Rewriting rules to permeate complex similarity and fuzzy queries within a relational database system

A unified multimedia and semantic perspective for data retrieval in the semantic web

“…Two neighbor selection policies are supported: (1) a range-based selection, where the selected peers are those within a combined semantic-multimedia distance threshold and (2) a k-NN selection, which finds out the k nearest peers from both points of views, the semantic and multimedia ones. 8 For both policies, efficient algorithms which take advantage of specific index structures distributed across the network are exploited to reduce the search space (we refer the reader to [6] for a detailed description of these aspects).…”

“…7 In this case, we adopt a standard agglomerative approach by defining the distance between clusters as an average group linkage [15]. 8 It is worth noting that the topology of the network is heavily influenced by the kind of neighbor selection policy each peer chooses when it joins the network.…”

“…We start by providing a formal specification of the semantics of query answering, where each answer is characterized by a score which expresses the overall relevance of the answer from both data description and data content perspectives (Section 2). The proposed model does not compel to a fixed semantics but rather it is founded on a fuzzy settlement which proved to be sound for a formal characterization of the approximations originated in the network for both data description [7] and data content [8].…”

Novel Model for the Computation of Linguistic Hedges in Database Queries

“…The application of fuzzy set theory to relational data models is one major shift in addressing the vagueness in the data and the query specification. The research in this direction includes extensions to SQL to facilitate vague queries on relational databases [1], [2], [7], functional dependencies in fuzzy relational data models [8], fuzzy extensions to relational calculus and relational algebra [4], [5], [6], [9], [12], and a logic based approach to the fuzzy relational databases to deal with various forms of fuzziness and a domain calculus based fuzzy query language [3], [11]. Complexity normally arises from uncertainty in the form of ambiguity.…”

K-Relations and Beyond

“…Most utilize fuzzy logic Zadeh (1965), and the annotations are typically modelled by a membership function to the unit interval, [0, 1] Ma (2006); Penzo (2005); Rosado et al (2006); Schmitt & Schulz (2004), although there are generalizations where the membership function instead maps to an algebraic structure of some kind (typically poset or lattice based) Belohlávek & V. Vychodil (2006); Peeva & Kyosev (2004); Shenoi & Melton (1989). Green et al Green et al (2007) proposed a general data model (referred to as the K-relation model) for annotated relations.…”