Management of uncertain data : towards unattended integration

In recent years, the need to support uncertain data has increased. Sensor\ud applications, for example, are dealing with the inherent uncertainty about\ud the readings of the sensors. Current database management systems are not\ud equipped to deal with this uncertainty, other than as a user defined attribute.\ud This forces the user of the DBMS to take on the responsibility of managing\ud the uncertainty associated with the data.\ud In this thesis, we present a new data model, based on XML that is capable\ud of storing uncertainty about elements and subtrees. The XML data\ud model is extended in such a way, that probabilities can be associated with\ud the elements and subtrees, dependency and independency of elements can be\ud expressed and even the existence of entire elements or subtrees can be uncertain.\ud We give a sound semantical foundation for dealing with the uncertainty\ud associated with the data, and show how querying using this semantics works.\ud The probabilistic XML data model is used in an information integration\ud application. Decisions about equality are postponed if the integration system\ud is uncertain about equality. This uncertainty is stored using the probabilistic\ud XML data model, making the integration process itself unattended. The\ud amount of uncertainty arising from this integration can be large. We therefore\ud introduce knowledge rules that help deciding on equality during the integration\ud phase. Using these rules, integrated documents contain less uncertainty\ud and are therefore smaller in size. We also introduced two measures with\ud which the amount of uncertainty in the document can be quantified. Uncertainty\ud density measures the amount of uncertainty in the database. The\ud second measure, answer decisiveness, quantifies the ease with which most\ud likely possibilities in query results can be chosen.\ud At a later stage, when the user is querying the information source, and\ud therefore already actively using the system, feedback can be provided on\ud query results. This feedback is explained in the same semantical setting as\ud querying. Feedback statements can either be positive, i.e. the query result\ud can be observed in the real world, or negative, i.e. the query result cannot\ud be observed in the real world. We show that using this feedback technique, if\ud used with caution, reduces the amount of uncertainty and lets the information\ud source converge to a correctly integrated document. To measure the quality\ud of query results, we adapted precision and recall for probabilistic data in a\ud way that, for example incorrect answers with low probability do not have the\ud same negative impact as incorrect answers with a high probability

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

Geological resource management of the future: Drilling down the possibilities

Management of geological resources is based, ideally, on information on the quality and quantity of surface and subsurface litho-stratigraphical properties. Increasingly, these data become available for the offshore realm, though the integration into manageable and user-friendly applications is still at its infancy. Building on expertise from on-land data mining, we are now in the phase of creating 3D voxel models allowing for multi criteria resource volume calculations. The underlying data will be subdued to uncertainty modelling, a necessary step to produce data products with confidence limits. Anticipating on the dynamic nature of the marine environment, we aim at coupling the voxel model to environmental impact models to calculate resource depletion and regeneration, based on geological boundary conditions. In combination with anticipated impacts on fauna and flora, mining thresholds will be defined. All of the information is integrated into a decision support system for easy querying and online visualizations . The main aim is to provide long-term predictions on resource quantities to ensure future developments for the benefit of society and our future generations

Representing uncertainty regarding satisfaction degrees using possibility distributions

Evaluating flexible criteria on data leads to degrees of satisfaction. If a datum is uncertain, it can be uncertain to which degree it satisfies the criterion. This uncertainty can be modelled using a possibility distribution over the domain of possible degrees of satisfaction. In this work, we discuss the meaningfulness thereof by looking at the semantics of such a representation of the uncertainty. More specifically, it is shown that defuzzification of such a representation, towards usability in (multi-criteria) decision support systems, corresponds to expressing a clear attitude towards uncertainty (optimistic, pessimistic, cautious, etc.

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects