TMAP visualizes up to millions of high-dimensional points as an interactive 2D tree by computing an approximate nearest-neighbor graph and drawing its minimum spanning tree.
Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Data exploration and visualization systems are of great importance in the Big Data era. Exploring and visualizing very large datasets has become a major research challenge, of which scalability is a vital requirement. In this survey, we describe the major prerequisites and challenges that should be addressed by the modern exploration and visualization systems. Considering these challenges, we present how state-of-the-art approaches from the Database and Information Visualization communities attempt to handle them. Finally, we survey the systems developed by Semantic Web community in the context of the Web of Linked Data, and discuss to which extent these satisfy the contemporary requirements.
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cs.HC 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees
TMAP visualizes up to millions of high-dimensional points as an interactive 2D tree by computing an approximate nearest-neighbor graph and drawing its minimum spanning tree.