The World’s Largest Library of more than
5,000 Business Book Summaries
Mois : septembre 2009
Eco, Lector in fabula — Equipe Littérature de l’Institut National de Recherche Pédagogique
Eco s'interroge sur le rôle du lecteur : comment coopère-t-il pour interpréter le texte ? comment les contraintes structurelles du texte régissent-elles l'interprétation ?
A.nnotate.com: Upload, Annotate, Share. Online document review and collaboration – PDF, Word and HTML
A.nnotate is an online annotation, collaboration and indexing system for documents and images, supporting PDF and MS Office formats. Instead of emailing different versions of a document back and forth you can now all comment on a single read-only copy online. Unlike online word processors, the document is displayed in high quality with fonts and layout just like the printed version. It is easy to use and runs in all common web browsers, with no software or plugins to install.
The University’s Mandate to Mandate Open Access — Open Students
Open Students
Why Faceted Navigation is Hard
I was thinking about the relational model today, and I realized that a lot of people probably don't realize that faceted navigation is an example of an application that simply doesn't fit comfortably in the relational model. It also occurs to me that most of you don't care. But I'm going to tell you why anyway.
Faceted navigation is built on faceted classification, which is conceptually very simple. You've got two things: records and categories, and those two things have a many-to-many relationship.
I like to work with concrete examples, so let's take the US states as our dataset. We've got one record for each state. For our categories, let's use adjacent states. That means Massachusetts has the following categories: adjoins-NH, adjoins-VT, adjoins-CT, adjoins-RI, and adjoins-NY. Obviously there are a lot of states that belong to the adjoins-NY category: Pennsylvania, New Jersey, Connecticut, Massachusetts, and Vermont. So clearly this is many-to-many. That's faceted classification.
RDF Aggregates and Full Text Search on Steroids with Solr at Frederick Giasson’s Weblog
The idea is to use the RDF data model and a triples store to populate the Solr schema index. We leverage the powerful and flexible data representation framework (RDF), in conjunction with the piece of software that lets you do whatever you want with that data (Virtuoso), to feed a carefully tailored Solr schema index to optimally perform three things: full-text search, aggregates and filtering. Also, we want to leverage the ontologies used to describe this data to be able to infer things vis-à-vis these indexed resources in Solr. This leverage enables us to use inference on full-text search, aggregates and filtering, in Solr! This is quite important since you will be able to perform full text searches, filtered by types that are inferred!