An important component of spatial data quality is the imprecision resulting from the resolution at which data are represented. Current research on topics such as spatial data integration and generalisation needs to be well-founded on a theory of multi-resolution. This paper provides a formal framework for treating the notion of resolution and multi-resolution in geographic spaces. It goes further to develop an approach to reasoning with imprecision about spatial entities and relationships resulting from finite resolution representations. The approach is similar to aspects of rough and fuzzy set theories. The paper concludes by providing the beginnings of a geometry of vague spatial entities and relationships. Keywords: uncertainty, vagueness, rough set, fuzzy set, resolution, spatial reasoning, data quality 1. Introduction The notion of spatial resolution is fundamental to many aspects of the representation of spatial data, and a proper formulation of a multi-resolution data model is...

Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. Unfortunately, sensor data is subject to several sources of errors such as noise from external sources, hardware noise, inaccuracies and imprecision, and various environmental effects. Such errors may seriously impact the answer to any query posed to the sensors. In particular, they may yield imprecise or even incorrect and misleading answers which can be very significant if they result in immediate critical decisions or activation of actuators. In this paper, we present a framework for cleaning and querying noisy sensors. Specifically, we present a Bayesian approach for reducing the uncertainty associated with the data, that arise due to random noise, in an online fashion. Our approach combines prior knowledge of the true sensor reading, the noise characteristics of this sensor, and the observed noisy reading in order to obtain a more accurate estimate of the reading. This cleaning step can be performed either at the sensor level or at the base-station. Based on our proposed uncertainty models and using a statistical approach, we introduce several algorithms for answering traditional database queries over uncertain sensor readings. Finally, we present a preliminary evaluation of our proposed approach using synthetic data and highlight some exciting research directions in this area.

Robust and scalable user context management is the key enabler for the emerging context- and situation-aware applications, and ontology-based approaches have shown their usefulness for capturing especially context information on a high level of abstraction. But so far the problem has not been approached as a data management problem, which is key to scalability and robustness. The specific challenges lie in the imperfection of high-level context information, its time-dependence and the variability in the dynamics between its different elements.

Current research in context-awareness is biased toward low-level context information. High-level context information, however, poses several challenges to context management systems, which can be traced back to the asynchronicity of context acquisition and use and the inherent dynamics and imperfection in that process. This paper presents a three layer model allowing for dealing with the problems of imperfection and aging in a controlled way. It conceives the problem of high-level user context management as an information management problem with specific requirements.

Abstract. We propose an extension to Description Logics (DLs) with uncertainty which unifies and/or generalizes a number of existing frameworks for DLs with uncertainty. To this end, we first give a classification of these frameworks and identify the essential features as well as properties of the various combination functions allowed in the underlying uncertainty formalisms they model. This also allows us express the semantics of the DL elements in a flexible manner. We illustrate how various DLs with uncertainty can be expressed in our generic framework. 1

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient. Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval. Based on this observation, we develop the miR Ror architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database. First, we introduce a concept layer to enable reasoning over low-level concepts in the database. Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer. Third, we add the functionality to process the users ’ relevance feedback. We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing. We conclude with an outline for implementation of miR Ror on top of the Monet extensible database system. 1

Abstract. Description Logics (DLs) are gaining more popularity as the foundation of ontology languages for the Semantic Web. On the other hand, uncertainty is a form of deficiency or imperfection commonly found in the real-world information/data. In recent years, there has been an increasing interest in extending the expressive power of DLs to support uncertainty, for which a number of frameworks have been proposed. In this paper, we introduce an extension of DL ( ) that unifies and/or generalizes a number of existing approaches for DLs with uncertainty. We first provide a classification of the components of existing frameworks for DLs with uncertainty in a generic way. Using this as a basis, we then discuss ways to extend these components with uncertainty, which includes the description language, the knowledge base, and the reasoning services. Detailed explanations and examples are included to describe the proposed completion rules. 1

Abstract. We introduce a novel view on how to deal with the problems of semantic interoperability in distributed systems. This view is based on the concept of emergent semantics, which sees both the representation of semantics and the discovery of the proper interpretation of symbols as the result of a self-organizing process performed by distributed agents exchanging symbols and having utilities dependent on the proper interpretation of the symbols. This is a complex systems perspective on the problem of dealing with semantics. We highlight some of the distinctive features of our vision and point out preliminary examples of its application. 1

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 II. Problem Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1 Verification & Validation Testing . . . . . . . . . . . . . . . . 2-1 2.2 Methods of Verification & Validation . . . . . . . . . . . . . . 2-3 2.3 V & V of knowledge based systems versus conventional software 2-4 2.4 Knowledge Acquisition . . . . . . . . . . . . . . . . . . . . . 2-5 2.5 Knowledge Representation . . . . . . . . . . . . . . . . . . . 2-6 2.6 The Bayesian Knowledge Base representation . . . . . . . . . 2-7 2.7 Knowledge Base Errors . . . . . . . . . . . . . . . . . . . . . 2-9 III. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1 PESKI Validation . . . . . . . . . . . . . . . . . . . . . . . . 3-2 3.1.1 Test Cases . . . . . . . . . . . . . . . . . . . . . . . 3-2 3.1.2 Direct Dependency R...

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