Duplicate Elimination in Space-partitioning Tree Indexes

2007 ◽  
Author(s):  
M. Y. Eltabakh ◽  
Mourad Ouzzani ◽  
Walid G. Aref
Keyword(s):  
Space Partitioning

MODIFICATION OF TROPICAL COASTAL LANDSCAPES BY BRACHYURAN CRABS: NEOICHNOLOGY OF BIOTIC INNOVATION AND SPACE PARTITIONING

2017 ◽  
Author(s):  
H. Allen Curran ◽  
◽  
Ilya V. Buynevich ◽  
Koji Seike ◽  
Karen Kopcznski ◽  
...  
Keyword(s):  
Space Partitioning ◽  
Coastal Landscapes ◽  
Brachyuran Crabs

scholarly journals Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning

2020 ◽  
Vol 10 (10) ◽  
pp. 3356 ◽  
Author(s):  
Jose J. Valero-Mas ◽  
Francisco J. Castellanos
Keyword(s):  
Data Reduction ◽  
Structural Data ◽  
Data Representation ◽  
Space Partitioning ◽  
Classification Rate ◽  
Symbolic Data ◽  
Knn Classifier ◽  
Initial Dataset ◽  
Low Efficiency ◽  
High Level

Within the Pattern Recognition field, two representations are generally considered for encoding the data: statistical codifications, which describe elements as feature vectors, and structural representations, which encode elements as high-level symbolic data structures such as strings, trees or graphs. While the vast majority of classifiers are capable of addressing statistical spaces, only some particular methods are suitable for structural representations. The kNN classifier constitutes one of the scarce examples of algorithms capable of tackling both statistical and structural spaces. This method is based on the computation of the dissimilarity between all the samples of the set, which is the main reason for its high versatility, but in turn, for its low efficiency as well. Prototype Generation is one of the possibilities for palliating this issue. These mechanisms generate a reduced version of the initial dataset by performing data transformation and aggregation processes on the initial collection. Nevertheless, these generation processes are quite dependent on the data representation considered, being not generally well defined for structural data. In this work we present the adaptation of the generation-based reduction algorithm Reduction through Homogeneous Clusters to the case of string data. This algorithm performs the reduction by partitioning the space into class-homogeneous clusters for then generating a representative prototype as the median value of each group. Thus, the main issue to tackle is the retrieval of the median element of a set of strings. Our comprehensive experimentation comparatively assesses the performance of this algorithm in both the statistical and the string-based spaces. Results prove the relevance of our approach by showing a competitive compromise between classification rate and data reduction.

Stable minimum space partitioning in linear time

1992 ◽  
Vol 32 (4) ◽  
pp. 580-585 ◽  
Author(s):  
Jyrki Katajainen ◽  
Tomi Pasanen
Keyword(s):  
Linear Time ◽  
Space Partitioning

scholarly journals Shallow-circuit variational quantum eigensolver based on symmetry-inspired Hilbert space partitioning for quantum chemical calculations

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Feng Zhang ◽  
Niladri Gomes ◽  
Noah F. Berthusen ◽  
Peter P. Orth ◽  
Cai-Zhuang Wang ◽  
...  
Keyword(s):  
Hilbert Space ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Space Partitioning

scholarly journals Floorplan generation from 3D point clouds: A space partitioning approach

2021 ◽  
Vol 175 ◽  
pp. 44-55
Author(s):  
Hao Fang ◽  
Florent Lafarge ◽  
Cihui Pan ◽  
Hui Huang
Keyword(s):  
Point Clouds ◽  
Space Partitioning ◽  
3D Point Clouds

Hardware-Accelerated Dual-Split Trees

2020 ◽  
Vol 3 (2) ◽  
pp. 1-21
Author(s):  
Daqi Lin ◽  
Elena Vasiou ◽  
Cem Yuksel ◽  
Daniel Kopta ◽  
Erik Brunvand
Keyword(s):  
Ray Tracing ◽  
Hardware Acceleration ◽  
Memory Storage ◽  
Compact Representation ◽  
Space Partitioning ◽  
Data Movement ◽  
Bounding Volume ◽  
Bounding Boxes ◽  
Split Trees ◽  
Bounding Volume Hierarchies

Bounding volume hierarchies (BVH) are the most widely used acceleration structures for ray tracing due to their high construction and traversal performance. However, the bounding planes shared between parent and children bounding boxes is an inherent storage redundancy that limits further improvement in performance due to the memory cost of reading these redundant planes. Dual-split trees can create identical space partitioning as BVHs, but in a compact form using less memory by eliminating the redundancies of the BVH structure representation. This reduction in memory storage and data movement translates to faster ray traversal and better energy efficiency. Yet, the performance benefits of dual-split trees are undermined by the processing required to extract the necessary information from their compact representation. This involves bit manipulations and branching instructions which are inefficient in software. We introduce hardware acceleration for dual-split trees and show that the performance advantages over BVHs are emphasized in a hardware ray tracing context that can take advantage of such acceleration. We provide details on how the operations needed for decoding dual-split tree nodes can be implemented in hardware and present experiments in a number of scenes with different sizes using path tracing. In our experiments, we have observed up to 31% reduction in render time and 38% energy saving using dual-split trees as compared to binary BVHs representing identical space partitioning.

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