Skip to main content
SpringerLink
Log in
Book cover

International Conference on Database Systems for Advanced Applications

DASFAA 2020: Database Systems for Advanced Applications pp 272–287Cite as

Dynamic Dimension Indexing for Efficient Skyline Maintenance on Data Streams

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12114))

Abstract

Skyline computation receives much attention in research and application domains, for which many algorithms have been developed during decades. However, maintaining the skyline in data streams is much challenging because of the continuous updates of skyline with respect to non stop adding of incoming tuples and removing of expired tuples. In this paper, we present a dynamic dimension indexing based approach RSS to skyline computation on high dimensional data streams, which is efficient at both count-based and time-based sliding windows regardless the dimensionality of data. Our analysis shows that the time complexity of RSS is bounded by a subset of the instant skyline, and our evaluation shows the efficiency of RSS on both of low and high dimensional data streams.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    Any skyline algorithm can be applied to compute local skylines, BNL is the simplest one with respect to a small number of repeated dimension values.

  2. 2.

    https://github.com/skyline-sdi/sdi-rss.

  3. 3.

    http://pgfoundry.org/projects/randdataset.

  4. 4.

    https://github.com/sean-chester/SkyBench.

References

  1. Bartolini, I., Ciaccia, P., Patella, M.: Efficient sort-based skyline evaluation. ACM Trans. Database Syst. 33(4), 31 (2008)

    Article  Google Scholar 

  2. Borzsony, S., Kossmann, D., Stocker, K.: The Skyline operator. In: ICDE, pp. 421–430 (2001)

    Google Scholar 

  3. Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: ICDE, vol. 3, pp. 717–719 (2003)

    Google Scholar 

  4. Godfrey, P., Shipley, R., Gryz, J.: Maximal vector computation in large data sets. In: VLDB, pp. 229–240 (2005)

    Google Scholar 

  5. Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. In: VLDB, pp. 275–286 (2002)

    Google Scholar 

  6. Lee, J., Hwang, S.-W.: Scalable skyline computation using a balanced pivot selection technique. Inf. Syst. 39, 1–21 (2014)

    Article  Google Scholar 

  7. Lee, K.C.K., Lee, W.-C., Zheng, B., Li, H., Tian, Y.: Z-SKY: an efficient skyline query processing framework based on Z-order. VLDB J. 19(3), 333–362 (2010)

    Google Scholar 

  8. Lin, X., Yuan, Y., Wang, W., Lu, H.: Stabbing the sky: efficient skyline computation over sliding windows. In: ICDE, pp. 502–513 (2005)

    Google Scholar 

  9. Liu, R., Li, D.: Efficient skyline computation in high-dimensionality domains. In: EDBT (2020)

    Google Scholar 

  10. Morse, M., Patel, J.M., Grosky, W.I.: Efficient continuous skyline computation. Inf. Sci. 177(17), 3411–3437 (2007)

    Google Scholar 

  11. Papadias, D., Tao, Y., Greg, F., Seeger, B.: Progressive skyline computation in database systems. ACM Trans. Database Syst. 30(1), 41–82 (2005)

    Article  Google Scholar 

  12. Patroumpas, K., Sellis, T.: Window specification over data streams. In: EDBT, pp. 445–464 (2006)

    Google Scholar 

  13. Sheng, C., Tao, Y.: Worst-case I/O-efficient skyline algorithms. ACM Trans. Database Syst. 37(4), 26 (2012)

    Article  Google Scholar 

  14. Tan, K.-L., Eng, P.-K., Ooi, B.C.: Efficient progressive skyline computation. In: VLDB, pp. 301–310 (2001)

    Google Scholar 

  15. Tao, Y., Papadias, D.: Maintaining sliding window skylines on data streams. IEEE Trans. Knowl. Data Eng. 18(03), 377–391 (2006)

    Article  Google Scholar 

  16. Lin, X., Lu, H., Xu, J., Yu, J.X.: Continuously maintaining quantile summaries of the most recent n elements over a data stream. In: ICDE, pp. 362–373 (2004)

    Google Scholar 

  17. Zhang, S., Mamoulis, N., Cheung, D.W.: Scalable skyline computation using object-based space partitioning. In: SIGMOD, pp. 483–494 (2009)

    Google Scholar 

  18. Zhang, S., Mamoulis, N., Cheung, D.W., Kao, B.: Efficient skyline evaluation over partially ordered domains. PVLDB 3(1–2), 1255–1266 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Tours, 41000, Blois, France

    Rui Liu & Dominique Li

  2. LIFAT Laboratory, 37000, Tours, France

    Dominique Li

Authors
  1. Rui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dominique Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dominique Li .

Editor information

Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Peking University, Haidian, China

    Bin Cui

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Sang-Won Lee

  4. Department of Systems Engineering and En, The Chinese University of Hong Kong, Hong Kong, Hong Kong

    Jeffrey Xu Yu

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  6. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Steven Euijong Whang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, R., Li, D. (2020). Dynamic Dimension Indexing for Efficient Skyline Maintenance on Data Streams. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59419-0_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59418-3

  • Online ISBN: 978-3-030-59419-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation