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Storing Combustion Data Experiments: New Requirements Emerging from a First Prototype

Position Paper

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Semantics, Analytics, Visualization (SAVE-SD 2017, SAVE-SD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10959))

Abstract

Repositories for scientific and scholarly data are valuable resources to share, search, and reuse data by the community. Such repositories are essential in data-driven research based on experimental data. In this paper we focus on the case of combustion kinetic modeling, where the goal is to design models typically validated by means of comparisons with a large number of experiments.

In this paper, we discuss new requirements emerging from the analysis of an existing data collection prototype and its associated services. New requirements, elaborated in the paper, include the acquisition of new experiments, the automatic discovery of new sources, semantic exploration of information and multi-source integration, the selection of data for model validation.

These new requirements set the need for a new representation of scientific data and associated metadata. This paper describes the scenario, the requirements and outlines an initial architecture to support them.

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Notes

  1. 1.

    http://www.smartcats.eu/wg4/task-force/.

  2. 2.

    https://ec.europa.eu/research/openscience/index.cfm?pg=open-science-cloud.

  3. 3.

    http://creckmodeling.chem.polimi.it/.

  4. 4.

    https://www.jetscreen-h2020.eu/.

  5. 5.

    https://www.residue2heat.eu/.

  6. 6.

    https://www.spire2030.eu/improof.

  7. 7.

    http://www.clean-gas.polimi.it/.

References

  1. ChemKED repository. http://www.chemked.com/

  2. CloudFlame repository. https://cloudflame.kaust.edu.sa/

  3. PrIMe repository. http://primekinetics.org/

  4. ReSpecTh repository. http://respecth.hu/

  5. Bergthorson, J.M., Thomson, M.J.: A review of the combustion and emissions properties of advanced transportation biofuels and their impact on existing and future engines. Renew. Sustain. Energy Rev. 42, 1393–1417 (2015)

    Article  Google Scholar 

  6. Bernardi, M., et al.: Curve matching, a generalized framework for models/experiments comparison: an application to n-heptane combustion kinetic mechanisms. Combust. Flame 168, 186–203 (2016)

    Article  Google Scholar 

  7. Calvanese, D., et al.: Ontop: answering SPARQL queries over relational databases. Semant. Web 8(3), 471–487 (2017)

    Article  Google Scholar 

  8. Cavallotti, C., Pelucchi, M., Klippenstein, S.: EStokTP: electronic structure to temperature and pressure dependent rate constants (2017, unpublished)

    Google Scholar 

  9. Cohan, A., Goharian, N.: Scientific article summarization using citation-context and article’s discourse structure. arXiv preprint arXiv:1704.06619 (2017)

  10. Cuoci, A., Frassoldati, A., Faravelli, T., Ranzi, E.: OpenSMOKE++: an object-oriented framework for the numerical modeling of reactive systems with detailed kinetic mechanisms. Comput. Phys. Commun. 192, 237–264 (2015)

    Article  MathSciNet  Google Scholar 

  11. Daudaravicius, V.: A framework for keyphrase extraction from scientific journals. In: González-Beltrán, A., Osborne, F., Peroni, S. (eds.) SAVE-SD 2016. LNCS, vol. 9792, pp. 51–66. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-53637-8_7

    Chapter  Google Scholar 

  12. de Waard, A.: Research data management at Elsevier: supporting networks of data and workflows. Inf. Serv. Use 36(1–2), 49–55 (2016)

    Article  Google Scholar 

  13. Di Blas, N., Mazuran, M., Paolini, P., Quintarelli, E., Tanca, L.: Exploratory computing: a comprehensive approach to data sensemaking. Int. J. Data Sci. Anal. 3(1), 61–77 (2017)

    Article  Google Scholar 

  14. Francalanci, C., Pernici, B., Scalia, G.: Exploratory spatio-temporal queries in evolving information. In: Doulkeridis, C., Vouros, G.A., Qu, Q., Wang, S. (eds.) MATES 2017. LNCS, vol. 10731, pp. 138–156. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73521-4_9

    Chapter  Google Scholar 

  15. Frenklach, M.: Transforming data into knowledge-process informatics for combustion chemistry. Proc. Combust. Inst. 31(1), 125–140 (2007)

    Article  Google Scholar 

  16. Gábor, K., Zargayouna, H., Tellier, I., Buscaldi, D., Charnois, T.: A typology of semantic relations dedicated to scientific literature analysis. In: González-Beltrán, A., Osborne, F., Peroni, S. (eds.) SAVE-SD 2016. LNCS, vol. 9792, pp. 26–32. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-53637-8_3

    Chapter  Google Scholar 

  17. Jung, D., et al.: ChartSense: interactive data extraction from chart images. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 6706–6717. ACM (2017)

    Google Scholar 

  18. Keçeli, M., et al.: Automated computational thermochemistry for butane oxidation: a prelude to predictive automated combustion kinetics. Proc. Combust. Inst. (2018). Elsevier

    Google Scholar 

  19. Libkin, L., Martens, W., Vrgoč, D.: Querying graphs with data. J. ACM (JACM) 63(2), 14 (2016)

    Article  MathSciNet  Google Scholar 

  20. Pelucchi, M.: Development of kinetic mechanisms for the combustion of renewable fuels. Ph.D. thesis, Politecnico di Milano (2017)

    Google Scholar 

  21. Pelucchi, M., Cavallotti, C., Faravelli, T., Klippenstein, S.: H-abstraction reactions by OH, \(\text{ HO }_{2}\), O, \(\text{ O }_2\) and benzyl radical addition to \(\text{ O }_2\) and their implications for kinetic modelling of toluene oxidation. Phys. Chem. Chem. Phys. 20, 10607–10627 (2018)

    Article  Google Scholar 

  22. Poco, J., Heer, J.: Reverse-engineering visualizations: recovering visual encodings from chart images. Comput. Graph. Forum. 36, 353–363 (2017)

    Article  Google Scholar 

  23. Rigamonti, A.: Automatic modeling system: a database based infrastructure to develop, validate and evaluate scientific models. An application to combustion kinetic models (2017)

    Google Scholar 

  24. Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. Web Semant.: Sci. Serv. Agents World Wide Web 36, 1–22 (2016)

    Article  Google Scholar 

  25. Ronzano, F., Saggion, H.: Knowledge extraction and modeling from scientific publications. In: González-Beltrán, A., Osborne, F., Peroni, S. (eds.) SAVE-SD 2016. LNCS, vol. 9792, pp. 11–25. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-53637-8_2

    Chapter  Google Scholar 

  26. Schätzle, A., Przyjaciel-Zablocki, M., Skilevic, S., Lausen, G.: S2RDF: RDF querying with SPARQL on spark. Proc. VLDB Endow. 9(10), 804–815 (2016)

    Article  Google Scholar 

  27. Soliman, M.A., Ilyas, I.F., Chang, K.C.-C.: Top-k query processing in uncertain databases. In: DIEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 896–905. IEEE (2007)

    Google Scholar 

  28. Stagni, A., Frassoldati, A., Cuoci, A., Faravelli, T., Ranzi, E.: Skeletal mechanism reduction through species-targeted sensitivity analysis. Combust. Flame 163, 382–393 (2016)

    Article  Google Scholar 

  29. Varga, T., Turányi, T., Czinki, E., Furtenbacher, T., Császár, A.: ReSpecth: a joint reaction kinetics, spectroscopy, and thermochemistry information system. In: Proceedings of the 7th European Combustion Meeting, vol. 30, pp. 1–5 (2015)

    Google Scholar 

  30. Wasay, A., Athanassoulis, M., Idreos, S.: Queriosity: automated data exploration. In: Carminati, B., Khan, L. (eds.) 2015 IEEE International Congress on Big Data, New York City, NY, USA, 27 June–2 July 2015, pp. 716–719. IEEE (2015)

    Google Scholar 

  31. Weber, B.W., Niemeyer, K.E.: ChemKED: a human-and machine-readable data standard for chemical kinetics experiments. Int. J. Chem. Kinet. 50, 135–148 (2017)

    Article  Google Scholar 

  32. Wilkinson, M.D., et al.: The FAIR guiding principles for scientific data management and stewardship. Scientific Data 3 (2016)

    Google Scholar 

  33. Yu, R., Gadiraju, U., Fetahu, B., Dietze, S.: Adaptive focused crawling of linked data. In: Wang, J., et al. (eds.) WISE 2015 Part I. LNCS, vol. 9418, pp. 554–569. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26190-4_37

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

    Gabriele Scalia & Barbara Pernici

  2. Department of Chemistry Materials, and Chemical Engineering Giulio Natta, Politecnico di Milano, Milan, Italy

    Matteo Pelucchi, Alessandro Stagni & Tiziano Faravelli

Authors
  1. Gabriele Scalia
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  2. Matteo Pelucchi
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  3. Alessandro Stagni
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  4. Tiziano Faravelli
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  5. Barbara Pernici
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Corresponding author

Correspondence to Gabriele Scalia .

Editor information

Editors and Affiliations

  1. Oxford e-Research Centre, University of Oxford, Oxford, UK

    Alejandra González-Beltrán

  2. Waltan Hall, KMi, Open University, Milton Keynes, UK

    Francesco Osborne

  3. Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy

    Silvio Peroni

  4. University of Bonn, Bonn, Germany

    Sahar Vahdati

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Scalia, G., Pelucchi, M., Stagni, A., Faravelli, T., Pernici, B. (2018). Storing Combustion Data Experiments: New Requirements Emerging from a First Prototype. In: González-Beltrán, A., Osborne, F., Peroni, S., Vahdati, S. (eds) Semantics, Analytics, Visualization . SAVE-SD SAVE-SD 2017 2018. Lecture Notes in Computer Science(), vol 10959. Springer, Cham. https://doi.org/10.1007/978-3-030-01379-0_10

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  • DOI: https://doi.org/10.1007/978-3-030-01379-0_10

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  • Online ISBN: 978-3-030-01379-0

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