Skip to main content
Springer Nature Link
Log in

Summarizing Edge-Device Data via Core Items

  • Conference paper
  • First Online:
Advances in Databases and Information Systems (ADBIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13389))

Included in the following conference series:

  • 820 Accesses

Abstract

In this work, we consider the problem of summarizing a data stream through an item-based summary using core items. We consider an IoT setting, where computing such summaries at the edge devices instead of emitting the whole data stream can drastically reduce the network traffic and speed up further processing. Core items of a data stream are the items with the highest values for a given monotone submodular utility function. To create stream summaries, we propose the SoftSieving approach for parallel processing with low memory consumption and fast execution time while attaining acceptable utility gain. Through extensive experiments with real-world datasets, we show the suitability of our approach and its superiority over state-of-the-art competitors.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Apache storm (2011). https://storm.apache.org/. Accessed 09 Apr 2022

  2. Aggarwal, C.C.: On biased reservoir sampling in the presence of stream evolution. In: VLDB, pp. 607–618 (2006)

    Google Scholar 

  3. Badanidiyuru, A., Mirzasoleiman, B., Karbasi, A., Krause, A.: Streaming submodular maximization: massive data summarization on the fly. In: KDD (2014)

    Google Scholar 

  4. Brown, G., Pocock, A.C., Zhao, M., Luján, M.: Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J. Mach. Learn. Res. 13, 27–66 (2012)

    MathSciNet  MATH  Google Scholar 

  5. Buchbinder, N., Feldman, M., Schwartz, R.: Online submodular maximization with preemption. In: SIAM, pp. 1202–1216 (2014)

    Google Scholar 

  6. Buschjäger, S., Honysz, P.-J., Pfahler, L., Morik, K.: Very fast streaming submodular function maximization. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds.) ECML PKDD 2021. LNCS (LNAI), vol. 12977, pp. 151–166. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86523-8_10

    Chapter  Google Scholar 

  7. Buschjäger, S., Morik, K., Schmidt, M.: Summary extraction on data streams in embedded systems. In: IOTSTREAMING@ PKDD/ECML (2017)

    Google Scholar 

  8. Chen, J., Zhang, Q.: Distinct sampling on streaming data with near-duplicates. In: PODS, pp. 369–382 (2018)

    Google Scholar 

  9. Dasgupta, A., Kumar, R., Ravi, S.: Summarization through submodularity and dispersion. In: ACL, vol. 1, pp. 1014–1022 (2013)

    Google Scholar 

  10. Feige, U.: A threshold of ln n for approximating set cover. J. ACM (JACM) 45(4), 634–652 (1998)

    Article  Google Scholar 

  11. Feldman, M., Norouzi-Fard, A., Svensson, O., Zenklusen, R.: The one-way communication complexity of submodular maximization with applications to streaming and robustness. In: SIGACT, pp. 1363–1374 (2020)

    Google Scholar 

  12. Fu, X., Ghaffar, T., Davis, J.C., Lee, D.: \(\{\)EdgeWise\(\}\): a better stream processing engine for the edge. In: USENIX ATC, pp. 929–946 (2019)

    Google Scholar 

  13. Indyk, P., Mahabadi, S., Mahdian, M., Mirrokni, V.S.: Composable core-sets for diversity and coverage maximization. In: PODS, pp. 100–108 (2014)

    Google Scholar 

  14. Kaufman, L., Rousseeuw, P.J.: Partitioning around medoids (program pam). In: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics, vol. 344, pp. 68–125 (1990)

    Google Scholar 

  15. Kazemi, E., Mitrovic, M., Zadimoghaddam, M., Lattanzi, S., Karbasi, A.: Submodular streaming in all its glory: tight approximation, minimum memory and low adaptive complexity. In: ICML, pp. 3311–3320 (2019)

    Google Scholar 

  16. Lawrence, N., Seeger, M., Herbrich, R.: Fast sparse gaussian process methods: the informative vector machine. In: NIPS, vol. 15 (2002)

    Google Scholar 

  17. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: SIGKDD, pp. 420–429 (2007)

    Google Scholar 

  18. Lin, H., Bilmes, J.: Multi-document summarization via budgeted maximization of submodular functions. In: HLT-NAACL, pp. 912–920 (2010)

    Google Scholar 

  19. Lin, H., Bilmes, J.: A class of submodular functions for document summarization. In: HLT, pp. 510–520 (2011)

    Google Scholar 

  20. Luber, M., Spinello, L., Arras, K.: People tracking in RGB-D data with on-line boosted target models. In: IROS, pp. 3844–3849 (2011)

    Google Scholar 

  21. Mirrokni, V.S., Zadimoghaddam, M.: Randomized composable core-sets for distributed submodular maximization. In: STOC, pp. 153–162 (2015)

    Google Scholar 

  22. Mirzasoleiman, B., Karbasi, A., Sarkar, R., Krause, A.: Distributed submodular maximization: Identifying representative elements in massive data. In: NIPS, vol. 26 (2013)

    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  Google Scholar 

  24. Seeger, M.: Greedy forward selection in the informative vector machine. Technical report, UC Berkeley (2004)

    Google Scholar 

  25. Spinello, L., Arras, K.O.: People detection in RGB-D data. In: RSJ, pp. 3838–3843 (2011)

    Google Scholar 

  26. Stafford, G.A.: Environmental sensor telemetry data (2020). https://www.kaggle.com/datasets/garystafford/environmental-sensor-data-132k. Accessed 28 Apr 2022

  27. Vitter, J.S.: Random sampling with a reservoir. ACM TOMS 11(1), 37–57 (1985)

    Article  MathSciNet  Google Scholar 

  28. Williams, C.K., Rasmussen, C.E.: Gaussian Processes for Machine Learning, vol. 2. MIT press, Cambridge (2006)

    MATH  Google Scholar 

  29. Zhao, J., Wang, P., Tao, J., Zhang, S., Lui, J.C.: Continuously tracking core items in data streams with probabilistic decays. In: ICDE, pp. 769–780 (2020)

    Google Scholar 

  30. Zhuang, H., Rahman, R., Hu, X., Guo, T., Hui, P., Aberer, K.: Data summarization with social contexts. In: CIKM, pp. 397–406. ACM (2016)

    Google Scholar 

Download references

Acknowledgments

This work has been partially funded by the German Federal Ministry of Education and Research under grant number 28DE113C18 (DigiVine). The responsibility for the content of this publication lies with the authors.

Author information

Authors and Affiliations

  1. TU Kaiserslautern (TUK), Kaiserslautern, Germany

    Damjan Gjurovski, Jan Heidemann & Sebastian Michel

Authors
  1. Damjan Gjurovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan Heidemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sebastian Michel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Damjan Gjurovski .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gjurovski, D., Heidemann, J., Michel, S. (2022). Summarizing Edge-Device Data via Core Items. In: Chiusano, S., Cerquitelli, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2022. Lecture Notes in Computer Science, vol 13389. Springer, Cham. https://doi.org/10.1007/978-3-031-15740-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15740-0_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15739-4

  • Online ISBN: 978-3-031-15740-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics