𝔖 Scriptorium
✦   LIBER   ✦

πŸ“

Uncertainty Approaches for Spatial Data Modeling and Processing: A Decision Support Perspective

✍ Scribed by Ashley Morris, Piotr Jankowski, Brian S. Bourgeois, Frederick E. Petry (auth.), Janusz Kacprzyk, Frederick E. Petry, Adnan Yazici (eds.)


Publisher
Springer-Verlag Berlin Heidelberg
Year
2010
Tongue
English
Leaves
201
Series
Studies in Computational Intelligence 271
Edition
1
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis


This volume is dedicated to the memory of Professor Ashley Morris who passed away some two years ago. Ashley was a close friend of all of us, the editors of this volume, and was also a Ph.D. student of one of us. We all had a chance to not only fully appreciate, and be inspired by his contributions, which have had a considerable impact on the entire research community. Due to our personal relations with Ashley, we also had an opportunity to get familiar with his deep thinking about the areas of his expertise and interests. Ashley has been involved since the very beginning of his professional career in database research and practice. Notably, he introduced first some novel solution in database management systems that could handle imprecise and uncertain data, and flexible queries based on imprecisely specified user interests. He proposed to use for that purpose fuzzy logic as an effective and efficient tool. Later the interests of Ashley moved to ways of how to represent and manipulate more complicated databases involving spatial or temporal objects. In this research he discovered and pursued the power of Geographic Information Systems (GISs).

These two main lines of Ashley’s research interests and contributions are reflected in the composition of this volume. Basically, we collected some significant papers by well known researchers and scholars on the above mentioned topics. The particular contributions will now be briefly summarized to help the reader get a view of the topics covered and the contents of the particular contributions.

✦ Table of Contents


Front Matter....Pages -
Front Matter....Pages 1-1
Decision Support Classification of Geospatial and Regular Objects Using Rough and Fuzzy Sets....Pages 3-8
Supporting Spatial Decision Making by Means of Suitability Maps....Pages 9-27
Exploring the Sensitivity of Fuzzy Decision Models to Landscape Information Inputs in a Spatially Explicit Individual-Based Ecological Model....Pages 29-42
Fuzzy Multidimensional Databases....Pages 43-59
Expressing Hierarchical Preferences in OLAP Queries....Pages 61-77
Imperfect Multisource Spatial Data Fusion Based on a Local Consensual Dynamics....Pages 79-94
Front Matter....Pages 95-95
Querying Fuzzy Spatiotemporal Databases: Implementation Issues....Pages 97-116
Bipolar Queries: A Way to Deal with Mandatory and Optional Conditions in Database Querying....Pages 117-132
On Some Uses of a Stratified Divisor in an Ordinal Framework....Pages 133-154
Integration of Fuzzy ERD Modeling to the Management of Global Contextual Data....Pages 155-173
Repercussions of Fuzzy Databases Migration on Programs....Pages 175-193
Back Matter....Pages -

✦ Subjects


Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)


πŸ“œ SIMILAR VOLUMES


Perspectives on Uncertainty and Risk: Th
✍ Marjolein B. A. van Asselt (auth.) πŸ“‚ Library πŸ“… 2000 πŸ› Springer Netherlands 🌐 English

<p>This volume is intended to stimulate a change in the practice of decision support, advocating an interdisciplinary approach centred on both social and natural sciences, both theory and practice. <br/> It addresses the issue of analysis and management of uncertainty and risk in decision support co

Uncertainty Modelling and Quality Contro
✍ Shi Wenzhong (Editor); Bo Wu (Editor); Alfred Stein (Editor) πŸ“‚ Library πŸ“… 2015 πŸ› CRC Press

<p>Offers New Insight on Uncertainty ModellingFocused on major research relative to spatial information, Uncertainty Modelling and Quality Control for Spatial Data introduces methods for managing uncertainties-such as data of questionable quality-in geographic information science (GIS) applications.

Uncertainty Modeling for Data Mining: A
✍ Zengchang Qin, Yongchuan Tang πŸ“‚ Library πŸ“… 2015 πŸ› Springer 🌐 English

<p>Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. <i>Uncertainty Modeling for Data Mining: A Label Semantics Approach</i> introduces 'label semantics', a fuzzy-logic-based theory for modeling un

Uncertainty Modeling for Data Mining: A
✍ Prof. Zengchang Qin, Prof. Yongchuan Tang (auth.) πŸ“‚ Library πŸ“… 2014 πŸ› Springer-Verlag Berlin Heidelberg 🌐 English

<p><p>Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. <i>Uncertainty Modeling for Data Mining: A Label Semantics Approach</i> introduces 'label semantics', a fuzzy-logic-based theory for modeling

Processing And Managing Complex Data for
✍ Jerome Darmont, Omar Boussaid πŸ“‚ Library πŸ“… 2006 πŸ› Idea Group Publishing 🌐 English

In many decision support fields the data that is exploited tends to be more and more complex. To take this phenomenon into account, classical architectures of data warehouses or data mining algorithms must be completely re-evaluated. Processing and Managing Complex Data for Decision Support provi