Titlebook: On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE; OTM Confederated Int Robert Meersman,Zahir Tari Conference procee

Feckless

STAT

Jorge Coelho,Mário Floridonst harmful interference. By extending the perfect spectrum sensing in the literature to more realistic situations, a new random sensing error framework of secondary users (SUs) is proposed to study the SUs’ behavior so as to make the best benefit for the CR system in this paper. A novel queueing-ga

希望

Ethics

Obstacle

Security Management Through Overloading Viewsntation and maintenance of database applications. In this paper we show how it can be used to manage data security. The model is based on updateable object views built within the stack-based approach to object-oriented query languages. After inserting the overloading view on top of the given populat

ITCH

Paradigms for Decentralized Social Filtering Exploiting Trust Network Structurel subsets of most relevant peers from which to receive personal product recommendations. However, common similarity-based neighborhood forming techniques imply various drawbacks, rendering the conception of decentralized recommender systems virtually impossible. We advocate trust metrics and trust-d

軟膏

A Necessary Condition for Semantic Interoperability in the Larger. Even if research on semantic reconciliation methods is abundant, it is not clear how interoperable very large scale semantic systems can be. This paper represents a first effort towards analytically analyzing semantic interoperability in the large: By adapting a recent graph-theoretic framework,

inscribe

Mining the Meaningful Compound Terms from Materialized Faceted Taxonomies can . an expression of the . that specifies exactly those compound terms that have non-empty interpretation. The mined expressions can be used for encoding compactly (and subsequently reusing) the domain knowledge that is stored in existing materialized faceted taxonomies. Furthermore, expression m

航海太平洋

金盤(pán)是高原

Taming the Unstructured: Creating Structured Content from Partially Labeled Schematic Text Sequencesntent. Typically such content is characterized by attribute value sequences having a common schema. In addition each sequence is unstructured free text without any separators between the attribute values. Hidden Markov Models (HMMs) have been used for creating structured content from such text seque