An Empirical Analysis to Identify the Effect of Indexing on Influence Detection using Graph Databases
Mitali Desai1, Rupa G. Mehta2, Dipti P. Rana3
1Mitali Desai, Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat (Gujarat), India.
2Rupa G. Mehta, Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat (Gujarat), India.
3Dipti P. Rana, Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat (Gujarat), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 26 August 2019 | PP: 414-421 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10660789S19/19©BEIESP | DOI: 10.35940/ijitee.I1066.0789S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The data generated on social media platforms such as Twitter, Facebook, LinkedIn etc. are highly connected. Such data can be efficiently stored and analyzed using graph databases due to the inherent property of graphs to model connected data. To reduce the time complexity of data retrieval from huge graph databases, various indexing techniques are used. This paper presents an extensive empirical analysis on popular graph databases i.e. Neo4j, ArangoDB and OrientDB; with an aim to measure the competencies and effectiveness of primitive indexing techniques on query response time to identify the influencing entities from Twitter data. The analysis demonstrates that Neo4j performs efficient and stable for load, relation and property queries compare to other two databases whereas the performance of OrientDB can be improved using primitive indexing.
Keywords: Graph Database, Index, Influence Detection, Query Processing, Twitter.
Scope of the Article: Structural Reliability Analysis