99,015 research outputs found
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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
Exploration of Large Digital Sky Surveys
We review some of the scientific opportunities and technical challenges posed
by the exploration of the large digital sky surveys, in the context of a
Virtual Observatory (VO). The VO paradigm will profoundly change the way
observational astronomy is done. Clustering analysis techniques can be used to
discover samples of rare, unusual, or even previously unknown types of
astronomical objects and phenomena. Exploration of the previously poorly probed
portions of the observable parameter space are especially promising. We
illustrate some of the possible types of studies with examples drawn from
DPOSS; much more complex and interesting applications are forthcoming.
Development of the new tools needed for an efficient exploration of these vast
data sets requires a synergy between astronomy and information sciences, with
great potential returns for both fields.Comment: To appear in: Mining the Sky, eds. A. Banday et al., ESO Astrophysics
Symposia, Berlin: Springer Verlag, in press (2001). Latex file, 18 pages, 6
encapsulated postscript figures, style files include
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