21,568 research outputs found
Goal-Based Selection of Visual Representations for Big Data Analytics
The H2020 TOREADOR Project adopts a model-driven architecture to streamline big data analytics and make it widely available to companies as a service. Our work in this context focuses on visualization, in particular on how to automate the translation of the visualization objectives declared by the user into a suitable visualization type. To this end we first define a visualization context based on seven prioritizable coordinates for assessing the user's objectives and describing the data to be visualized; then we propose a skyline-based technique for automatically translating a visualization context into a set of suitable visualization types. Finally, we evaluate our approach on a real use case excerpted from the pilot applications of TOREADOR
Recommended from our members
Representation Effects and Loss Aversion in Analytical Behaviour: An Experimental Study into Decision Making Facilitated by Visual Analytics
This paper presents the results of an experiment into the relationship between the representation of data and decision-making. Three hundred participants online, were asked to choose between a series of financial investment opportunities using data presented in line charts. A single dependent variable of investment choice was examined over four levels of varying display conditions and randomised data. Three variations to line chart visualisations provided a controlled factor between subjects divided into three groups; -˜standard’ line charts, -˜tall’ line charts, and one dual-series line chart. The final results revealed a consistent main effect and two other interactions between certain display conditions and decision-making. The findings of this paper are significant to the study visualisation and to the field of visual analytics. This experiment was devised as part of a study into Analytical Behaviour, defined as decision-making facilitated by visual analytics - a new topic that encompasses existing research and real-world applications
Visual Integration of Data and Model Space in Ensemble Learning
Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classification outputs and where the errors come from. We propose a tight
visual integration of the data and the model space for exploring and combining
classifier models. We introduce a workflow that builds upon the visual
integration and enables the effective exploration of classification outputs and
models. We then present a use case in which we start with an ensemble
automatically selected by a standard ensemble selection algorithm, and show how
we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it
Improving Big Data Visual Analytics with Interactive Virtual Reality
For decades, the growth and volume of digital data collection has made it
challenging to digest large volumes of information and extract underlying
structure. Coined 'Big Data', massive amounts of information has quite often
been gathered inconsistently (e.g from many sources, of various forms, at
different rates, etc.). These factors impede the practices of not only
processing data, but also analyzing and displaying it in an efficient manner to
the user. Many efforts have been completed in the data mining and visual
analytics community to create effective ways to further improve analysis and
achieve the knowledge desired for better understanding. Our approach for
improved big data visual analytics is two-fold, focusing on both visualization
and interaction. Given geo-tagged information, we are exploring the benefits of
visualizing datasets in the original geospatial domain by utilizing a virtual
reality platform. After running proven analytics on the data, we intend to
represent the information in a more realistic 3D setting, where analysts can
achieve an enhanced situational awareness and rely on familiar perceptions to
draw in-depth conclusions on the dataset. In addition, developing a
human-computer interface that responds to natural user actions and inputs
creates a more intuitive environment. Tasks can be performed to manipulate the
dataset and allow users to dive deeper upon request, adhering to desired
demands and intentions. Due to the volume and popularity of social media, we
developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing
emerging technologies of today to create a fully immersive tool that promotes
visualization and interaction can help ease the process of understanding and
representing big data.Comment: 6 pages, 8 figures, 2015 IEEE High Performance Extreme Computing
Conference (HPEC '15); corrected typo
What May Visualization Processes Optimize?
In this paper, we present an abstract model of visualization and inference
processes and describe an information-theoretic measure for optimizing such
processes. In order to obtain such an abstraction, we first examined six
classes of workflows in data analysis and visualization, and identified four
levels of typical visualization components, namely disseminative,
observational, analytical and model-developmental visualization. We noticed a
common phenomenon at different levels of visualization, that is, the
transformation of data spaces (referred to as alphabets) usually corresponds to
the reduction of maximal entropy along a workflow. Based on this observation,
we establish an information-theoretic measure of cost-benefit ratio that may be
used as a cost function for optimizing a data visualization process. To
demonstrate the validity of this measure, we examined a number of successful
visualization processes in the literature, and showed that the
information-theoretic measure can mathematically explain the advantages of such
processes over possible alternatives.Comment: 10 page
- …