[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Analyzing Transaction Fees with Probabilistic Logic Programming

  • Conference paper
  • First Online:
Business Information Systems Workshops (BIS 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 373))

Included in the following conference series:

  • 1955 Accesses

Abstract

Fees are used in Bitcoin to prioritize transactions. Transactions with high associated fee are usually included in a block faster than those with lower fees. Users would like to pay just the minimum amount to make the transaction confirmed in the desired time. Fees are collected as a reward when transactions are included in a block so, on the other perspective, miners usually process first the most profitable transactions, i.e. the one with higher fee rate. Bitcoin is a dynamic system influenced by several variables, such as transaction arrival time and block discovery time making the prediction of the confirmation time a hard task. In this paper we use probabilistic logic programming to model how fees influence the confirmation time and how much fees affect miner’s revenue.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 63.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 79.99
Price includes VAT (United Kingdom)
  • 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://coinmarketcap.com/.

  2. 2.

    https://en.bitcoin.it/wiki/Block_size_limit_controversy.

  3. 3.

    https://en.bitcoin.it/wiki/Miner_fees.

  4. 4.

    https://bitcoin.org/en/download.

  5. 5.

    https://github.com/bitcoin/bitcoin/blob/master/src/policy/fees.h.

  6. 6.

    http://cplint.eu/.

References

  1. Alberti, M., Bellodi, E., Cota, G., Riguzzi, F., Zese, R.: cplint on SWISH: probabilistic logical inference with a web browser. Intell. Artif. 11(1), 47–64 (2017). https://doi.org/10.3233/IA-170105

    Article  Google Scholar 

  2. Alberti, M., Cota, G., Riguzzi, F., Zese, R.: Probabilistic logical inference on the web. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 351–363. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49130-1_26

    Chapter  Google Scholar 

  3. Azzolini, D., Riguzzi, F., Lamma, E., Bellodi, E., Zese, R.: Modeling bitcoin protocols with probabilistic logic programming. In: Bellodi, E., Schrijvers, T. (eds.) Proceedings of the 5th International Workshop on Probabilistic Logic Programming, PLP 2018, Co-located with the 28th International Conference on Inductive Logic Programming (ILP 2018), Ferrara, Italy, 1 September 2018, CEUR Workshop Proceedings, vol. 2219, pp. 49–61. CEUR-WS.org (2018). http://ceur-ws.org/Vol-2219/paper6.pdf

  4. Basu, S., Easley, D., O’Hara, M., Sirer, E.G.: Towards a functional fee market for cryptocurrencies. CoRR abs/1901.06830 (2019). http://arxiv.org/abs/1901.06830

  5. Bowden, R., Keeler, H.P., Krzesinski, A.E., Taylor, P.G.: Block arrivals in the bitcoin blockchain. CoRR abs/1801.07447 (2018). http://arxiv.org/abs/1801.07447

  6. Bragaglia, S., Riguzzi, F.: Approximate inference for logic programs with annotated disjunctions. In: Frasconi, P., Lisi, F.A. (eds.) ILP 2010. LNCS (LNAI), vol. 6489, pp. 30–37. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21295-6_7

    Chapter  MATH  Google Scholar 

  7. Buterin, V.: A next-generation smart contract and decentralized application platform (2014). https://github.com/ethereum/wiki/wiki/White-Paper. Accessed 14 Feb 2019

  8. Cardano. https://whycardano.com/

  9. Carlsten, M., Kalodner, H., Weinberg, S.M., Narayanan, A.: On the instability of bitcoin without the block reward. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 154–167. ACM (2016)

    Google Scholar 

  10. De Raedt, L., Kimmig, A.: Probabilistic (logic) programming concepts. Mach. Learn. 100(1), 5–47 (2015)

    Article  MathSciNet  Google Scholar 

  11. Eosio - an introduction by ian grigg. https://eos.io/introduction

  12. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  13. Haber, S., Stornetta, W.S.: How to time-stamp a digital document. In: Menezes, A.J., Vanstone, S.A. (eds.) CRYPTO 1990. LNCS, vol. 537, pp. 437–455. Springer, Heidelberg (1991). https://doi.org/10.1007/3-540-38424-3_32

    Chapter  Google Scholar 

  14. Hyperledger. https://www.hyperledger.org/

  15. Kasahara, S., Kawahara, J.: Priority mechanism of bitcoin and its effect on transaction-confirmation process. CoRR abs/1604.00103 (2016). http://arxiv.org/abs/1604.00103

  16. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. Adaptive Computation and Machine Learning. MIT Press, Cambridge (2009)

    MATH  Google Scholar 

  17. Koops, D.T.: Predicting the confirmation time of bitcoin transactions. CoRR abs/1809.10596 (2018). http://arxiv.org/abs/1809.10596

  18. Kroll, J.A., Davey, I.C., Felten, E.W.: The economics of bitcoin mining, or bitcoin in the presence of adversaries. In: Proceedings of WEIS, vol. 2013, p. 11 (2013)

    Google Scholar 

  19. Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008)

    Google Scholar 

  20. Fadja, A.N., Riguzzi, F.: Probabilistic logic programming in action. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds.) Towards Integrative Machine Learning and Knowledge Extraction. LNCS (LNAI), vol. 10344, pp. 89–116. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69775-8_5

    Chapter  Google Scholar 

  21. Nitti, D.: Hybrid Probabilistic Logic Programming. Ph.D. thesis, KU Leuven (2106)

    Google Scholar 

  22. Pinzón, C., Rocha, C.: Double-spend attack models with time advantange for bitcoin. Electr. Notes Theor. Comput. Sci. 329, 79–103 (2016). https://doi.org/10.1016/j.entcs.2016.12.006

    Article  MathSciNet  Google Scholar 

  23. Poon, J., Dryja, T.: The bitcoin lightning network: Scalable off-chain instant payments (2016). https://lightning.network/lightning-network-paper.pdf

  24. Riguzzi, F.: MCINTYRE: a Monte Carlo system for probabilistic logic programming. Fund. Inform. 124(4), 521–541 (2013). https://doi.org/10.3233/FI-2013-847

    Article  MathSciNet  Google Scholar 

  25. Riguzzi, F.: The distribution semantics for normal programs with function symbols. Int. J. Approx. Reason. 77, 1–19 (2016). https://doi.org/10.1016/j.ijar.2016.05.005

    Article  MathSciNet  MATH  Google Scholar 

  26. Riguzzi, F.: Foundations of Probabilistic Logic Programming. River Publishers, Gistrup (2018). http://www.riverpublishers.com/book_details.php?book_id=660

  27. Riguzzi, F., Bellodi, E., Lamma, E., Zese, R., Cota, G.: Probabilistic logic programming on the web. Softw.-Pract. Exp. 46(10), 1381–1396 (2016). https://doi.org/10.1002/spe.2386

    Article  MATH  Google Scholar 

  28. Riguzzi, F., Swift, T.: Tabling and answer subsumption for reasoning on logic programs with annotated disjunctions. In: ICLP TC 2010. LIPIcs, vol. 7, pp. 162–171. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik (2010). https://doi.org/10.4230/LIPIcs.ICLP.2010.162

  29. Riguzzi, F., Swift, T.: The PITA system: tabling and answer subsumption for reasoning under uncertainty. Theor. Pract. Log. Prog. 11(4–5), 433–449 (2011). https://doi.org/10.1017/S147106841100010X

    Article  MathSciNet  MATH  Google Scholar 

  30. Rosenfeld, M.: Analysis of hashrate-based double spending. CoRR abs/1402.2009 (2014). http://arxiv.org/abs/1402.2009

  31. Sato, T.: A statistical learning method for logic programs with distribution semantics. In: Sterling, L. (ed.) ICLP 1995, pp. 715–729. MIT Press (1995)

    Google Scholar 

  32. Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media Inc., Newton (2015)

    Google Scholar 

  33. Vennekens, J., Verbaeten, S., Bruynooghe, M.: Logic programs with annotated disjunctions. In: Demoen, B., Lifschitz, V. (eds.) ICLP 2004. LNCS, vol. 3132, pp. 431–445. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27775-0_30

    Chapter  MATH  Google Scholar 

  34. Wood, G.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Pap. 151, 1–32 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Damiano Azzolini .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Azzolini, D., Riguzzi, F., Lamma, E. (2019). Analyzing Transaction Fees with Probabilistic Logic Programming. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems Workshops. BIS 2019. Lecture Notes in Business Information Processing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-030-36691-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36691-9_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36690-2

  • Online ISBN: 978-3-030-36691-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics