Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-05-17T03:03:47.950Z Has data issue: false hasContentIssue false
  • English
  • Français

I-DLV-sr: A Stream Reasoning System based on I-DLV

Published online by Cambridge University Press:  23 September 2021

FRANCESCO CALIMERI Open the ORCID record for FRANCESCO CALIMERI [Opens in a new window] ,
MARCO MANNA Open the ORCID record for MARCO MANNA [Opens in a new window] ,
ELENA MASTRIA ,
MARIA CONCETTA MORELLI ,
SIMONA PERRI Open the ORCID record for SIMONA PERRI [Opens in a new window]  and
JESSICA ZANGARI Open the ORCID record for JESSICA ZANGARI [Opens in a new window]
FRANCESCO CALIMERI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: francesco.calimeri@unical.it, marco.manna@unical.it, elena.mastria@unical.it, maria.morelli@unical.it, simona.perri@unical.it, jessica.zangari@unical.it)
MARCO MANNA
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: francesco.calimeri@unical.it, marco.manna@unical.it, elena.mastria@unical.it, maria.morelli@unical.it, simona.perri@unical.it, jessica.zangari@unical.it)
ELENA MASTRIA
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: francesco.calimeri@unical.it, marco.manna@unical.it, elena.mastria@unical.it, maria.morelli@unical.it, simona.perri@unical.it, jessica.zangari@unical.it)
MARIA CONCETTA MORELLI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: francesco.calimeri@unical.it, marco.manna@unical.it, elena.mastria@unical.it, maria.morelli@unical.it, simona.perri@unical.it, jessica.zangari@unical.it)
SIMONA PERRI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: francesco.calimeri@unical.it, marco.manna@unical.it, elena.mastria@unical.it, maria.morelli@unical.it, simona.perri@unical.it, jessica.zangari@unical.it)
JESSICA ZANGARI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: francesco.calimeri@unical.it, marco.manna@unical.it, elena.mastria@unical.it, maria.morelli@unical.it, simona.perri@unical.it, jessica.zangari@unical.it)
Get access Rights & Permissions [Opens in a new window]

Abstract

We introduce a novel logic-based system for reasoning over data streams, which relies on a framework enabling a tight, fine-tuned interaction between Apache Flink and the $${{\mathcal I}^2}$$ -DLV system. The architecture allows to take advantage from both the powerful distributed stream processing capabilities of Flink and the incremental reasoning capabilities of $${{\mathcal I}^2}$$ -DLV, based on overgrounding techniques. Besides the system architecture, we illustrate the supported input language and its modeling capabilities, and discuss the results of an experimental activity aimed at assessing the viability of the approach.

Keywords

stream reasoningstream processingovergroundingknowledge representation and reasoninganswer set programming
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 5: 37th International Conference on Logic Programming Special Issue I , September 2021 , pp. 610 - 628
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

This work has been partially supported by the project “MAP4ID - Multipurpose Analytics Platform 4 Industrial Data”, N. F/190138/01-03/X44 and by the Italian MIUR Ministry and the Presidency of the Council of Ministers under the project “Declarative Reasoning over Streams” under the “PRIN” 2017 call (CUP H24I17000080001, project 2017M9C25L_001).

References

Barbieri, D. F., Braga, D., Ceri, S., Valle, E. D. and Grossniklaus, M. 2010. C-SPARQL: a continuous query language for RDF data streams. International Journal of Semantic Computing 4, 1, 3–25.CrossRefGoogle Scholar
Bazoobandi, H. R., Beck, H. and Urbani, J. 2017. Expressive stream reasoning with laser. In International Semantic Web Conference (1). LNCS, vol. 10587. Springer, 87–103.Google Scholar
Beck, H., Bierbaumer, B., Dao-Tran, M., Eiter, T., Hellwagner, H. and Schekotihin, K. 2017. Stream reasoning-based control of caching strategies in CCN routers. In IEEE International Conference on Communications, ICC 2017, Paris, France, May 21–25, 2017. IEEE, 1–6.Google Scholar
Beck, H., Dao-Tran, M. and Eiter, T. 2015. Answer update for rule-based stream reasoning. In IJCAI. AAAI Press, 2741–2747.Google Scholar
Beck, H., Dao-Tran, M. and Eiter, T. 2018. LARS: A logic-based framework for analytic reasoning over streams. Artificial Intelligence 261, 16–70.Google Scholar
Beck, H., Dao-Tran, M., Eiter, T. and Folie, C. 2018. Stream reasoning with LARS. Künstliche Intell. 32, 2–3, 193–195.CrossRefGoogle Scholar
Brewka, G., Eiter, T. and Truszczynski, M. 2011. Answer set programming at a glance. Communications of the ACM 54, 12, 92–103.CrossRefGoogle Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Maratea, M., Ricca, F. and Schaub, T. 2020. Asp-core-2 input language format. TPLP 20, 2, 294–309.Google Scholar
Calimeri, F., Fuscà, D., Perri, S. and Zangari, J. 2017. I-DLV: the new intelligent grounder of DLV. Intelligenza Artificiale 11, 1, 5–20.CrossRefGoogle Scholar
Calimeri, F., Ianni, G., Pacenza, F., Perri, S. and Zangari, J. 2019. Incremental answer set programming with overgrounding. Theory and Practice of Logic Programming 19, 5–6 (Sep), 957–973.10.1017/S1471068419000292CrossRefGoogle Scholar
Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S. and Tzoumas, K. 2015. Apache flink: Stream and batch processing in a single engine. IEEE Data Engineering Bulletin 38, 4, 28–38.Google Scholar
Dell’Aglio, D., Valle, E. D., van Harmelen, F. and Bernstein, A. 2017. Stream reasoning: A survey and outlook. Data Science 1, 1-2, 59–83.CrossRefGoogle Scholar
Do, T. M., Loke, S. W. and Liu, F. 2011. Answer set programming for stream reasoning. In Canadian Conference on AI. LNCS, vol. 6657. Springer, 104–109.Google Scholar
Eiter, T., Ogris, P. and Schekotihin, K. 2019. A distributed approach to LARS stream reasoning (system paper). Theory and Practice of Logic Programming 19, 5-6, 974–989.CrossRefGoogle Scholar
Gebser, M., Grote, T., Kaminski, R. and Schaub, T. 2011. Reactive answer set programming. In LPNMR. LNCS, vol. 6645. Springer, 54–66.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2019. Multi-shot ASP solving with clingo. TPLP 19, 1, 27–82.Google Scholar
Gebser, M., Leone, N., Maratea, M., Perri, S., Ricca, F. and Schaub, T. 2018. Evaluation techniques and systems for answer set programming: a survey. In IJCAI. ijcai.org, 5450–5456.Google Scholar
Hoeksema, J. and Kotoulas, S. 2011. High-performance distributed stream reasoning using s4. In Ordring Workshop at ISWC.Google Scholar
Hueske, F. and Kalavri, V. 2019. Stream Processing with Apache Flink: Fundamentals, Implementation, and Operation of Streaming Applications . O’Reilly Media, Incorporated.Google Scholar
Ianni, G., Pacenza, F. and Zangari, J. 2020. Incremental maintenance of overgrounded logic programs with tailored simplifications. Theory and Practice of Logic Programming 20, 5, 719–734.CrossRefGoogle Scholar
Karimov, J., Rabl, T., Katsifodimos, A., Samarev, R., Heiskanen, H. and Markl, V. 2018. Benchmarking distributed stream data processing systems. In 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16-19, 2018. IEEE Computer Society, 1507–1518.Google Scholar
Mileo, A., Abdelrahman, A., Policarpio, S. and Hauswirth, M. 2013. Streamrule: A nonmonotonic stream reasoning system for the semantic web. In RR. LNCS, vol. 7994. Springer, 247–252.Google Scholar
Pham, T., Ali, M. I. and Mileo, A. 2019. C-ASP: continuous asp-based reasoning over RDF streams. In LPNMR. LNCS, vol. 11481. Springer, 45–50.Google Scholar
Phuoc, D. L., Dao-Tran, M., Parreira, J. X. and Hauswirth, M. 2011. A native and adaptive approach for unified processing of linked streams and linked data. In International Semantic Web Conference (1). LNCS, vol. 7031. Springer, 370–388.Google Scholar
Ren, X., Curé, O., Naacke, H. and Xiao, G. 2018. Bigsr: real-time expressive RDF stream reasoning on modern big data platforms. In IEEE BigData. IEEE, 811–820.Google Scholar
Toshniwal, A., Taneja, S., Shukla, A., Ramasamy, K., Patel, J. M., Kulkarni, S., Jackson, J., Gade, K., Fu, M., Donham, J., Bhagat, N., Mittal, S. and Ryaboy, D. V. 2014. Storm@twitter. In SIGMOD Conference. ACM, 147–156.Google Scholar
Supplementary material: PDF

Calimeri et al. supplementary material

Calimeri et al. supplementary material

Download Calimeri et al. supplementary material(PDF)
PDF 142.4 KB