nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

About the Quality of Data and Services in Natural Sciences

verfasst von : Barbara Pernici, Francesca Ratti, Gabriele Scalia

Erschienen in: Next-Gen Digital Services. A Retrospective and Roadmap for Service Computing of the Future

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Managing data related to natural sciences poses new and challenging problems as it is impossible to represent reality on a one-to-one scale, and imprecision has to be taken into account, both in data memorization and in its processing. Machine learning has been a key enabler in the context of information extraction from natural sciences data. However, data-driven results are strongly affected by the volume, the sparsity and different types of imprecision in the available sources. Therefore, it becomes pivotal to associate both to data and to data-driven services information about their quality, in order to effectively interpret the results. Different levels of granularity and multiple data modalities captured from the same processes could coexist, due to technological constraints or other intrinsic limiting factors. In addition, different levels of granularity might be also the result of application requirements, and outcomes at multiple levels of precision needs to be provided. Affinities of quality issues in domains such as chemistry, biology, and geoinformatics are discussed in the paper.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Design Methodology for Service-Based Data Product Sharing and Trading

Nächstes Kapitel Just-in-Time Sentiment Analysis for Streamed Data in Greek

http://www.crowd4sdg.eu/.

Ameller, D., Illa, X.B., Collell, O., Costal, D., Franch, X., Papazoglou, M.P.: Development of service-oriented architectures using model-driven development: a mapping study. Inf. Softw. Technol. 62, 42–66 (2015). https://doi.org/10.1016/j.infsof.2015.02.006CrossRef

Andrikopoulos, V., Benbernou, S., Papazoglou, M.P.: On the evolution of services. IEEE Trans. Softw. Eng. 38(3), 609–628 (2012). https://doi.org/10.1109/TSE.2011.22CrossRef

Andrikopoulos, V., Fugini, M., Papazoglou, M.P., Parkin, M., Pernici, B., Siadat, S.H.: QoS contract formation and evolution. In: Buccafurri, F., Semeraro, G. (eds.) EC-Web 2010. LNBIP, vol. 61, pp. 119–130. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15208-5_11CrossRef

Anselma, L., Piovesan, L., Terenziani, P.: Dealing with temporal indeterminacy in relational databases: an AI methodology. AI Commun. 32(3), 207–221 (2019). https://doi.org/10.3233/AIC-190619MathSciNetCrossRef

Ardagna, D., Cappiello, C., Samá, W., Vitali, M.: Context-aware data quality assessment for big data. Future Gener. Comput. Syst. 89, 548–562 (2018). https://doi.org/10.1016/j.future.2018.07.014CrossRef

Autelitano, A., Pernici, B., Scalia, G.: Spatio-temporal mining of keywords for social media cross-social crawling of emergency events. Geoinformatica 23(3), 425–447 (2019)CrossRef

Batini, C., Scannapieco, M.: Data and Information Quality - Dimensions, Principles and Techniques. DSA. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-24106-7CrossRefMATH

Bertossi, L., Geerts, F.: Data quality and explainable AI. J. Data Inf. Qual. (JDIQ) 12(2), 1–9 (2020)CrossRef

Bouguettaya, A., et al.: A service computing manifesto: the next 10 years. Commun. ACM 60(4), 64–72 (2017). https://doi.org/10.1145/2983528CrossRef

10.

Breck, E., Polyzotis, N., Roy, S., Whang, S., Zinkevich, M.: Data validation for machine learning. In: Talwalkar, A., Smith, V., Zaharia, M. (eds.) Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, 31 March–2 April 2019 (2019). https://proceedings.mlsys.org/book/267.pdf. mlsys.org

11.

Brusoni, V., Console, L., Terenziani, P., Pernici, B.: Qualitative and quantitative temporal constraints and relational databases: theory, architecture, and applications. IEEE Trans. Knowl. Data Eng. 11(6), 948–968 (1999). https://doi.org/10.1109/69.824613CrossRef

12.

Butler, A., Hoffman, P., Smibert, P., Papalexi, E., Satija, R.: Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36(5), 411–420 (2018). https://doi.org/10.1109/69.824613CrossRef

13.

Cappiello, C., Gal, A., Jarke, M., Rehof, J.: Data ecosystems: sovereign data exchange among organizations (Dagstuhl Seminar 19391). Dagstuhl Rep. 9(9), 66–134 (2020). https://doi.org/10.4230/DagRep.9.9.66. https://drops.dagstuhl.de/opus/volltexte/2020/11845CrossRef

14.

Castano, S., De Antonellis, V., Fugini, M.G., Pernici, B.: Conceptual schema analysis: techniques and applications. ACM Trans. Database Syst. 23(3), 286–332 (1998). https://doi.org/10.1145/293910.293150CrossRef

15.

Ching, T., et al.: Opportunities and obstacles for deep learning in biology and medicine. J. Roy. Soc. Interface 15(141), 20170387 (2018)CrossRef

16.

Consortiu, H., et al.: The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574(7777), 187 (2019)CrossRef

17.

Fauw, J.D., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24(9), 1342–1350 (2018). http://lmb.informatik.uni-freiburg.de/Publications/2018/Ron18CrossRef

18.

Fox, C.R., Ülkümen, G.: Distinguishing Two Dimensions of Uncertainty, vol. 14, chap. 1. Universitetsforlaget Oslo (2011)

19.

Gala, R., et al.: A coupled autoencoder approach for multi-modal analysis of cell types. In: Advances in Neural Information Processing Systems, pp. 9267–9276 (2019)

20.

Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89. IEEE (2018)

21.

Gilyazev, R., Turdakov, D.Y.: Active learning and crowdsourcing: a survey of optimization methods for data labeling. Program. Comput. Softw. 44(6), 476–491 (2018). https://doi.org/10.1134/S0361768818060142MathSciNetCrossRef

22.

Grambow, C.A., Li, Y.P., Green, W.H.: Accurate thermochemistry with small data sets: a bond additivity correction and transfer learning approach. J. Phys. Chem. A 123(27), 5826–5835 (2019)CrossRef

23.

Gu, Z., de Schipper, N.C., Van Deun, K.: Variable selection in the regularized simultaneous component analysis method for multi-source data integration. Scientific Rep. 9(1), 1–21 (2019)

24.

Hansen, N., He, X., Griggs, R., Moshammer, K.: Knowledge generation through data research: new validation targets for the refinement of kinetic mechanisms. In: Proceedings of the Combustion Institute (2018)

25.

Havas, C., et al.: E2mC: improving emergency management service practice through social media and crowdsourcing analysis in near real time. Sensors 17(12), 2766 (2017)CrossRef

26.

Jagadish, H.: Big data and science: myths and reality. Big Data Res. 2(2), 49–52 (2015)MathSciNetCrossRef

27.

Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017 pp. 5580–5590 (2017). http://dl.acm.org/citation.cfm?id=3295222.3295309

28.

Sellis, T.K., et al. (eds.): Spatio-Temporal Databases. LNCS, vol. 2520. Springer, Heidelberg (2003). https://doi.org/10.1007/b83622CrossRefMATH

29.

Kritikos, K., et al.: A survey on service quality description. ACM Comput. Surv. 46(1), 1:1–1:58 (2013). https://doi.org/10.1145/2522968.2522969CrossRef

30.

Lähnemann, D., et al.: Eleven grand challenges in single-cell data science. Genome Biol. 21(1), 1–35 (2020). https://doi.org/10.1186/s13059-020-1926-6CrossRef

31.

Li, Y.P., Han, K., Grambow, C.A., Green, W.H.: Self-evolving machine: a continuously improving model for molecular thermochemistry. J. Phys. Chem. A 123(10), 2142–2152 (2019)CrossRef

32.

Metzger, A., Pohl, K., Papazoglou, M.P., Di Nitto, E., Marconi, A., Karastoyanova, D.: Research challenges on adaptive software and services in the future internet: towards an S-cube research roadmap. In: Metzger, A., Pohl, K., Papazoglou, M.P. (eds.) First International Workshop on European Software Services and Systems Research - Results and Challenges, S-Cube 2012, Zurich, Switzerland, 5 June 2012, pp. 1–7. IEEE (2012). https://doi.org/10.1109/S-Cube.2012.6225501

33.

Papazoglou, M.P.: Unraveling the semantics of conceptual schemas. Commun. ACM 38(9), 80–94 (1995). https://doi.org/10.1145/223248.223275CrossRef

34.

Papazoglou, M.P., Georgakopoulos, D.: Introduction. Commun. ACM 46(10), 24–28 (2003). https://doi.org/10.1145/944217.944233CrossRef

35.

Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F.: Service-oriented computing: state of the art and research challenges. IEEE Comput. 40(11), 38–45 (2007). https://doi.org/10.1109/MC.2007.400CrossRef

36.

Ratti, F., Scalia, G., Pernici, B., Magarini, M.: A data-driven approach to optimize bounds on the capacity of the molecular channel. In: GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, pp. 1–7. IEEE (2020) . https://doi.org/10.1109/GLOBECOM42002.2020.9322078

37.

Scalia, G., Grambow, C.A., Pernici, B., Li, Y.P., Green, W.H.: Evaluating scalable uncertainty estimation methods for deep learning-based molecular property prediction. J. Chem. Inf. Model. 60(6), 2697–2717 (2020). https://doi.org/10.1021/acs.jcim.9b00975CrossRef

38.

Scalia, G., Pelucchi, M., Stagni, A., Cuoci, A., Faravelli, T., Pernici, B.: Evaluating scalable uncertainty estimation methods for deep learning-based molecular property prediction. Data Sci. 2(1–2), 245–273 (2019)CrossRef

39.

Squires, S., Ewing, R., Prügel-Bennett, A., Niranjan, M.: A method of integrating spatial proteomics and protein-protein interaction network data. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017, Part V. LNCS, vol. 10638, pp. 782–790. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70139-4_79CrossRef

40.

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018, Part III. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_27CrossRef

41.

Tomašev, N., et al.: A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572(7767), 116–119 (2019). https://doi.org/10.1038/s41586-019-1390-CrossRef

42.

Wang, J., et al.: Data denoising with transfer learning in single-cell transcriptomics. Nat. Methods 16(9), 875–878 (2019)CrossRef

43.

Wang, T.T., et al.: BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes. Genome Biol. 20(1), 1–15 (2019). https://doi.org/10.1186/s13059-019-1764-6CrossRef

Titel: About the Quality of Data and Services in Natural Sciences
verfasst von: Barbara Pernici
Francesca Ratti
Gabriele Scalia
Verlag: Springer International Publishing
Buch: Next-Gen Digital Services. A Retrospective and Roadmap for Service Computing of the Future
Print ISBN: 978-3-030-73202-8

Electronic ISBN: 978-3-030-73203-5

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-73203-5_18

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"