Skip to main content
Springer Nature Link
Log in

Computing Online Average Happiness Maximization Sets over Data Streams

  • Conference paper
  • First Online:
Web and Big Data (APWeb-WAIM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13423))

  • 876 Accesses

Abstract

Finding a small subset representing a large dataset is an important functionality in many real applications such as data mining, recommendation and web search. The average happiness maximization set problem also known as the average regret minimization set problem was recently proposed to fulfill this task and it can additionally satisfy users on average with the representative subset. In this paper, we study the online average happiness maximization set (Online-AHMS) problem over data streams where each data point should be decided to be accepted or discarded when it arrives, and the discarded data points will never be considered. We provide an efficient online algorithm named GreedyAT with theoretical guarantees for the Online-AHMS problem which greedily selects data points based on the adaptive thresholds strategy. Experimental results on the synthetic and real datasets demonstrate the efficiency and effectiveness of our GreedyAT algorithm.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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

Similar content being viewed by others

Notes

  1. 1.

    https://web.archive.org/.

References

  1. Agarwal, P.K., Kumar, N., Sintos, S., Suri, S.: Efficient algorithms for \({k}\)-regret minimizing sets. In: SEA, pp. 7:1–7:23 (2017)

    Google Scholar 

  2. Alami, K., Maabout, S.: A framework for multidimensional skyline queries over streaming data. Data Knowl. Eng. 127, 101792 (2020)

    Article  Google Scholar 

  3. Asudeh, A., Nazi, A., Zhang, N., Das, G.: Efficient computation of regret-ratio minimizing set: a compact maxima representative. In: SIGMOD, pp. 821–834 (2017)

    Google Scholar 

  4. Asudeh, A., Nazi, A., Zhang, N., Das, G., Jagadish, H.V.: RRR: rank-regret representative. In: SIGMOD, pp. 263–280 (2019)

    Google Scholar 

  5. Börzsöny, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)

    Google Scholar 

  6. Buchbinder, N., Feldman, M., Schwartz, R.: Online submodular maximization with preemption. ACM Trans. Algorithms. 15(3), 30:1–30:31 (2019)

    Google Scholar 

  7. Cao, W., et al.: \({k}\)-regret minimizing set: Efficient algorithms and hardness. In: ICDT, pp. 11:1–11:19 (2017)

    Google Scholar 

  8. Chakrabarti, A., Kale, S.: Submodular maximization meets streaming: matchings, matroids, and more. Math. Program. 154(1–2), 225–247 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chan, T.H.H., Huang, Z., Jiang, S.H.C., Kang, N., Tang, Z.G.: Online submodular maximization with free disposal. ACM Trans. Algorithms. 14(4), 56:1–56:29 (2018)

    Google Scholar 

  10. Chester, S., Thomo, A., Venkatesh, S., Whitesides, S.: Computing \({k}\)-regret minimizing sets. In: VLDB, pp. 389–400 (2014)

    Google Scholar 

  11. Chomicki, J., Ciaccia, P., Meneghetti, N.: Skyline queries, front and back. SIGMOD Rec. 42(3), 6–18 (2013)

    Article  Google Scholar 

  12. Chu, W., Ghahramani, Z.: Preference learning with gaussian processes. In: ICML, pp. 137–144 (2005)

    Google Scholar 

  13. Faulkner, T.K., Brackenbury, W., Lall, A.: \({k}\)-regret queries with nonlinear utilities. In: VLDB, pp. 2098–2109 (2015)

    Google Scholar 

  14. Feige, U., Mirrokni, V.S., Vondrak, J.: Maximizing non-monotone submodular functions. SIAM J. Comput. (SICOMP) 40(4), 1133–1153 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  15. Gomes, R., Krause, A.: Budgeted nonparametric learning from data streams. In: ICML, pp. 391–398 (2010)

    Google Scholar 

  16. Houlsby, N., Huszar, F., Z. Ghahramani, Z., Hernández-lobato, J.: Collaborative gaussian processes for preference learning. In: NIPS, pp. 2096–2104 (2012)

    Google Scholar 

  17. Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-\({k}\) query processing techniques in relational database systems. CSUR. 40(4), 11:1–11:58 (2008)

    Google Scholar 

  18. Li, Y., et al.: Hyperbolic hypergraphs for sequential recommendation. In: CIKM, pp. 988–997 (2021)

    Google Scholar 

  19. Luenam, P., Chen, Y.P., Wong, R.C.: Approximating happiness maximizing set problems. CoRR abs/2102.03578, pp. 1–13 (2021)

    Google Scholar 

  20. Ma, W., Zheng, J., Hao, Z.: A coreset based approach for continuous \({k}\)-regret minimization set queries over sliding windows. In: WISA, pp. 49–61 (2021)

    Google Scholar 

  21. Nanongkai, D., Sarma, A., Lall, A., Lipton, R., Xu, J.: Regret-minimizing representative databases. In: VLDB, pp. 1114–1124 (2010)

    Google Scholar 

  22. Nanongkai, D., Lall, A., Das Sarma, A., Makino, K.: Interactive regret minimization. In: SIGMOD, pp. 109–120 (2012)

    Google Scholar 

  23. Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions-i. Math. Program. 14(1), 265–294 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  24. Peng, P., Wong, R.C.W.: Geometry approach for \({k}\)-regret query. In: ICDE, pp. 772–783 (2014)

    Google Scholar 

  25. Qi, J., Zuo, F., Samet, H., Yao, J.C.: \({k}\)-regret queries using multiplicative utility functions. TODS. 43(2), 10:1–10:41 (2018)

    Google Scholar 

  26. Qiu, X., Zheng, J., Dong, Q., Huang, X.: Speed-up algorithms for happiness-maximizing representative databases. In: APWebWAIM DS Workshop, pp. 321–335 (2018)

    Google Scholar 

  27. Qu, M., Ren, X., Han, J.: Automatic synonym discovery with knowledge bases. In: KDD, pp. 997–1005 (2017)

    Google Scholar 

  28. Storandt, S., Funke, S.: Algorithms for average regret minimization. In: AAAI, pp. 1600–1607 (2019)

    Google Scholar 

  29. Stoyanovich, J., Yang, K., Jagadish, H.: Online set selection with fairness and diversity constraints. In: EDBT, pp. 241–252 (2018)

    Google Scholar 

  30. Wang, Y., Li, Y., Tan, K.: Efficient representative subset selection over sliding windows. TKDE 31(7), 1327–1340 (2019)

    Google Scholar 

  31. Wang, Y., Mathioudakis, M., Li, Y., Tan, K.: Minimum coresets for maxima representation of multidimensional data. In: PODS, pp. 138–152 (2021)

    Google Scholar 

  32. Wang, Y., Li, Y., Wong, R.C.W., Tan, K.L.: A fully dynamic algorithm for \({k}\)-regret minimizing sets. In: ICDE, pp. 1631–1642 (2021)

    Google Scholar 

  33. Xiao, X., Li, J.: Rank-regret minimization. CoRR abs/2111.08563, pp. 1–15 (2021)

    Google Scholar 

  34. Xie, M., Wong, R.C.W., Lall, A.: Strongly truthful interactive regret minimization. In: SIGMOD, pp. 281–298 (2019)

    Google Scholar 

  35. Xie, M., Wong, R.C.W., Lall, A.: An experimental survey of regret minimization query and variants: bridging the best worlds between top-\({k}\) query and skyline query. VLDB J. 29, 147–175 (2020)

    Article  Google Scholar 

  36. Xie, M., Wong, R.C.W., Li, J., Long, C., Lall, A.: Efficient \({k}\)-regret query algorithm with restriction-free bound for any dimensionality. In: SIGMOD, pp. 959–974 (2018)

    Google Scholar 

  37. Xie, M., Wong, R.C.W., Peng, P., Tsotras, V.J.: Being happy with the least: achieving \(\alpha \)-happiness with minimum number of tuples. In: ICDE, pp. 1009–1020 (2020)

    Google Scholar 

  38. Zeighami, S., Wong, R.C.W.: Minimizing average regret ratio in database. In: SIGMOD, pp. 2265–2266 (2016)

    Google Scholar 

  39. Zeighami, S., Wong, R.C.: Finding average regret ratio minimizing set in database. CoRR abs/1810.08047, pp. 1–18 (2018)

    Google Scholar 

  40. Zheng, J., Chen, C.: Sorting-based interactive regret minimization. In: APWeb-WAIM, pp. 473–490 (2020)

    Google Scholar 

  41. Zheng, J., Dong, Q., Wang, X., Zhang, Y., Ma, W., Ma, Y.: Efficient processing of \({k}\)-regret minimization queries with theoretical guarantees. Inf. Sci. 586, 99–118 (2022)

    Article  Google Scholar 

  42. Zheng, J., Wang, Y., Wang, X., Ma, W.: Continuous k-regret minimization queries: a dynamic coreset approach. TKDE (2022). https://doi.org/10.1109/TKDE.2022.3166835

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Zhiyang Hao & Jiping Zheng

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Jiping Zheng

Authors
  1. Zhiyang Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiping Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiping Zheng .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. Newcastle University, Callaghan, NSW, Australia

    Lin Yue

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Chuanqi Tao

  4. Jinan University, Guangzhou, China

    Xuming Han

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Diego Calvanese

  6. University of Tsukuba, Tsukuba, Japan

    Toshiyuki Amagasa

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hao, Z., Zheng, J. (2023). Computing Online Average Happiness Maximization Sets over Data Streams. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13423. Springer, Cham. https://doi.org/10.1007/978-3-031-25201-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25201-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25200-6

  • Online ISBN: 978-3-031-25201-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics