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
Devido às limitações da computação em nuvem em termos de latência, largura de banda e confidencialidade de dados, a edge computing emergiu como um novo paradigma de reconhecimento de localização para fornecer-lhes mais capacidade de processamento para melhorar o desempenho da computação e a qualidade de serviço (QoS) em vários domínios típicos de atividade humana na sociedade inteligente, como redes sociais, diagnóstico médico, telecomunicações, sistemas de recomendação, detecção de ameaças internas, transportes, Internet das Coisas (IoT), etc. Esses domínios de aplicação geralmente lidam com uma vasta coleção de entidades com vários relacionamentos, que podem ser naturalmente representado pela estrutura de dados do gráfico. O processamento de gráficos é uma ferramenta poderosa para modelar e otimizar problemas complexos nos quais os dados baseados em gráficos estão envolvidos. Tendo em vista o provisionamento de recursos relativamente insuficiente dos terminais portáteis, neste artigo, pela primeira vez até onde sabemos, propomos uma biblioteca de processamento de gráficos (GPL) interativa e redutiva para computação de ponta na sociedade inteligente com baixa sobrecarga. A avaliação experimental é conduzida para indicar que a GPL proposta é mais amigável e altamente competitiva em comparação com outros sistemas estabelecidos, como igraph, NetworKit e NetworkX, baseados em diferentes conjuntos de dados gráficos em uma variedade de algoritmos populares.
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},}
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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 -