Scalable Uncertainty Management: 4th International Conference, SUM 2010, Toulouse, France, September 27-29, 2010, Proceedings

by Amol Deshpande (Editor), Anthony Hunter (Editor)

Overview

Managing uncertainty and inconsistency has been extensively explored in - Artificial Intelligence over a number of years. Now with the advent of massive amounts of data and knowledge from distributed heterogeneous,and potentially conflicting, sources, there is interest in developing and applying formalisms for uncertainty andinconsistency widelyin systems that need to better managethis data and knowledge. The annual International Conference on Scalable Uncertainty Management (SUM) has grown out of this wide-ranging interest in managing uncertainty and inconsistency in databases, the Web, the Semantic Web, and AI. It aims at bringing together all those interested in the management of large volumes of uncertainty and inconsistency, irrespective of whether they are in databases,the Web, the Semantic Web, or in AI, as well as in other areas such as information retrieval, risk analysis, and computer vision, where significant computational - forts are needed. After a promising First International Conference on Scalable Uncertainty Management was held in Washington DC, USA in 2007, the coference series has been successfully held in Napoli, Italy, in 2008, and again in Washington DC, USA, in 2009.

Product Details

ISBN-13:	9783642159503
Publisher:	Springer Berlin Heidelberg
Publication date:	12/09/2010
Series:	Lecture Notes in Computer Science , #6379
Edition description:	2010
Pages:	389
Product dimensions:	6.10(w) x 9.30(h) x 0.70(d)

Table of Contents

Invited Talks.- Markov Chain Monte Carlo and Databases.- Answer Set Programming, the Solving Paradigm for Knowledge Representation and Reasoning.- Discussant Contributions.- Graphical and Logical-Based Representations of Uncertain Information in a Possibility Theory Framework.- Probabilistic Data: A Tiny Survey.- The Role of Epistemic Uncertainty in Risk Analysis.- Uncertainty in Clustering and Classification.- Information Fusion.- Use of the Domination Property for Interval Valued Digital Signal Processing.- Regular Contributions.- Managing Lineage and Uncertainty under a Data Exchange Setting.- A Formal Analysis of Logic-Based Argumentation Systems.- Handling Inconsistency with Preference-Based Argumentation.- A Possibility Theory-Oriented Discussion of Conceptual Pattern Structures.- DK-BKM: Decremental K Belief K-Modes Method.- On the Use of Fuzzy Cardinalities for Reducing Plethoric Answers to Fuzzy Queries.- From Bayesian Classifiers to Possibilistic Classifiers for Numerical Data.- Plausibility of Information Reported by Successive Sources.- Combining Semantic Web Search with the Power of Inductive Reasoning.- Evaluating Trust from Past Assessments with Imprecise Probabilities: Comparing Two Approaches.- Range-Consistent Answers of Aggregate Queries under Aggregate Constraints.- Characterization, Propagation and Analysis of Aleatory and Epistemic Uncertainty in the 2008 Performance Assessment for the Proposed Repository for High-Level Radioactive Waste at Yucca Mountain, Nevada.- Comparing Evidential Graphical Models for Imprecise Reliability.- Imprecise Bipolar Belief Measures Based on Partial Knowledge from Agent Dialogues.- Kriging with Ill-Known Variogram and Data.- Event Modelling and Reasoning with Uncertain Information for Distributed Sensor Networks.- Uncertainty in Decision Tree Classifiers.- Efficient Policy-Based Inconsistency Management in Relational Knowledge Bases.- Modelling Probabilistic Inference Networks and Classification in Probabilistic Datalog.- Handling Dirty Databases: From User Warning to Data Cleaning — Towards an Interactive Approach.- Disjunctive Fuzzy Logic Programs with Fuzzy Answer Set Semantics.- Cost-Based Query Answering in Action Probabilistic Logic Programs.- Clustering Fuzzy Data Using the Fuzzy EM Algorithm.- Combining Multi-resolution Evidence for Georeferencing Flickr Images.- A Structure-Based Similarity Spreading Approach for Ontology Matching.- Risk Modeling for Decision Support.