Memory and hypertext: a reflection on relational knowledge

This paper analyzes the changes the ways of organizing memory have undergone since ancient times, turning them into the current artificial memory systems. It aims to draw a parallel between the art of memory (which associates images to specific texts) and the hypertext (which also uses associations, but in a non-linear way). Our methodology consisted of a qualitative approach, involving the collection of texts about the art of memory and hypertext; this enables us to salvage the historical-cultural changes which have modified form and use of the art of memory and allowed the creation of hypertext. It also analyzes the similarities among artificial memory systems created by different cultures in order to prevent loss of knowledge produced by society.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

Seamlessly integrating similarity queries in SQL

Modern database applications are increasingly employing database management systems (DBMS) to store multimedia and other complex data. To adequately support the queries required to retrieve these kinds of data, the DBMS need to answer similarity queries. However, the standard structured query language (SQL) does not provide effective support for such queries. This paper proposes an extension to SQL that seamlessly integrates syntactical constructions to express similarity predicates to the existing SQL syntax and describes the implementation of a similarity retrieval engine that allows posing similarity queries using the language extension in a relational DBM. The engine allows the evaluation of every aspect of the proposed extension, including the data definition language and data manipulation language statements, and employs metric access methods to accelerate the queries. Copyright (c) 2008 John Wiley & Sons, Ltd.