The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
En raison des limites du cloud computing en matière de latence, de bande passante et de confidentialité des données, l'edge computing est devenu un nouveau paradigme de localisation pour leur fournir plus de capacité de traitement afin d'améliorer les performances informatiques et la qualité de service (QoS) dans plusieurs domaines typiques de l'activité humaine dans la société intelligente, comme les réseaux sociaux, le diagnostic médical, les télécommunications, les systèmes de recommandation, la détection de menaces internes, les transports, l'Internet des objets (IoT), etc. Ces domaines d'application manipulent souvent un vaste ensemble d'entités entretenant des relations diverses, qui peuvent être naturellement représenté par la structure de données du graphique. Le traitement graphique est un outil puissant pour modéliser et optimiser des problèmes complexes dans lesquels les données basées sur des graphiques sont impliquées. Compte tenu de l'approvisionnement en ressources relativement insuffisant des terminaux portables, dans cet article, pour la première fois à notre connaissance, nous proposons une bibliothèque de traitement de graphes (GPL) interactive et réductrice pour l'informatique de pointe dans une société intelligente à faible coût. Une évaluation expérimentale est menée pour indiquer que la GPL proposée est plus conviviale et hautement compétitive par rapport à d'autres systèmes établis, tels que igraph, NetworKit et NetworkX, basés sur différents ensembles de données graphiques sur une variété d'algorithmes populaires.
Jun ZHOU
Keio University
Masaaki KONDO
Keio University
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Jun ZHOU, Masaaki KONDO, "An Interactive and Reductive Graph Processing Library for Edge Computing in Smart Society" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 3, pp. 319-327, March 2023, doi: 10.1587/transinf.2022FCP0008.
Abstract: Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide them with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of human activity in smart society, such as social networks, medical diagnosis, telecommunications, recommendation systems, internal threat detection, transports, Internet of Things (IoT), etc. These application domains often handle a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. Graph processing is a powerful tool to model and optimize complex problems in which the graph-based data is involved. In view of the relatively insufficient resource provisioning of the portable terminals, in this paper, for the first time to our knowledge, we propose an interactive and reductive graph processing library (GPL) for edge computing in smart society at low overhead. Experimental evaluation is conducted to indicate that the proposed GPL is more user-friendly and highly competitive compared with other established systems, such as igraph, NetworKit and NetworkX, based on different graph datasets over a variety of popular algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022FCP0008/_p
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@ARTICLE{e106-d_3_319,
author={Jun ZHOU, Masaaki KONDO, },
journal={IEICE TRANSACTIONS on Information},
title={An Interactive and Reductive Graph Processing Library for Edge Computing in Smart Society},
year={2023},
volume={E106-D},
number={3},
pages={319-327},
abstract={Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide them with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of human activity in smart society, such as social networks, medical diagnosis, telecommunications, recommendation systems, internal threat detection, transports, Internet of Things (IoT), etc. These application domains often handle a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. Graph processing is a powerful tool to model and optimize complex problems in which the graph-based data is involved. In view of the relatively insufficient resource provisioning of the portable terminals, in this paper, for the first time to our knowledge, we propose an interactive and reductive graph processing library (GPL) for edge computing in smart society at low overhead. Experimental evaluation is conducted to indicate that the proposed GPL is more user-friendly and highly competitive compared with other established systems, such as igraph, NetworKit and NetworkX, based on different graph datasets over a variety of popular algorithms.},
keywords={},
doi={10.1587/transinf.2022FCP0008},
ISSN={1745-1361},
month={March},}
Copier
TY - JOUR
TI - An Interactive and Reductive Graph Processing Library for Edge Computing in Smart Society
T2 - IEICE TRANSACTIONS on Information
SP - 319
EP - 327
AU - Jun ZHOU
AU - Masaaki KONDO
PY - 2023
DO - 10.1587/transinf.2022FCP0008
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2023
AB - Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide them with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of human activity in smart society, such as social networks, medical diagnosis, telecommunications, recommendation systems, internal threat detection, transports, Internet of Things (IoT), etc. These application domains often handle a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. Graph processing is a powerful tool to model and optimize complex problems in which the graph-based data is involved. In view of the relatively insufficient resource provisioning of the portable terminals, in this paper, for the first time to our knowledge, we propose an interactive and reductive graph processing library (GPL) for edge computing in smart society at low overhead. Experimental evaluation is conducted to indicate that the proposed GPL is more user-friendly and highly competitive compared with other established systems, such as igraph, NetworKit and NetworkX, based on different graph datasets over a variety of popular algorithms.
ER -