Lazy View Maintenance for Social Networking Applications

  • Keita Mikami
  • Shinji Morishita
  • Makoto Onizuka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)


We introduce CAMEL, a lazy view maintenance system for social networking applications on a database server with a distributed memory cache. System administrators can control the throughput of the system by tuning the level of freshness of materialized views. CAMEL employs the existing view maintenance techniques of incremental maintenance, lazy maintenance, and control table. In addition, CAMEL optimizes view maintenance performance by pushing the top-k operation down to before join operations and by constructing a reverse index. We evaluate CAMEL using real data from a mini-blog service. The results show that CAMEL is 6.13 and 11.2 times faster than the method of eager view maintenance while keeping the freshness of materialized views at 66.2% and 38.0%, respectively.


Database Server Cache Data Memory Cache Control Table Base Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cashmore, P.: MySpace, America’s Number One (2006),
  2. 2.
  3. 3.
    Arrington, M.: End Of Speculation: The Real Twitter Usage Numbers (2008),
  4. 4.
    Gupta, A., Mumick, I.S.: Maintenance of materialized views: Problems, techniques and applications. IEEE Data Engineering Bulletin 18(2), 3–18 (1995)Google Scholar
  5. 5.
    Zhou, J., Larson, P.Å., Goldstein, J., Ding, L.: Dynamic materialized views. In: Proceedings of ICDE, pp. 526–535 (2007)Google Scholar
  6. 6.
    Colby, L.S., Griffin, T., Libkin, L., Mumick, I.S., Trickey, H.: Algorithms for deferred view maintenance. SIGMOD Rec. 25(2), 469–480 (1996)CrossRefGoogle Scholar
  7. 7.
    Owyang, J.: Understanding HP Lab’s Twitter Research research (2008),
  8. 8.
    Howard, B.: Analyzing online social networks. Commun. ACM 51(11), 14–16 (2008)CrossRefGoogle Scholar
  9. 9.
    Hanson, E.N.: A performance analysis of view materialization strategies. SIGMOD Rec. 16(3), 440–453 (1987)CrossRefGoogle Scholar
  10. 10.
    Johnson, T., Shasha, D.: 2Q: A low overhead high performance buffer management replacement algorithm. In: Proceedings of VLDB, pp. 439–450 (1994)Google Scholar
  11. 11.
    Agrawal, R., Dewitt, D.J.: Updating Hypothetical Data Bases. Information Processang Letters 16, 145–146 (1983)CrossRefGoogle Scholar
  12. 12.
    Salem, K., Beyer, K., Lindsay, B., Cochrane, R.: How to roll a join: asynchronous incremental view maintenance. SIGMOD Rec. 29(2), 129–140 (2000)CrossRefGoogle Scholar
  13. 13.
    Zhou, J., Larson, P.A., Elmongui, H.G.: Lazy maintenance of materialized views. In: Proceedings of VLDB, pp. 231–242 (2007)Google Scholar
  14. 14.
    Quass, D., Widom, J.: On-line warehouse view maintenance. SIGMOD Rec. 26(2), 393–404 (1997)CrossRefGoogle Scholar
  15. 15.
    Agrawal, P., Silberstein, A., Cooper, B.F., Srivastava, U., Ramakrishnan, R.: Asynchronous view maintenance for VLSD databases. In: Proceedings of SIGMOD, pp. 179–192. ACM, New York (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Keita Mikami
    • 1
  • Shinji Morishita
    • 1
  • Makoto Onizuka
    • 1
  1. 1.NTT CyberSpace LaboratoriesNTT CorporationJapan

Personalised recommendations