Scalable Uncertainty Management

ISBN-13: 9783540879923 / Angielski / Miękka / 2008 / 416 str.

Sergio Greco

Opis

Originally, managinguncertainty and inconsistencyhas especiallybeen explored in the ?eld of arti?cial intelligence. During recent years, particularly with the availabilityofmassiveamountsofdataindi?erentrepositoriesandthepossibility of integrating and exploiting these data, technologies for managing uncertainty andinconsistencyhavestartedtoplayakeyroleindatabasesandtheWeb.Some of the most prominent of these technologies are probably the ranking algorithms behind Web search engines. Techniques for handling uncertainty and incons- tency are expected to play a similarly important role in the Semantic Web. The annual International Conference on Scalable Uncertainty Management (SUM) hasgrownout ofthisverylargeinterestonmanaginguncertaintyand- consistency in databases, the Web, the Semantic Web, and arti?cial intelligence. Theconferenceaimsat bringingtogether allthoseinterestedin the management of large volumes of uncertainty and inconsistency in these areas. The First - ternational Conference on Scalable Uncertainty Management (SUM 2007) was held in Washington DC, USA, October 10-12, 2007. This volume contains the papers presented at the Second International C- ference on Scalable Uncertainty Management (SUM 2008), which was held in Naples, Italy, October 1-3, 2008. It contains 27 technical papers, which were selected out of 42 submitted papers in a rigorous reviewing process, where each paper wasreviewedby at leastthree ProgramCommittee members.The volume also contains extended abstracts of the three invited tutorials/talks.

Spis treści

Consistent Query Answering: The First Ten Years.- Heavy Tails and Web Models.- Managing Probabilistic Data with MystiQ: The Can-Do, the Could-Do, and the Can’t-Do.- Frequent Itemset Mining from Databases Including One Evidential Attribute.- Evaluating Trustworthiness from Past Performances: Interval-Based Approaches.- A Comparative Study of Six Formal Models of Causal Ascription.- An Efficient Algorithm for Naive Possibilistic Classifiers with Uncertain Inputs.- Transitive Observation-Based Causation, Saliency, and the Markov Condition.- A Family of Tolerant Antidivision Operators for Database Fuzzy Querying.- Uncertainty Management for the Retrieval of Economic Information from Distributed Markets.- Loopy Propagation in a Probabilistic Description Logic.- On the Performance of Fuzzy Data Querying.- Tractable Reasoning with Bayesian Description Logics.- Approximate Reasoning for Efficient Anytime Induction from Relational Knowledge Bases.- Fusing Uncertain Structured Spatial Information.- A Neuro Fuzzy Approach for Handling Structured Data.- A Framework for the Partial Evaluation of SPARQL Queries.- An Evolutionary Perspective on Approximate RDF Query Answering.- Clustering Uncertain Data Via K-Medoids.- Speeding Up the NRA Algorithm.- Uncertain Context Modeling of Dimensional Ontology Using Fuzzy Subset Theory.- A Personalized Approach to Experience-Aware Service Ranking and Selection.- Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results.- Optimization of Queries over Interval Probabilistic Data.- Polynomial Time Queries over Inconsistent Databases.- Using OBDDs for Efficient Query Evaluation on Probabilistic Databases.- A Logical Framework to Reinforcement Learning Using Hybrid Probabilistic Logic Programs.- On the Relationship between Hybrid Probabilistic Logic Programs and Stochastic Satisfiability.- Scaling Most Probable World Computations in Probabilistic Logic Programs.- Measuring the Ignorance and Degree of Satisfaction for Answering Queries in Imprecise Probabilistic Logic Programs.

Blurb

This book constitutes the refereed proceedings of the Second International Conference on Scalable Uncertainty Management, SUM 2008, held in Naples, Italy, in Oktober 2008.

The 27 revised full papers presented together with the extended abstracts of 3 invited talks/tutorials were carefully reviewed and selected from 42 submissions. The papers address artificial intelligence researchers, database researchers, and practitioners to demonstrate theoretical techniques required to manage the uncertainty that arises in large scale real world applications and to cope with large volumes of uncertainty and inconsistency in databases, the Web, the semantic Web, and artificial intelligence in general.