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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Disebabkan oleh pengehadan pengkomputeran awan pada kependaman, lebar jalur dan kerahsiaan data, pengkomputeran tepi telah muncul sebagai paradigma menyedari lokasi baharu untuk memberikan mereka lebih kapasiti pemprosesan untuk meningkatkan prestasi pengkomputeran dan kualiti perkhidmatan (QoS) dalam beberapa domain tipikal aktiviti manusia dalam masyarakat pintar, seperti rangkaian sosial, diagnosis perubatan, telekomunikasi, sistem pengesyoran, pengesanan ancaman dalaman, pengangkutan, Internet Perkara (IoT), dll. Domain aplikasi ini sering mengendalikan koleksi entiti yang besar dengan pelbagai perhubungan, yang boleh diwakili secara semula jadi oleh struktur data graf. Pemprosesan graf ialah alat yang berkuasa untuk memodelkan dan mengoptimumkan masalah kompleks yang melibatkan data berasaskan graf. Memandangkan peruntukan sumber terminal mudah alih yang agak tidak mencukupi, dalam makalah ini, untuk pertama kalinya untuk pengetahuan kami, kami mencadangkan perpustakaan pemprosesan graf (GPL) interaktif dan reduktif untuk pengkomputeran tepi dalam masyarakat pintar pada overhed rendah. Penilaian eksperimen dijalankan untuk menunjukkan bahawa GPL yang dicadangkan adalah lebih mesra pengguna dan berdaya saing tinggi berbanding dengan sistem lain yang ditetapkan, seperti igraph, NetworKit dan NetworkX, berdasarkan set data graf yang berbeza melalui pelbagai algoritma popular.
Jun ZHOU
Keio University
Masaaki KONDO
Keio University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
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
Salinan
@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},}
Salinan
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 -