Skip to main content
SpringerLink
Log in

Workload characterization of a location-based social network

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Recently, there has been a large popularization of location-based social networks, such as Foursquare and Apontador, in which users can share their current locations, upload tips and make comments about places. Part of this popularity is due to facility access to the Internet through mobile devices with GPS. Despite the various efforts towards understanding characteristics of these systems, little is known about the access pattern of users in these systems. Providers of this kind of services need to deal with different challenges that could benefit of such understanding, such as content storage, performance and scalability of servers, personalization and service differentiation for users. This article aims at characterizing and modeling the patterns of requests that reach a server of a location-based social network. To do that, we use a dataset obtained from Apontador, a Brazilian system with characteristics similar to Foursquare and Gowalla, where users share information about their locations and can navigate on existent system locations. As results, we identified models that describe unique characteristics of the user sessions on this kind of system, patterns in which requests arrive on the server as well as the access profile of users in the system.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. www.apontador.com.br.

  2. http://www.apontador.com.br.

  3. Intentionally, the phone number of the site is partially hidden to force a person to do an extra click to view the number.

  4. http://api.apontador.com.br/.

  5. www.apontador.com.br/robots.txt

References

  • Arlitt M (2000) Characterizing web user sessions. SIGMETRICS Perform Eval Rev 28(2):50–63

    Article  Google Scholar 

  • Arlitt M, Jin T (1999) Workload characterization of the 1998 world cup web site. In: Technical Report HPL-1999-35R1

  • Arlitt M, Krishnamurthy D, Rolia J (2001) Characterizing the scalability of a large web-based shopping system. In ACM Trans Internet Technol, pp 44–69

  • Arlitt M, Williamson C (1996) Web server workload characterization: the search for invariants. SIGMETRICS Perform Eval Rev 24(1):126–137

    Article  Google Scholar 

  • Barford P, Bestavros A, Bradley A, Crovella M (1999) Changes in Web client access patterns: characteristics and caching implications. In: Proceedings of international conference on World Wide Web (WWW), pp 15–28

  • Barford P, Crovella M (1998) Generating representative web workloads for network and server performance evaluation. ACM SIGMETRICS Jt Intern Conf Measure Model Comput Syst 26:151–160

    Google Scholar 

  • Benevenuto F, Duarte F, Almeida V, Almeida J (2005) Web Cache replacement policies: properties, limitations and implications. In: Proceedings of Latin American Web Congress (La-Web)

  • Benevenuto F, Pereira A, Rodrigues T, Almeida V, Almeida J, Gontalves M (2010) Characterization and analysis of user profiles in online video sharing systems. J Info Data Manag 1(2):115–129

    Google Scholar 

  • Benevenuto F, Rodrigues T, Cha M, Almeida V (2009) Characterizing user behavior in online social networks. In: ACM SIGCOMM conference on Internet measurement conference (IMC), pp 49–62

  • Benevenuto F, Rodrigues T, Cha M, Almeida V (2012) Characterizing user navigation and interactions in online social networks. Inform Sci 195(15):1–24

    Article  Google Scholar 

  • Carrera D, Gavalda R, Torres J, Ayguade E (2010) Characterization of workload and resource consumption for an online travel and booking site. In: Proceedings of IEEE international symposium on workload characterization (IISWC), pp 1–10

  • Cha M, Kwak H, Rodriguez P, Ahn Y, Moon S (2007) I Tube, You Tube, Everybody Tubes: analyzing the world’s largest user generated content video system. In: ACM Internet Measurement Conference

  • Cho E, Myers S, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 1082–1090

  • Comarela G, Crovella M, Almeida V, Benevenuto F (2012) Understanding factors that affect response rates in twitter. In: Proceedings of the ACM conference on hypertext and social media (HT), pp 123–132

  • Inc. comScore (2011) Nearly 1 in 5 smartphone owners access check-in services via their mobile device. http://bit.ly/mgaCIG

  • Costa C, Cunha I, Vieira A, Ramos C, Rocha M, Almeida J, Ribeiro-Neto B (2004) Analyzing client interactivity in streaming media. In: World Wide Web Conference (WWW), pp 534–543

  • Duarte F, Mattos B, Bestavros A, Almeida V, Almeida J (2007) Traffic characteristics and communication patterns in blogosphere. In Proceedings international conference on weblogs and social media (ICWSM)

  • Erramillia V, Yanga X, Rodriguez P (2012) Explore what-if scenarios with song: social network write generator. http://arxiv.org/abs/1102.0699

  • Fan L, Cao P, Almeida J, Broder A (2000) Summary cache: a scalable wide-area web cache sharing protocol. IEEE / ACM Trans Netw 8(3):281–293

    Article  Google Scholar 

  • Gavras A, Karila A, Fdida S, May M, Potts M (2007) Future internet research and experimentation: the fire initiative. SIGCOMM Comput Commun Rev 37:89–92

    Article  Google Scholar 

  • Gill P, Arlitt M, Li Z, Mahanti A (2007) Youtube traffic characterization: a view from the edge. In: ACM SIGCOMM conference on internet measurement (IMC)

  • Gill P, Arlitt M, Li Z, Mahanti A (2008) Characterizing user sessions on youtube, In: IEEE Multimedia Computing and Networking (MMCN)

  • Khan A, Yan X, Shu T, Anerousis N (2012) Workload characterization and prediction in the cloud: a multiple time series approach. In: IEEE network operations and management symposium (NOMS), pp 1287–1294

  • Krishnamurthy D, Rolia J, Majumdar S (2006) A synthetic workload generation technique for stress testing session-based systems. IEEE Trans Softw Eng 32:868–882

    Article  Google Scholar 

  • Key Facts (2012) Facebook Newsroom. http://newsroom.fb.com/Key-Facts

  • Menascé D, Almeida V (2000) Scaling for E business: technologies, models, performance, and capacity planning. Prentice Hall PTR, Upper Saddle River

    Google Scholar 

  • Menascé D, Almeida V, Fonseca R, Mendes M (1999) A methodology for workload characterization of e-commerce sites. In: ACM conference on electronic commerce (EC)

  • Needle in a Haystack (2009) Efficient storage of Billions of Photos, Facebook Engineering Notes, http://tinyurl.com/cju2og

  • Noulas A, Mascolo C, Scellato S, Pontil M (2011) Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. SMW 2011

  • Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in foursquare. In: International conference on weblogs and social media

  • Oke Ad, Bunt R (2002) Hierarchical workload characterization for a busy web server. In: International conference on computer performance evaluation, modelling techniques and tools (TOOLS)

  • Pereira A, Silva L, Meira Jr W (2006) Evaluating the impact of reactive workloads on the performance of web applications. In: Proceedings of the 25th IEEE international performance, computing, and communications ccnference (IPCCC), Phoenix, Arizona, IEEE CS

  • Rodrigues T, Benevenuto F, Cha M, Gummadi K, Almeida V (2011) On word-of-mouth based discovery of the web. In: ACM SIGCOMM internet measurement conference (IMC), pp 381–393

  • Scellato S (2011) Beyond the social web: the geo-social revolution. SIGWEB Newslett, pp 5:1–5:5

  • Scellato S, Mascolo C, Musolesi M, Crowcroft J (2011) Track globally, deliver locally: Improving content delivery networks by tracking geographic social cascades. In: Proceedings of international conference on world wide web (WWW), pp 457–466

  • Schneider F, Feldmann A, Krishnamurthy B, Willinger W (2009) Understanding online social network usage from a network perspective. In: ACM SIGCOMM Internet Measurement Conference (IMC), pp 35–48

  • Vasconcelos M, Ricci S, Almeida J, Benevenuto F, Almeida V (2012) Caracterizacpo e influOncia do uso de tips e dones no foursquare. In: Simp=sio Brasileiro de Redes de Computadores e Sistemas Distribufdos (SBRC)

  • Vasconcelos M, Ricci S, Almeida J, Benevenuto F, Almeida V (2012) Tips, dones and to-dos: uncovering user profiles in foursquare. In: ACM international conference of web search and data mining (WSDM)

  • Veloso E, Almeida V, Meira W Jr, Bestavros A, Jin S (2006) A hierarchical characterization of a live streaming media workload. IEEE/ACM Trans Netw 14(1):133–146

    Article  Google Scholar 

  • Wang J (1999) A survey of web caching schemes for the internet. ACM Comput Commun Rev 25(9):36–46

    Article  Google Scholar 

  • Wittie M, Pejovic V, Deek L, Almeroth K, Zhao B (2010) Exploiting locality of interest in online social networks. In: Proceedings of ACM international conference on emerging networking experiments and technologies (CoNEXT), pp 1–12

  • Xi H, Zhan J, Jia Z, Hong X, Wang L, Zhang L, Sun N, Lu G (2011) Characterization of real workloads of web search engines. In: Proceedings of IEEE international symposium on workload characterization (IISWC), pp 15–25

  • YouTube Fact Sheet (2011) http://www.youtube.com/t/fact_sheet Acessado em Dezembro/2012

Download references

Acknowledgments

This research has been supported by CAPES, CNPq, and Fapemig. The authors also would like to thank Apontador Inc. for the data provided.

Author information

Authors and Affiliations

  1. Computer Science Department (DCC), Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil

    Theo Lins, Adriano C. M. Pereira & Fabrício Benevenuto

Authors
  1. Theo Lins
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adriano C. M. Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabrício Benevenuto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabrício Benevenuto.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lins, T., Pereira, A.C.M. & Benevenuto, F. Workload characterization of a location-based social network. Soc. Netw. Anal. Min. 4, 209 (2014). https://doi.org/10.1007/s13278-014-0209-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-014-0209-1

Keywords

Search

Navigation