66,488 research outputs found
Immersive and Collaborative Data Visualization Using Virtual Reality Platforms
Effective data visualization is a key part of the discovery process in the
era of big data. It is the bridge between the quantitative content of the data
and human intuition, and thus an essential component of the scientific path
from data into knowledge and understanding. Visualization is also essential in
the data mining process, directing the choice of the applicable algorithms, and
in helping to identify and remove bad data from the analysis. However, a high
complexity or a high dimensionality of modern data sets represents a critical
obstacle. How do we visualize interesting structures and patterns that may
exist in hyper-dimensional data spaces? A better understanding of how we can
perceive and interact with multi dimensional information poses some deep
questions in the field of cognition technology and human computer interaction.
To this effect, we are exploring the use of immersive virtual reality platforms
for scientific data visualization, both as software and inexpensive commodity
hardware. These potentially powerful and innovative tools for multi dimensional
data visualization can also provide an easy and natural path to a collaborative
data visualization and exploration, where scientists can interact with their
data and their colleagues in the same visual space. Immersion provides benefits
beyond the traditional desktop visualization tools: it leads to a demonstrably
better perception of a datascape geometry, more intuitive data understanding,
and a better retention of the perceived relationships in the data.Comment: 6 pages, refereed proceedings of 2014 IEEE International Conference
on Big Data, page 609, ISBN 978-1-4799-5665-
Visualizing recommendations to support exploration, transparency and controllability
Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities - i.e. items bookmarked by users, recommendations and tags. Copyright © 2013 ACM
Using Visualization to Support Data Mining of Large Existing Databases
In this paper. we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process. but also for evaluating query results and. thereafter, refining the query accordingly. The main idea of our system is to represent as many data items as possible by the pixels of the display device. By arranging and coloring the pixels according to the relevance for the query, the user gets a visual impression of the resulting data set and of its relevance for the query. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of the query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. To support complex queries, we introduce the notion of approximate joins which allow the user to find data items that only approximately fulfill join conditions. We also present ideas how our technique may be extended to support the interoperation of heterogeneous databases. Finally, we discuss the performance problems that are caused by interfacing to existing database systems and present ideas to solve these problems by using data structures supporting a multidimensional search of the database
Interactive Visual Analysis of Networked Systems: Workflows for Two Industrial Domains
We report on a first study of interactive visual analysis of networked systems. Working with ABB Corporate Research and Ericsson Research, we have created workflows which demonstrate the potential of visualization in the domains of industrial automation and telecommunications. By a workflow in this context, we mean a sequence of visualizations and the actions for generating them. Visualizations can be any images that represent properties of the data sets analyzed, and actions typically either change the selection of data visualized or change the visualization by choice of technique or change of parameters
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis
Exploring data requires a fast feedback loop from the analyst to the system,
with a latency below about 10 seconds because of human cognitive limitations.
When data becomes large or analysis becomes complex, sequential computations
can no longer be completed in a few seconds and data exploration is severely
hampered. This article describes a novel computation paradigm called
Progressive Computation for Data Analysis or more concisely Progressive
Analytics, that brings at the programming language level a low-latency
guarantee by performing computations in a progressive fashion. Moving this
progressive computation at the language level relieves the programmer of
exploratory data analysis systems from implementing the whole analytics
pipeline in a progressive way from scratch, streamlining the implementation of
scalable exploratory data analysis systems. This article describes the new
paradigm through a prototype implementation called ProgressiVis, and explains
the requirements it implies through examples.Comment: 10 page
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