Learning Object Repositories with Dynamically Reconfigurable Metadata Schemata

[ES] In this paper we describe a model of learning object repository in which users have full control on the metadata schemata. Thus, they can define new schemata and they can reconfigure existing ones in a collaborative fashion. As consequence, the repository must react to changes in schemata in a dynamic and responsive way. Since schemata enable operations like navigation and search, dynamic reconfigurability requires clever indexing strategies, resilient to changes in these schemata. For this purpose, we have used conventional inverted indexing approaches and we have also devised a hierarchical clusteringbased indexing model. By using Clavy, a system for managing learning object repositories in the field of the Humanities, we provide some experimental results that show how the hierarchical clustering-based model can outperform the more conventional inverted indexes-based solutions

Browsing Digital Collections with Reconfigurable Faceted Thesauri

Faceted thesauri group classification terms into hierarchically arranged facets. They enable faceted browsing, a well-known browsing technique that makes it possible to navigate digital collections by recursively choosing terms in the facet hierarchy. In this paper we develop an approach to achieve faceted browsing in live collections, in which not only the contents but also the thesauri can be constantly reorganized. We start by introducing a digital collection model letting users reconfigure facet hierarchies. Then we introduce navigation automata as an efficient way of supporting faceted browsing in these collections. Since, in the worst-case, the number of states in these automata can grow exponentially, we propose two alternative indexing strategies able to bridge this complexity: inverted indexes and navigation dendrograms. Finally, by comparing these strategies in the context of Clavy, a system for managing collections with reconfigurable structures in digital humanities and educational settings, we provide evidence that navigation dendrogram organization outperforms the inverted index-based one

Move-minimizing puzzles, diamond-colored modular and distributive lattices, and poset models for Weyl group symmetric functions

The move-minimizing puzzles presented here are certain types of one-player combinatorial games that are shown to have explicit solutions whenever they can be encoded in a certain way as diamond-colored modular and distributive lattices. Such lattices can also arise naturally as models for certain algebraic objects, namely Weyl group symmetric functions and their companion semisimple Lie algebra representations. The motivation for this paper is therefore both diversional and algebraic: To show how some recreational move-minimizing puzzles can be solved explicitly within an order-theoretic context and also to realize some such puzzles as combinatorial models for symmetric functions associated with certain fundamental representations of the symplectic and odd orthogonal Lie algebras

High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)

Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.Comment: 72 page

Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis

In this paper we fuse together the Landscapes of Knowledge of Wille's and Exploratory Data Analysis by leveraging Formal Concept Analysis (FCA) to support data-induced scientific enquiry and discovery. We use extended FCA first by allowing K-valued entries in the incidence to accommodate other, non-binary types of data, and second with different modes of creating formal concepts to accommodate diverse conceptualizing phenomena. With these extensions we demonstrate the versatility of the Landscapes of Knowledge metaphor to help in creating new scientific and engineering knowledge by providing several successful use cases of our techniques that support scientific hypothesis-making and discovery in a range of domains: semiring theory, perceptual studies, natural language semantics, and gene expression data analysis. While doing so, we also capture the affordances that justify the use of FCA and its extensions in scientific discovery.FJVA and AP were partially supported by EUFP7 project LiMo- SINe (contract288024) for this research. CPM was partially supported by the Spanish Ministry of Economics and Competitiveness projects TEC2014-61729-EXP and TEC2014-53390-P

Formal Concept Analysis and Information Retrieval – A Survey

International audienceOne of the first models to be proposed as a document index for retrieval purposes was a lattice structure, decades before the introduction of Formal Concept Analysis. Nevertheless, the main notions that we consider so familiar within the community (" extension " , " intension " , " closure operators " , " order ") were already an important part of it. In the '90s, as FCA was starting to settle as an epistemic community, lattice-based Information Retrieval (IR) systems smoothly transitioned towards FCA-based IR systems. Currently, FCA theory supports dozens of different retrieval applications, ranging from traditional document indices to file systems, recommendation, multi-media and more recently, semantic linked data. In this paper we present a comprehensive study on how FCA has been used to support IR systems. We try to be as exhaustive as possible by reviewing the last 25 years of research as chronicles of the domain, yet we are also concise in relating works by its theoretical foundations. We think that this survey can help future endeavours of establishing FCA as a valuable alternative for modern IR systems