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Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time
We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales
Video Data Visualization System: Semantic Classification And Personalization
We present in this paper an intelligent video data visualization tool, based
on semantic classification, for retrieving and exploring a large scale corpus
of videos. Our work is based on semantic classification resulting from semantic
analysis of video. The obtained classes will be projected in the visualization
space. The graph is represented by nodes and edges, the nodes are the keyframes
of video documents and the edges are the relation between documents and the
classes of documents. Finally, we construct the user's profile, based on the
interaction with the system, to render the system more adequate to its
references.Comment: graphic
Simulation and Visualization of Thermal Metaphor in a Virtual Environment for Thermal Building Assessment
La référence est présente sur HAL mais est incomplÚte (il manque les co-auteurs et le fichier pdf).The current application of the design process through energy efficiency in virtual reality (VR) systems is limited mostly to building performance predictions, as the issue of the data formats and the workflow used for 3D modeling, thermal calculation and VR visualization. The importance of energy efficiency and integration of advances in building design and VR technology have lead this research to focus on thermal simulation results visualized in a virtual environment to optimize building design, particularly concerning heritage buildings. The emphasis is on the representation of thermal data of a room simulated in a virtual environment (VE) in order to improve the ways in which thermal analysis data are presented to the building stakeholder, with the aim of increasing accuracy and efficiency. The approach is to present more immersive thermal simulation and to project the calculation results in projective displays particularly in Immersion room (CAVE-like). The main idea concerning the experiment is to provide an instrument of visualization and interaction concerning the thermal conditions in a virtual building. Thus the user can immerge, interact, and perceive the impact of the modifications generated by the system, regarding the thermal simulation results. The research has demonstrated it is possible to improve the representation and interpretation of building performance data, particularly for thermal results using visualization techniques.Direktorat Riset dan Pengabdian Masyarakat (DRPM) Universitas Indonesia Research Grant No. 2191/H2.R12/HKP.05.00/201
On encouraging multiple views for visualization
Visualization enables 'seeing the unseen', and provides new insight into the underlying data. However users far too easily believe or rely on a single representation of the data; this view may be a favourite method, the simplest to perform, or a method that has always been used! But, a single representation may generate a misinterpretation of the information or provide a situation where the user is missing the 'richness' of the data content! By displaying the data in multiple ways a user may understand the information through different perspectives, overcome possible misinterpretations and perform interactive investigative visualization through correlating the information between views. Thus, the use of multiple views of the same information should be encouraged. We believe the visualization system itself should actively encourage the generation of multiple views by providing appropriate tools to aid in this operation. We present and categorise issues for encouraging multiple views and provide a framework for the generation, management and manipulation of such views
Design and Evaluation of a Probabilistic Music Projection Interface
We describe the design and evaluation of a probabilistic
interface for music exploration and casual playlist generation.
Predicted subjective features, such as mood and
genre, inferred from low-level audio features create a 34-
dimensional feature space. We use a nonlinear dimensionality
reduction algorithm to create 2D music maps of
tracks, and augment these with visualisations of probabilistic
mappings of selected features and their uncertainty.
We evaluated the system in a longitudinal trial in usersâ
homes over several weeks. Users said they had fun with the
interface and liked the casual nature of the playlist generation.
Users preferred to generate playlists from a local
neighbourhood of the map, rather than from a trajectory,
using neighbourhood selection more than three times more
often than path selection. Probabilistic highlighting of subjective
features led to more focused exploration in mouse
activity logs, and 6 of 8 users said they preferred the probabilistic
highlighting mode
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains,
we are regularly confronted with material whose largely unknown thematic
structure we want to uncover. With unsupervised, exploratory analysis, no prior
knowledge about the content is required and highly open-ended tasks can be
supported. In the past few years, probabilistic topic modeling has emerged as a
popular approach to this problem. Nevertheless, the representation of the
latent topics as aggregations of semi-coherent terms limits their
interpretability and level of detail.
This paper presents an alternative approach to topic modeling that maps
topics as a network for exploration, based on distributional semantics using
learned word vectors. From the granular level of terms and their semantic
similarity relations global topic structures emerge as clustered regions and
gradients of concepts. Moreover, the paper discusses the visual interactive
representation of the topic map, which plays an important role in supporting
its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent
Data Analysis (IDA 2015
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