International audienceSupporting knowledge discovery through visual means is a hot research topic in the field of visual analytics in general, and a key issue in the analysis of textual data sets. In that context, the StorylineViz study aims at developing a generic approach to narrative analysis, supporting the identification of significant patterns inside textual data, and ultimately knowledge discovery and sensemaking. It builds on a text segmentation procedure through which sequences of situations are extracted. A situation is defined by a quadruplet of components: actors, space, time and motion. The approach aims at facilitating visual reasoning on the structure, rhythm, patterns and variations of heterogeneous texts in order to enable comparative analysis, and to summarise how the space/time/actors/motion components are organised inside a given narrative. It encompasses issues that are rooted in Information Sciences - visual analytics, knowledge representation – and issues that more closely relate to Digital Humanities – comparative methods and analytical reasoning on textual content, support in teaching and learning, cultural mediation

This paper reports on an interdisciplinary data acquisition and processing chain, the novelty of which is primarily to be found in a close integration of acoustic and spatial data. It provides a detailed description of the technological and methodological choices that were made in order to adapt to the particularities of the corpus studied (interiors of small scale rural architectural artefacts) keeping in mind the backbone objective of the research: facilitate comparisons (among buildings, among spatial and acoustic features). The research outputs pave the way for proportion-as-ratios analyses, as well as for the study of perceptual aspects from an acoustic point of view. Ultimately, “perceptual” acoustic data characterised by acoustic descriptors will be related to “objective” spatial data such as architectural metrics. The experiment is carried out on a set of fifteen “small-scale” rural chapels, which is a corpus intended at fostering cross-examinations in the context of an architectural programme acting as a constant. The specificity of this corpus, in terms of architectural layout, usage, and economic or access constraints, will be shown to have had a significant impact on choices made during the acquisition and processing chains.

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Abstract. In the field of the architectural heritage, the representation of artefacts, particularly for communication purposes, has benefited from the development of computer-based modelling techniques in fields ranging form archaeology to geography. But numerous experts in the above mentioned heritage field have come to question the readability of realistic models inside which the hypothetical nature of the content, a reconstruction, is not clearly assessed. In parallel, research in information visualisation has demonstrated that graphics can support reasoning as well as communication. Our contribution introduces the genesis of an informative modelling methodology in which the representation of architectural objects is used for information search and visualisation. 3D or 2D models localise objects in time, in space, and in a hierarchy of canonical shapes; they are calculated on the fly and deliver information visually. This paper discusses the underlying modelling methodology and applications in investigations about the evolutions of the city of Kraków (Poland).

Abstract. In order to acquire and share a better understanding architectural changes, researchers face the challenge of modelling and representing events (cause and consequences) occurring in space and time, and for which assessments of doubts are vital. This contribution introduces a visualisation designed to facilitate reasoning tasks, in which a focus view on evidence about what happens to artefact λ at time t is a complemented with a context view where successive spatial configurations of neighbouring artefacts, durations of changes, and punctual events are correlated and tagged with uncertainty markers.