In the Middle Ages, some illuminations were intended for the elites of society and served as a means of communication for them to extend their social influences and to represent their social environments. They constitute an information system based on symbolic components linked together by semantic and influential relationships whose structure is close to models representing social relationships and networks. Today, understanding these illuminations and extracting their implicit messages, expressed through the combination of metaphorical graphic elements, are a difficult task reserved for experts. To help these latter and address the semantic heterogeneity of illuminations, this article explores the synergy between knowledge representation techniques and the analysis of medieval documents to build a knowledge model describing these medieval paintings. It proposes a formal ontology composed of items describing the explicit and visible knowledge of medieval illuminations and others expressing their implicit messages. The considered illuminations are part of those ordered or linked to the Burgundy duke, Philiphe le Bon
Social graph analysis is generally based on a local exploration of the underlying graph. That is, the analysis of a node of the graph is often done after having analyzed nodes located in its vicinity. However, over the time, networks are bound to grow with the addition of new members, which inevitably leads to the enlargement of the corresponding graphs. At this level we therefore have a problem because more the size of the graph increases, more the execution time of graph analysis applications too. This is due to the very large number of nodes that will need to be treated. Some recent work in-faces this problem by exploiting the properties of social networks such as the community structure to renumber the nodes of the graph in order to reduce cache misses. Reducing cache misses in an application allows to reduce the execution time of this application. In this paper, we argue that combining existing graph ordering with a new numbering that exploit execution traces analysis can allow to improve cache misses reduction and hence execution time reduction. The idea is to build graph numbering using execution traces of graph analysis applications and then combine it with an existing graph numbering (such as cn-order). To build this new ordering, we define a new distance and then used it to analyse execution traces with well known clustering algorithms K-means (for Kmeans-order) and hierarchical clustering (for cl-hier-order). Experiments on a user machine (dual-core) and four cores […]
