When expressing their information needs in a (database) query, users sometimes prefer to state what has to be rejected rather than what has to be accepted. In general, what has to be rejected is not necessarily the complement of what has to be accepted. This phenomenon is commonly known as the heterogeneous bipolar nature of expressing information needs. Satisfaction degrees in regular fuzzy querying approaches are based on the "symmetric" assumption that the extent to which a database record, respectively, satisfies and does not satisfy a given query are complements of each other and are therefore less suited to adequately handle heterogeneous bipolarity in query specifications and query processing. In this paper, we present a bipolar query satisfaction modeling framework which is based on pairs that consist of an independent degree of satisfaction and degree of dissatisfaction. The use and advantages of the framework are illustrated in the context of fuzzy query evaluation in regular relational databases. More specifically, the evaluation of heterogeneous bipolar queries that contain both positive, negative, and bipolar criteria is studied. C 2011 Wiley Periodicals, Inc.

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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.

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In the context of geographic information systems (GIS), points of interest (POIs) are descriptions that denote geographical locations which might be of interest for some user purposes. Examples are public transport facilities, historical buildings, hotels and restaurants, recreation areas, hospitals etc. Because information gathering with respect to POIs is usually resource consuming, the user community is often involved in this task. In general, POI data originate from different sources (or users) and are therefore vulnerable to imperfections which might have a negative impact on data quality. Different POIs referring to, or describing the same physical geographical location might exist. Such POIs are said to be coreferent POIs. Coreferent POIs must be avoided as they could harm the data(base) quality and integrity. In this chapter, a novel soft computing technique for the (semi-)automated cleansing of POI databases is proposed. The proposed technique consists of two consecutive main steps: the detection of collections of coreferent POIs and the fusion, for each collection, of all coreferent POIs into a single consistent POI that represents all the POIs in the collection. The technique is based on fuzzy set theory, whereas possibility theory is used to cope with the uncertainties in the data. It can be used as a component of (semi-)automated data quality improvement strategies for databases and other information sources.