A Study on Big Data-based GraphX Model for Social Network Service

Leesang Cho (조이상); KIM, JIN HONG (김진홍)

doi:10.34163/jkits.2020.15.6.004

A Study on Big Data-based GraphX Model for Social Network Service

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2020, 15(6), pp.955-962
DOI : 10.34163/jkits.2020.15.6.004
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Received : November 27, 2020
Accepted : December 11, 2020
Published : December 31, 2020

Leesang Cho ¹, KIM, JIN HONG ²

¹한성대학교
²배재대학교

Accredited

ABSTRACT

Nowaday, towards adopting big data processing system has increased and it is commonly seen in every aspect of life. For this, the problem of finding connected components in undirected graphs has been well studied, and it is an essential pre-processing step to many graph computations, and a fundamental task in graph analytics applications. Recently, it has been a main area of interest in the large graph processing. However, much of the research has focused on solving the problem using High Performance Computers. In large distributed systems, the MapReduce framework dominates the processing of big data, and has been used for finding connected components in big graphs although iterative processing is not directly supported in MapReduce. Current big data processing systems have developed into supporting iterative processing and providing additional features other than MapReduce. This research investigates how to enhance the performance of finding connected components algorithm for large graph in distributed processing system. It uses the approach to considering the graph degree property in choosing the component identifier, reviewing how this can affect the efficiency of the algorithm. In the design of our proposed algorithm features provided by current new processing systems such as moving the computation more toward the data partition in Spark framework model are integrated.

KEYWORDS

Graph analytics applications, Large graph processing, Distributed systems, Big data processing systems, Spark framework model

Citation status

* References for papers published after 2023 are currently being built.

[journal] 황성태 / 2020 / A Study on Big Data Platform Architecture-based Conceptual Measurement Model Using Comparative Analysis for Social Commerce / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 15(5) : 623~630

[journal] 김정식 / 2020 / A Study on Adaptive Smart Platform for Intelligent Software in Big Data Environment / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 15(3) : 347~355

[journal] 장효경 / 2020 / Context Recognition Method for Personalization Service in Bigdata Environment / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 15(5) : 631~638

[confproc] T. Akidau / 2015 / The dataflow model: A practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing / Proceeding VLDB Endow 8(12) : 1792~1803

[confproc] P. Alvaro / 2011 / Consistency analysis in bloom:A calm and collected approach / Proceedings 5th Biennial Conference on Innovative Data Systems Research : 249~260

[confproc] B. Schilit / 1994 / Context -aware computing applications / First Work shop on Mobile Computing Systems and Applications : 85~90

[confproc] D. Salber / 2000 / The context toolkit: aiding the development of context Aware applications / the Workshop on Software Engineering for Wearable and Pervasive Computing

[journal] N. Pandeeswari / 2016 / Anomaly detection system in cloud environment using fuzzy clustering based ANN / Mob. Netw. Appl 21(3) : 494~505

[confproc] R. J. Hyndman / 2015 / Large-scale unusual time series detection / 2015 IEEE International Conference on Data Mining Workshop (ICDMW) / IEEE : 1616~1619

[confproc] B. Agrawal / 2014 / R2Time: A framework to analyse open TSDB time-series data in HBase / Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference / : 970~975

[confproc] C. Wang / 2010 / Online detection of utility cloud anomalies using metric distributions / Network Operations and Management Symposium (NOMS), 2010 IEEE / : 96~103

[confproc] M. Jahrer / 2012 / Ensemble of collaborative filtering and feature engineered models for click through rate prediction / KDDCup Workshop

[confproc] P. Gaikwad / 2016 / Anomaly detection for scientific workflow applications on networked clouds / High Performance Computing &Simulation (HPCS), 2016 International Conference on / IEEE : 645~652

[confproc] U. Kang / 2011 / Mining large graphs: Algorithms, inference, and discoveries / 2011 IEEE 27th International Conference on Data Engineering / : 243~254

[book] T. Rabl / 2014 / Advancing Big Data Benchmarks, Vol. 8585 / Springer International Publishing

KJCKorea
Journal Central

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study on Big Data-based GraphX Model for Social Network Service

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study on Big Data-based GraphX Model for Social Network Service

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (2)

REFERENCES (15) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.