Titlebook: Scalable Uncertainty Management; Second International Sergio Greco,Thomas Lukasiewicz Conference proceedings 2008 Springer-Verlag Berlin He

一瞥

Fusing Uncertain Structured Spatial Information,perties may have various level of generality, giving birth to a . of properties for a given universe of discourse. Thus, the set of properties pertaining to a conceptual taxonomy, as the set of areas and parcels, are structured by a natural partial order. We refer to such structures as ontologies. I

依法逮捕

A Neuro Fuzzy Approach for Handling Structured Data,ly pre-process data and then use classic machine learning algorithm. Another problem that machine learning algorithm have to face is the intrinsic uncertainty of data, where in such situations classic algorithm do not have the means to handle them. In this work a novel neuro-fuzzy model for structur

browbeat

A Framework for the Partial Evaluation of SPARQL Queries, proposed approach, global evaluation of queries is accomplished by first performing local evaluation on each data source, then merging the obtained results. When merging the results, term equivalence across different sources is evaluated by looking at the context of each term. Moreover, the framewo

amygdala

An Evolutionary Perspective on Approximate RDF Query Answering,ssuming perfect answers on finite repositories. In this paper, we focus on a query method based on evolutionary computing, which allows us to handle uncertainty, incompleteness and unsatisfiability, and deal with large datasets, all within a single conceptual framework. Our technique supports approx

Spongy-Bone

Clustering Uncertain Data Via K-Medoids, uncertain data management, there has been a growing interest in clustering uncertain data. In particular, the classic K-means clustering algorithm has been recently adapted to handle uncertain data. However, the centroid-based partitional clustering approach used in the adapted K-means presents two

阶层

Blazon

Frequent Itemset Mining from Databases Including One Evidential Attribute,ave uncertain values modelled via the evidence theory, i.e., a basic belief assignment. We introduce in this paper a variant of the structure Belief Itemset Tree (BIT) for mining frequent itemsets from evidential data and we lead some experiments that showed efficiency of our mining algorithm compared to the existing ones.

Etching

BILE

A Framework for the Partial Evaluation of SPARQL Queries,rk allows scoring partial answers by evaluating how much a partial answer is able to capture each concept expressed in the query. Finally, a distributed index structure is proposed that supports early pruning of useless intermediate results.

尽忠

Conference proceedings 2008tical 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.