scholarly journals COLA: Optimizing Stream Processing Applications via Graph Partitioning

2009 ◽  
pp. 308-327 ◽  
Author(s):  
Rohit Khandekar ◽  
Kirsten Hildrum ◽  
Sujay Parekh ◽  
Deepak Rajan ◽  
Joel Wolf ◽  
...  
Keyword(s):  
Graph Partitioning ◽  
Stream Processing

Smart Community Edge: Stream Processing Edge Computing Node for Smart Community Services

2020 ◽  
Vol 140 (9) ◽  
pp. 1030-1039
Author(s):  
W.A. Shanaka P. Abeysiriwardhana ◽  
Janaka L. Wijekoon ◽  
Hiroaki Nishi
Keyword(s):  
Stream Processing ◽  
Community Services ◽  
Edge Computing ◽  
Computing Node ◽  
Smart Community

FINAL VERSION.pdf

2019 ◽  
Author(s):  
Nasir Saeed ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Graph Partitioning ◽  
Three Dimensional ◽  
Smart Objects ◽  
Localization Technique ◽  
Optical Internet ◽  
Spectral Graph ◽  
Accurate Localization ◽  
Spectral Graph Partitioning ◽  
Link Connectivity

<div>Localization is a fundamental task for optical internet</div><div>of underwater things (O-IoUT) to enable various applications</div><div>such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for OIoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor’s location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor’s location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable</div><div>sensors.</div>

RFID data stream processing technology based on CEP

2009 ◽  
Vol 29 (10) ◽  
pp. 2786-2790 ◽  
Author(s):  
Xiao-jia YIN ◽  
Shi-guang JU ◽  
Ying-jie WANG
Keyword(s):  
Data Stream ◽  
Stream Processing ◽  
Processing Technology ◽  
Data Stream Processing ◽  
Rfid Data

Mathematics of networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Walks ◽  
Adjacency Matrix ◽  
Graph Partitioning ◽  
Dynamic Networks ◽  
Graph Laplacian ◽  
Network Visualization ◽  
Final Part ◽  
Bipartite Networks ◽  
Component Structure ◽  
Basic Properties

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.

Video-rate, spatially variant filtering technique using stream processing architecture

1988 ◽  
Vol 24 (25) ◽  
pp. 1580
Author(s):  
D.J. Allerton ◽  
J.D. Evemy ◽  
E.J. Zaluska
Keyword(s):  
Stream Processing ◽  
Variant Filtering ◽  
Filtering Technique ◽  
Spatially Variant ◽  
Processing Architecture

Stream Processing Languages and Abstractions

2019 ◽  
pp. 1600-1607
Author(s):  
Martin Hirzel ◽  
Guillaume Baudart
Keyword(s):  
Stream Processing
Sign in / Sign up

Export Citation Format

Share Document