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Über dieses Buch

We are facing an immense growth of digital data and information resources, both in terms of size, complexity, modalities and intrusiveness. Almost every aspect of our existence is being digitally captured. This is exemplified by the omnipresent existence of all kinds of data storage, far beyond those stored in traditional relational databases. The spectrum of data being digitally stored runs from multimedia data repositories to your purchases in most stores. Every tweet that you broadcast is captured for posterity. Needless to say this situation posses new research opportunities, challenges and problems in the ways we store, manipulate, search, and - in general - make use of such data and information. Attempts to cope with these problems have been emerging all over the world with thousands of people devoted to developing tools and techniques to deal with this new area of research. One of the prominent scholars and researchers in this field was the late Professor Ashley Morris who died suddenly and tragically at a young age. Ashley's career begun in industry, where he specialized in databases.



Decision Support, OLAP, Data Fusion and GIS


Decision Support Classification of Geospatial and Regular Objects Using Rough and Fuzzy Sets

In Geospatial Databases, there is often the need to store and manipulate objects with uncertain, fuzzy, or indeterminate boundaries. Both fuzzy sets and rough sets have been used with success in this undertaking. In this paper we explore how we can use both of these techniques to better classify and categorize both regular objects and geospatial objects.
Ashley Morris, Piotr Jankowski, Brian S. Bourgeois, Frederick E. Petry

Supporting Spatial Decision Making by Means of Suitability Maps

Spatial Decision Support Systems (SDSS) are interactive, computer-based systems, designed to support decision makers in achieving a higher effectiveness of decision making while solving a semi-structured spatial decision problem. Current spatial decision support techniques are predominantly based on boolean logic, which makes their expressive power inadequate. In this chapter it is presented how the Logic Scoring of Preference (LSP) method, helps to overcome the inadequacies present in traditional approaches. LSP is well suited to produce so-called dynamic, geographic suitability maps (S-maps), which provide specialised information on the suitability degree of selected geographic regions for a specific purpose. The presented approach is based on soft computing and many-valued logic.
Guy De Tré, Jozo Dujmović, Nico Van de Weghe

Exploring the Sensitivity of Fuzzy Decision Models to Landscape Information Inputs in a Spatially Explicit Individual-Based Ecological Model

This is part of an ongoing exploration of incorporating fuzzy logic into spatially explicit, individual-based ecological models of dispersal. Following the theoretical discussion of Robinson (2002), a prototypical model of small mammal dispersal behavior was used to demonstrate how the fuzzy control of dispersal agents could be implemented (Robinson and Graniero 2005a). The implementation showed how the Extensible Component Objects for Constructing Observable Simulation Models (ECO-COSM) system could be loosely coupled with geographic information system (GIS) database for spatially explicit ecological simulation modeling of individual behavior (Graniero and Robinson 2006). If the problem is viewed from a geocomputational management perspective, we can say that an animal agent must be able to query the state of relevant GIS layers within its local perceptual range and use that information to make decisions regarding its movement behavior. Its movement behavior inturn leads eventually to a change in the state of the agent. Within the ECO-COSM framework, this is handled by the Probe mechanism. By obtaining Probes from relevant Probeable landscape layers , an agent can acquire a perceptual inventory of its world (Graniero and Robinson 2006). Thus, the general approach is consistent with Bian’s (2003) hybrid approach to representing the world in individual-based modeling, which incorporates a traditional grid model of the environment and an object-oriented model of individual organisms.
Vincent B. Robinson

Fuzzy Multidimensional Databases

The interest for OLAP (standing for On-Line Analytical Processing), working on multidimensional databases is growing dramatically due to its interest in data analysis and data mining. Recent works (LBMD+00),(LGM00) showed the great interest of integrating fuzzy set theory in such technologies in the framework of data mining. We now propose to enhance the multidimensional data model to handle fuzziness. This model then provides the way to apply OLAP Mining methods on Fuzzy Multidimensional Databases, for Fuzzy-OLAP Mining.
Anne Laurent

Expressing Hierarchical Preferences in OLAP Queries

OLAP query answering requirements for a knowledge based treatment of user requests led us to introduce the concept of hierarchical preferences over a universe that has a hierarchical structure. We introduce the automatic analysis of queries according to concepts defined as part of knowledge based hierarchy in order to guide the query answering as part of a data-warehouse environment with the aid of hierarchical Intuitionistic fuzzy sets, H-IFS. Based on the notion of H-IFS we propose an ad-hoc utility build on top of current OLAP tools like Oracle10g that allows us to enhance the query capabilities of by providing better and knowledgeable answers to user’s requests. The theoretical aspects as well the practical issues and achieved results are presented throughout the rest of the paper.
Panagiotis Chountas, Ermir Rogova, Krassimir Atanassov

Imperfect Multisource Spatial Data Fusion Based on a Local Consensual Dynamics

Strategies for multisource spatial data fusion have generally to cope with distinct kinds of uncertainty, related to both the trust of the information source, the imperfection of spatial data, and the vagueness of the fusion strategy itself. In this chapter we propose a consensual fusion method that allows to flexibly model several fusion strategies ranging from a risk-taking to a risk-adverse attitude, and capable to cope with both data imprecision and source reliability. Uncertainty and imprecision in spatial data are represented by associating a fuzzy value with each spatial unit. The fusion function models a consensual dynamics and is parameterized so as to consider a varying spatial neighborhood of the data to fuse. Moreover the fusion has a quantifier-guided nature, reflecting the concept of a fuzzy majority and works on imprecise values to compute an imprecise result. It is formalized by a generalized OWA operator defined in the paper for aggregating imprecise values with distinct importance. The consensual fusion works so that the greater the trust score of the source and its agreement with the other sources, the more influent (important) is the data from the source in determining the consensual values. Thus the obtained fused map is determined in each location by a distinct majority of the sources, those that locally are in agreement. In cases where the data are affected by uncertainty one can require to fuse them so as to compute a result affected by at most a given maximum uncertainty level.
Gloria Bordogna, Marco Pagani, Gabriella Pasi

Database Querying, Spatial and Temporal Databases


Querying Fuzzy Spatiotemporal Databases: Implementation Issues

Modeling and querying spatiotemporal data, in particular fuzzy and complex spatial objects representing geographic entities and relations are challenging topics that have many applications in geographic information systems. In a recent article the authors have presented an approach to these problems. The present chapter focuses on the issues that arise from implementing this approach. As a case study the implementation of a meteorological database application that combines an object-oriented database with a knowledgebase is discussed.
Aziz Sözer, Adnan Yazıcı, Halit Oğuztüzün, Frederick E. Petry

Bipolar Queries: A Way to Deal with Mandatory and Optional Conditions in Database Querying

Abstract.We discuss an approach to bipolar queries.We start with the original idea proposed in Lacroix and Lavency and review some selected relevant approaches recently proposed in the literature. In particular we point out two main lines of research, the one focusing on a formal representation within some well established theories and the analysis of a meaningful combinations of multiple conditions, and the one concerned mainly with the study of how to aggregate mandatory (negative, or required) and optional (positive, or desired) conditions. We follow the second line of reasoning and show some relations with other approaches, both concerning database querying, exemplified by Chomicki’s queries with preferences, and Yager’s works in multicriteria decision making. In the former case we offer a fuzzy counterpart of a new relational algebra operator winnow and show how a bipolar query can be represented via the select and winnow operators.
Sławomir Zadroz̈ny, Janusz Kacprzyk

On Some Uses of a Stratified Divisor in an Ordinal Framework

In this paper, we are interested in taking preferences into account for division-like queries. The interest for introducing preferences is first to cope with user needs, then to get discriminated results instead of a flat set of elements. Here, the idea is to use ordinal preferences which are not too demanding for a casual user. Moreover, the type of query considered is inspired by the division operator and some of its variations where preferences apply only to the divisor. The division aims at retrieving the elements associated with a specified set of values and in a similar spirit, the anti-division looks for elements which are associated with none of the values of a given set. One of the focuses of this paper is to investigate queries mixing those two aspects. In order to remain coherent with the denomination of (anti-)division, the property of the result delivered is characterized. Last, a special attention is paid to the implementation of such queries using a regular database management system and some experimental results illustrate the feasibility of the approach.
Patrick Bosc, Olivier Pivert

Integration of Fuzzy ERD Modeling to the Management of Global Contextual Data

This chapter introduces the idiosyncrasies of managing the new paradigm of global contextual data, sets of context data and super sets of context data. It introduces some of the basic idea’s behind contexts and then develops a model for management of aggregated sets of contextual data and proposes methods for dealing with the selection and retrieval of context data that is inherently ambiguous about what to retrieve for a given query. Because contexts are characterized by four dimensions, those of time, space, impact and similarity they are inherently complicated to manage.
This work builds on previous work and extends that work to incorporate contexts. The original model for spatial-temporal management is presented and then analyzed to determine much coverage it can provide to the new context paradigm.
Gregory Vert, S. S. Iyengar

Repercussions of Fuzzy Databases Migration on Programs

Fuzzy databases have been introduced to deal with uncertain or incomplete information in many applications demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate the fuzzy databases advantages (flexible querying, handling imprecise data, fuzzy data mining, ...), minimizing the transformation costs. The best solution is to offer a smoothly migration toward this technology. However, the migration of applications or databases in enterprises arises from changes in business demands or technology challenges. The need for this migration is to improve operational efficiency or to manage risk, data migration outage, as well as performance. This chapter is about the migration towards fuzzy databases. We present our migration approach and we concentrate on their repercussions on programs.
Mohamed Ali Ben Hassine, José Galindo, Habib Ounelli


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