], [9], vaguenesses [1], [9], contradictions, etc [8]- [10]. To allow the representation of such imperfections or ways to deal with them, many existing approaches propose to extend the data contained in databases to also contain descriptions of the determined intensity levels of such imperfections or such ways of handling imperfections [1], [5], [6], [11].…”

“…For example, consider table I, which is the visualization of an example relational database relation containing data representing 3 properties of each of 5 rental cars. For every such car, data representing a unique ID number, the color of the car and an ill-known time interval [1]- [3], [12]- [15] determining when the car might be available for rent, are stored. The first row in the table contains these properties' (or in the context of databases: attributes') names, every other row visualizes the values representing these properties for one car.…”

“…The interpretation is: an exact time interval during which a car is available, is intended, but due to a (partial) lack of knowledge about the circumstances determining this interval, it is uncertain exactly which interval is intended. In order to model confidence about which interval is intended, every existing time interval is given a possibility degree, which is interpreted as the degree of how plausible it is, given all available knowledge, that the corresponding time interval is the intended interval [1]- [3], [12]- [15].…”