Bipolar querying of valid-time intervals subject to uncertainty

Databases model parts of reality by containing data representing properties of real-world objects or concepts. Often, some of these properties are time-related. Thus, databases often contain data representing time-related information. However, as they may be produced by humans, such data or information may contain imperfections like uncertainties. An important purpose of databases is to allow their data to be queried, to allow access to the information these data represent. Users may do this using queries, in which they describe their preferences concerning the data they are (not) interested in. Because users may have both positive and negative such preferences, they may want to query databases in a bipolar way. Such preferences may also have a temporal nature, but, traditionally, temporal query conditions are handled specifically. In this paper, a novel technique is presented to query a valid-time relation containing uncertain valid-time data in a bipolar way, which allows the query to have a single bipolar temporal query condition

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules

Bipolarity in the querying of temporal databases

A database represents part of reality by containing data representing properties of real objects or concepts. To many real-world concepts or objects, time is an essential aspect and thus it should often be (implicitly) represented by databases, making these temporal databases. However, like other data, the time-related data in such databases may also contain imperfections such as uncertainties. One of the main purposes of a database is to allow the retrieval of information or knowledge deduced from its data, which is often done by querying the database. Because users may have both positive and negative preferences, they may want to query a database in a bipolar way. Moreover, their demands may have some temporal aspects. In this paper, a novel technique is presented, to query a valid-time relation containing uncertain valid-time data in a heterogeneously bipolar way, allowing every elementary query constraint a specific temporal constraint

Fuzzy and uncertain spatio-temporal database models : a constraint-based approach

In this paper a constraint-based generalised object-oriented database model is adapted to manage spatiotemporal information. This adaptation is based on the definition of a new data type, which is suited to handle both temporal and spatial information. Generalised constraints are used to describe spatio-temporal data, to enforce integrity rules on databases, to specify the formal semantics of a database scheme and to impose selection criteria for information retrieval