Skip to main content
SpringerLink
Log in

PageRank for Billion-Scale Networks in RDBMS

  • Conference paper
  • First Online:
Advances in Intelligent Networking and Collaborative Systems (INCoS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1263))

Abstract

Data processing for Big Data plays a vital role for decision-makers in organizations and government, enhances the user experience, and provides quality results in prediction analysis. However, many modern data processing solutions make a significant investment in hardware and maintenance costs, such as Hadoop and Spark, often neglecting the well established and widely used relational database management systems (RDBMS’s). PageRank is vital in Google Search and social networks to determine how to sort search results and how influential a person is in a social group. PageRank is an iterative algorithm which imposes challenges when implementing it over large graphs which are becoming the norm with the current volume of data processed everyday from social networks, IOT, and web content. In this paper we study computing PageRank using RDBMS for very large graphs using a consumer-grade server and compare the results to a dedicated graph database .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914–925 (1993)

    Article  Google Scholar 

  2. Ahmed, A., Thomo, A.: Computing source-to-target shortest paths for complex networks in RDBMS. J. Comput. Syst. Sci. 89, 114–129 (2017)

    Article  MathSciNet  Google Scholar 

  3. Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. (CSUR) 40(1), 1–39 (2008)

    Article  Google Scholar 

  4. Bharat, K., Mihaila, G.A.: When experts agree: using non-affiliated experts to rank popular topics. In: Proceedings of the 10th International Conference on World Wide Web, pp. 597–602 (2001)

    Google Scholar 

  5. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998)

    Article  Google Scholar 

  6. Chakrabarti, S., Dom, B.E., Kumar, S.R., Raghavan, P., Rajagopalan, S., Tomkins, A., Gibson, D., Kleinberg, J.: Mining the web’s link structure. Computer 32(8), 60–67 (1999)

    Article  Google Scholar 

  7. Codd, E.F.: A relational model of data for large shared data banks. In: Broy, M., Denert, E. (eds.) Software Pioneers, pp. 263–294. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Eisenberg, A., Melton, J., Kulkarni, K., Michels, J.-E., Zemke, F.: SQL: 2003 has been published. ACM SIGMoD Rec. 33(1), 119–126 (2004)

    Article  Google Scholar 

  9. Gao, J., Zhou, J., Yu, J.X., Wang, T.: Shortest path computing in relational DBMSs. IEEE Trans. Knowl. Data Eng. 26(4), 997–1011 (2014)

    Article  Google Scholar 

  10. Jagadish, H., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R., Shahabi, C.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)

    Article  Google Scholar 

  11. Kelly, R.: Internet of things data to top 1.6 zettabytes by 2020. Campus Technology (2015)

    Google Scholar 

  12. Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Data Sets. Cambridge University Press, Cambridge (2020)

    Book  Google Scholar 

  13. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D., Barton, D.: Big data: the management revolution. Harv. Bus. Rev. 90(10), 60–68 (2012)

    Google Scholar 

  14. Ordonez, C., Omiecinski, E.: Efficient disk-based k-means clustering for relational databases. IEEE Trans. Knowl. Data Eng. 16(8), 909–921 (2004)

    Article  Google Scholar 

  15. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)

    Google Scholar 

  16. Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., et al.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)

    Article  Google Scholar 

  17. Zikopoulos, P., Eaton, C., et al.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Victoria, Victoria, BC, Canada

    Aly Ahmed & Alex Thomo

Authors
  1. Aly Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex Thomo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aly Ahmed .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada

    Kin Fun Li

  3. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyoshi Miwa

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ahmed, A., Thomo, A. (2021). PageRank for Billion-Scale Networks in RDBMS. In: Barolli, L., Li, K., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2020. Advances in Intelligent Systems and Computing, vol 1263. Springer, Cham. https://doi.org/10.1007/978-3-030-57796-4_9

Download citation

Publish with us

Policies and ethics

Search

Navigation