SRelation: Fast RDF Graph Traversal

Voronoi Graph Traversal in High Dimensions with Applications to Topological Data Analysis and Piecewise Linear Interpolation

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining ◽

10.1145/3394486.3403266 ◽

2020 ◽

Author(s):

Vladislav Polianskii ◽

Florian T. Pokorny

Keyword(s):

Data Analysis ◽

Piecewise Linear ◽

Linear Interpolation ◽

Topological Data Analysis ◽

High Dimensions ◽

Graph Traversal ◽

Piecewise Linear Interpolation ◽

Voronoi Graph ◽

Topological Data

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Self-adaptive Graph Traversal on GPUs

Proceedings of the 2021 International Conference on Management of Data ◽

10.1145/3448016.3457279 ◽

2021 ◽

Author(s):

Mo Sha ◽

Yuchen Li ◽

Kian-Lee Tan

Keyword(s):

Graph Traversal ◽

Self Adaptive

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Graph traversal and graph transformation

Theoretical Computer Science ◽

10.1016/j.tcs.2004.03.034 ◽

2004 ◽

Vol 321 (2-3) ◽

pp. 215-231

Author(s):

Jason J Holdsworth

Keyword(s):

Graph Transformation ◽

Graph Traversal

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Fixing the Inconsistencies in Fuzzy Spatiotemporal RDF Graph

Information Sciences ◽

10.1016/j.ins.2021.07.038 ◽

2021 ◽

Author(s):

Luyi Bai ◽

Jinyao Wang ◽

Xiaofeng Di ◽

Nan Li

Keyword(s):

Rdf Graph

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Graph traversal aided detection in uplink MBM massive MIMO based on socio‐cognitive knowledge of swarm optimization

International Journal of Communication Systems ◽

10.1002/dac.4720 ◽

2021 ◽

Vol 34 (5) ◽

Author(s):

Arijit Datta ◽

Vimal Bhatia ◽

Manish Mandloi ◽

Ganapati Panda

Keyword(s):

Massive Mimo ◽

Swarm Optimization ◽

Graph Traversal ◽

Cognitive Knowledge

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You Only Traverse Twice: A YOTT Placement, Routing, and Timing Approach for CGRAs

ACM Transactions on Embedded Computing Systems ◽

10.1145/3477038 ◽

2021 ◽

Vol 20 (5s) ◽

pp. 1-25

Author(s):

Michael Canesche ◽

Westerley Carvalho ◽

Lucas Reis ◽

Matheus Oliveira ◽

Salles Magalhães ◽

...

Keyword(s):

Execution Time ◽

High Performance ◽

Coarse Grained ◽

Optimal Placement ◽

Greedy Heuristics ◽

High Quality ◽

Solution Quality ◽

Graph Traversal ◽

Trade Offs ◽

Graph Properties

Coarse-grained reconfigurable architecture (CGRA) mapping involves three main steps: placement, routing, and timing. The mapping is an NP-complete problem, and a common strategy is to decouple this process into its independent steps. This work focuses on the placement step, and its aim is to propose a technique that is both reasonably fast and leads to high-performance solutions. Furthermore, a near-optimal placement simplifies the following routing and timing steps. Exact solutions cannot find placements in a reasonable execution time as input designs increase in size. Heuristic solutions include meta-heuristics, such as Simulated Annealing (SA) and fast and straightforward greedy heuristics based on graph traversal. However, as these approaches are probabilistic and have a large design space, it is not easy to provide both run-time efficiency and good solution quality. We propose a graph traversal heuristic that provides the best of both: high-quality placements similar to SA and the execution time of graph traversal approaches. Our placement introduces novel ideas based on “you only traverse twice” (YOTT) approach that performs a two-step graph traversal. The first traversal generates annotated data to guide the second step, which greedily performs the placement, node per node, aided by the annotated data and target architecture constraints. We introduce three new concepts to implement this technique: I/O and reconvergence annotation, degree matching, and look-ahead placement. Our analysis of this approach explores the placement execution time/quality trade-offs. We point out insights on how to analyze graph properties during dataflow mapping. Our results show that YOTT is 60.6 , 9.7 , and 2.3 faster than a high-quality SA, bounding box SA VPR, and multi-single traversal placements, respectively. Furthermore, YOTT reduces the average wire length and the maximal FIFO size (additional timing requirement on CGRAs) to avoid delay mismatches in fully pipelined architectures.

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Graph Traversal

Sequential and Parallel Algorithms and Data Structures ◽

10.1007/978-3-030-25209-0_9 ◽

2019 ◽

pp. 271-299

Author(s):

Peter Sanders ◽

Kurt Mehlhorn ◽

Martin Dietzfelbinger ◽

Roman Dementiev

Keyword(s):

Graph Traversal

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Robust Software Watermarking for Protecting Three Dimension Applications Developed Using jMonkey Engine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.268-270.1873 ◽

2012 ◽

Vol 268-270 ◽

pp. 1873-1876

Author(s):

Dong Lai Fu

Keyword(s):

New Method ◽

Three Dimension ◽

Numbering System ◽

Scene Graph ◽

Graph Traversal ◽

Initial Sequence ◽

Important Clue ◽

Software Watermarking

To prevent three dimension applications developed using jMonkey Engine from being pirated, a robust software watermarking algorithm was proposed. A watermark based on an initial sequence of scene-graph-traversal was embedded into the software by a numbering system. Since the initial sequence was a very important clue for extracting the embedded watermark, it should be only known by the owner. Furthermore, the algorithm for embedding and extracting the watermark was discussed. Analysis shows that the new method is more robust. And it enjoys the advantage that the size of scene graph does not be changed.

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