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A Practical Approach to Process Streaming Data using Graph Database

by Mukul Sharma, Narendra Singh Yadav
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 23
Year of Publication: 2015
Authors: Mukul Sharma, Narendra Singh Yadav
10.5120/20695-3588

Mukul Sharma, Narendra Singh Yadav . A Practical Approach to Process Streaming Data using Graph Database. International Journal of Computer Applications. 117, 23 ( May 2015), 28-32. DOI=10.5120/20695-3588

@article{ 10.5120/20695-3588,
author = { Mukul Sharma, Narendra Singh Yadav },
title = { A Practical Approach to Process Streaming Data using Graph Database },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 23 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number23/20695-3588/ },
doi = { 10.5120/20695-3588 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:27.235505+05:30
%A Mukul Sharma
%A Narendra Singh Yadav
%T A Practical Approach to Process Streaming Data using Graph Database
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 23
%P 28-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In today's information scenario, processing of data for exact knowledge has become a very important but critical task for the researchers and organizations. Involvement of Big Data and real time streaming data makes the data processing more challenging in order to extract and to visualize the exact data. In the most popular social media portals that includes streaming data like Twitter, Facebook and LinkedIn the rapidly growing information is updated several thousand times within one second. In this research, the extraction of streaming data from most popular social media portal (Facebook) is done using different SDKs, Graph API Explorer and other APIs and data processing is done using Cypher Query Language (CQL) and Neo4jCLient (C# API for Neo4j) in Microsoft C#. Then Neo4j Graph Database along with Microsoft Visual Studio 2013 is used for the Visualization and knowledge extraction. CQL facilitate the extraction of streaming data in an efficient manner as its development intended the processing of data in a linked manner. The processing and visualization of rapidly growing streaming data is done as linked data which makes the data and knowledge extraction very easy as the relationship between data is present along with the link between them.

References
  1. Jayanta Mondal and Amol Deshpande, "Stream Querying and Reasoning on Social Data", Department of Computer Science, University of Maryland, College Park MD 20742
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  3. R. Angles and C. Gutierrez," Survey of graph data-base models",. ACM Comput. Surv. , 40(1):1–39, 2008.
  4. Mukul Sharma and Pradeep Soni, "Quantitative Analysis and Implementation of Relational and Graph Database Technologies", International Journal of Modern Computer Science and Applications (IJMCSA) ISSN: 2321-2632 (Online) Volume No. -2, Issue No. -5, September, 2014
  5. D. F. Barbieri, D. Braga, S. Ceri, E. Della Valle, and M. Grossniklaus. "Continuous queries and real-time analysis of social semantic data with c-sparql", Web Workshop at the 8th International Semantic Web Conference, October 2009.
  6. The Neo4j Team , The Neo4j Manual v2. 0. 0-M03, Neo Technology, May 2013, Available http://www. neotechnology. com
  7. Weaver,Jesse, and Gregory Todd Williams, "Scalable RDF query processing on clusters and supercomputers. " The 5th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS2009), 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Graph Database Streaming Data Neo4j Microsoft C# Neo4jClient Cypher Query Language

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