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» Scaling Clustering Algorithms to Large Databases
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PPOPP
2010
ACM
14 years 5 months ago
A distributed placement service for graph-structured and tree-structured data
Effective data placement strategies can enhance the performance of data-intensive applications implemented on high end computing clusters. Such strategies can have a significant i...
Gregory Buehrer, Srinivasan Parthasarathy, Shirish...
ICDE
2010
IEEE
371views Database» more  ICDE 2010»
14 years 8 months ago
TASM: Top-k Approximate Subtree Matching
Abstract-- We consider the Top-k Approximate Subtree Matching (TASM) problem: finding the k best matches of a small query tree, e.g., a DBLP article with 15 nodes, in a large docum...
Nikolaus Augsten, Denilson Barbosa, Michael H. B&o...
ICRA
2009
IEEE
218views Robotics» more  ICRA 2009»
13 years 6 months ago
Automatically and efficiently inferring the hierarchical structure of visual maps
In Simultaneous Localisation and Mapping (SLAM), it is well known that probabilistic filtering approaches which aim to estimate the robot and map state sequentially suffer from poo...
Margarita Chli, Andrew J. Davison
ICDE
2012
IEEE
227views Database» more  ICDE 2012»
11 years 11 months ago
Temporal Analytics on Big Data for Web Advertising
—“Big Data” in map-reduce (M-R) clusters is often fundamentally temporal in nature, as are many analytics tasks over such data. For instance, display advertising uses Behavio...
Badrish Chandramouli, Jonathan Goldstein, Songyun ...
CIVR
2007
Springer
180views Image Analysis» more  CIVR 2007»
14 years 2 months ago
Probabilistic matching and resemblance evaluation of shapes in trademark images
We present a novel matching and similarity evaluation method for planar geometric shapes represented by sets of polygonal curves. Given two shapes, the matching algorithm randomly...
Helmut Alt, Ludmila Scharf, Sven Scholz