Graph Based Analytics Enhanced by Deep Learning
Authors: 
Strato Angsoteng Bayitaa, Ebenezer Nanor, Michael Y. Kpiebaareh, Brighter Agyemang, Wei-Ping WuAuthors Info & Claims
Pages 39 - 43
Abstract
We explore the use of graph analytics and deep learning to generate insight for decision support systems. The solution consists of two phases: graph analytics, and deep learning. In the first phase, we modelled data into graph and execute graph analytical algorithms and queries. The second phase consists of the deep learning model where an injection channel is used to "call" the data from the graph database and fed to the deep learning model for training and testing. Our experimental results demonstrate that this synergized approach has more benefits as opposed to using graph analytics or deep learning independently.
References
[1]
"Fortune Global 500 List 2018: See Who Made It." [Online]. Available: http://fortune.com/global500/list/filtered?sortBy=rankchange&rankgain=true. [Accessed: 29-Nov-2018].
[2]
J. Morley, K. Widdicks, and M. Hazas, "Digitalisation, energy and data demand: The impact of Internet traffic on overall and peak electricity consumption," Energy Res. Soc. Sci., vol. 38, pp. 128--137, 2018.
[3]
R. Lu, H. Zhu, X. Liu, J. K. Liu, and J. Shao, "Toward efficient and privacy-preserving computing in big data era," IEEE Netw., vol. 28, no. 4, pp. 46--50, 2014.
[4]
D. S. Linthicum, "Connecting fog and cloud computing," IEEE Cloud Comput., vol. 4, no. 2, pp. 18--20, 2017.
[5]
X. Wu, X. Zhu, G.-Q. Wu, and W. Ding, "Data mining with big data," IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97--107, 2014.
[6]
J. R. Evans and C. H. Lindner, "Business analytics: the next frontier for decision sciences," Decis. Line, vol. 43, no. 2, pp. 4--6, 2012.
[7]
B. Benatallah, H. R. Motahari-Nezhad, and others, "Scalable graph-based OLAP analytics over process execution data," Distrib. Parallel Databases, vol. 34, no. 3, pp. 379--423, 2016.
[8]
X. Huang, Y. Zhao, C. Ma, J. Yang, X. Ye, and C. Zhang, "TrajGraph: A graph-based visual analytics approach to studying urban network centralities using taxi trajectory data," IEEE Trans. Vis. Comput. Graph., vol. 22, no. 1, pp. 160--169, 2016.
[9]
Y. Gu, C. Wang, J. Ma, R. J. Nemiroff, and D. L. Kao, "iGraph: a graph-based technique for visual analytics of image and text collections," in Visualization and Data Analysis 2015, 2015, vol. 9397, p. 939708.
[10]
C. Fan et al., "Multi-horizon time series forecasting with temporal attention learning," in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 2527--2535.
[11]
K.-Y. Lin and J. J. P. Tsai, "A Deep Learning-Based Customer Forecasting Tool," in 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), 2016, pp. 198--205.
[12]
A. L. D. Loureiro, V. L. Miguéis, and L. F. M. da Silva, "Exploring the use of deep neural networks for sales forecasting in fashion retail," Decis. Support Syst., vol. 114, pp. 81--93, 2018.
[13]
R. Angles and C. Gutierrez, "Survey of graph database models," ACM Comput. Surv., vol. 40, no. 1, p. 1, 2008.
[14]
R. Hull and R. King, "Semantic database modeling: Survey, applications, and research issues," ACM Comput. Surv., vol. 19, no. 3, pp. 201--260, 1987.
[15]
E. F. Codd, "Relational database: a practical foundation for productivity," in Readings in Artificial Intelligence and Databases, Elsevier, 1989, pp. 60--68.
[16]
M. Needham and A. E. Hodler, Graph Algorithms: Practical Examples in Apache Spark and Neo4j. O'Reilly Media, 2019.
[17]
A. Petermann, M. Junghanns, R. Müller, and E. Rahm, "Graph-based data integration and business intelligence with BIIIG," Proc. VLDB Endow., vol. 7, no. 13, pp. 1577--1580, 2014.
[18]
Y. Kaneko and K. Yada, "A deep learning approach for the prediction of retail store sales," in 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016, pp. 531--537.
[19]
L. C. Freeman, "A set of measures of centrality based on betweenness," Sociometry, pp. 35--41, 1977.
[20]
G. Hinton et al., "Deep neural networks for acoustic modeling in speech recognition," IEEE Signal Process. Mag., vol. 29, 2012.
Index Terms
- Graph Based Analytics Enhanced by Deep Learning
Recommendations
Big data analytics deep learning techniques and applications: A survey
Highlights- This paper provides an in-depth review of the latest deep learning methods for use in big data analytics.
- Explain the importance of deep learning, its taxonomy, and big data analytics techniques.
- Explores deep learning approaches ...
AbstractDeep learning (DL), as one of the most active machine learning research fields, has achieved great success in numerous scientific and technological disciplines, including speech recognition, image classification, language processing, big data ...
Novel Scalable Deep Learning Approaches for Big Data Analytics Applied to ECG Processing
Big data analytics and deep learning are nowadays two of the most active research areas in computer science. As the data is becoming bigger and bigger, deep learning has a very important role to play in data analytics, and big data technologies will ...
Urban big data fusion based on deep learning: An overview
Highlights- Spatio-temporal data and common data fusion methods are systematically reviewed.
AbstractUrban big data fusion creates huge values for urban computing in solving urban problems. In recent years, various models and algorithms based on deep learning have been proposed to unlock the power of knowledge from urban big data. To ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
January 2020
279 pages
ISBN:9781450376730
DOI:10.1145/3379247
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 March 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCDE 2020
ICCDE 2020: 2020 The 6th International Conference on Computing and Data Engineering
January 4 - 6, 2020
Sanya, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 108Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 13 Jan 2025
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in