25,657 research outputs found
A Progressive Visual Analytics Tool for Incremental Experimental Evaluation
This paper presents a visual tool, AVIATOR, that integrates the progressive
visual analytics paradigm in the IR evaluation process. This tool serves to
speed-up and facilitate the performance assessment of retrieval models enabling
a result analysis through visual facilities. AVIATOR goes one step beyond the
common "compute wait visualize" analytics paradigm, introducing a continuous
evaluation mechanism that minimizes human and computational resource
consumption
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
LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation
We present LDAExplore, a tool to visualize topic distributions in a given
document corpus that are generated using Topic Modeling methods. Latent
Dirichlet Allocation (LDA) is one of the basic methods that is predominantly
used to generate topics. One of the problems with methods like LDA is that
users who apply them may not understand the topics that are generated. Also,
users may find it difficult to search correlated topics and correlated
documents. LDAExplore, tries to alleviate these problems by visualizing topic
and word distributions generated from the document corpus and allowing the user
to interact with them. The system is designed for users, who have minimal
knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a
pilot study which uses the abstracts of 322 Information Visualization papers,
where every abstract is considered a document. The topics generated are then
explored by users. The results show that users are able to find correlated
documents and group them based on topics that are similar
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