Abstract
Today, more and more scientific groups are developing citizen science applications. Citizen science is a relatively new domain of science that has already proved to be as beneficial as classical science. One of the major challenges citizen science face is the data quality assurance. It uses several techniques to verify the data quality based on expert evaluation, voting systems, etc. Data provenance is used in many scientific systems and provides reliable mechanism for tracking data history. It includes history of origin, changes, and all interactions between different parts of data. Data provenance by itself has many types such as “Why provenance”, “When provenance”, and “What provenance”. The purpose of this work is to build a prototype of a database with built-in data provenance. Several databases systems and models such as Relational databases, NoSQL databases are taken into consideration. Experiments are been conducted to test limitations of proposed prototype.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ellul, C., Francis, L., Haklay, M.: A flexible database-centric platform for citizen science data capture. In: 2011 IEEE Seventh International Conference on E-Science Workshops (eScienceW) (2011)
McKinley, D.C., et al.: Citizen science can improve conservation science, natural resource management, and environmental protection. Biol. Conserv. 208, 15–28 (2017)
Wiggins, A., He, Y.: Community-based data validation practices in citizen science. In: CSCW (2016)
Sheppard, S.A., Wiggins, A., Terveen, L.: Capturing quality: retaining provenance for curated volunteer monitoring data. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (2014)
Memarsadeghi, N.: Citizen science [Guest editors’ introduction]. Comput. Sci. Eng. 17(4), 8–10 (2015)
Bonney, R., et al.: Next steps for citizen science. Science 343(6178), 1436–1437 (2014)
Understanding Citizen Science and Environmental Monitoring. Final Report. https://www.ceh.ac.uk/sites/default/files/citizensciencereview.pdf
Jambeck, J.R., Johnsen, K.: Citizen-based litter and marine debris data collection and mapping. Comput. Sci. Eng. 17(4), 20–26 (2015)
Dickinson, J.L., Zuckerberg, B., Bonter, D.N.: Citizen Science as an ecological research tool: challenges and benefits. Ann. Rev. Ecol. Evol. Syst. 41, 149–172 (2010)
Danielsen, F., et al.: A multicountry assessment of tropical resource monitoring by local communities. Bioscience 64(3), 236–251 (2014)
Smith, A., Lynn, S., Lintott, C.J.: Human Computation and Crowdsourcing: Works in Progress and Demonstrations (2013)
Buneman, P., Khanna, S., Wang-Chiew, T.: Why and where: a characterization of data provenance. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 316–330. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44503-X_20
Wang, Y.R., Madnick, S.E.: A polygen model for heterogeneous database systems: the source tagging perspective (1990)
Woodruff, A., Stonebraker, M.: Supporting fine-grained data lineage in a database visualization environment. In: 1997 Proceedings of the 13th International Conference on Data Engineering (1997)
Ioannou, E., Garofalakis, M.: Query analytics over probabilistic databases with unmerged duplicates. IEEE Trans. Knowled. Data Eng. 27(8), 2245–2260 (2015)
Ioannou, E., Garofalakis, M.: Query analytics over probabilistic databases with unmerged duplicates. IEEE Trans. Knowl. Data Eng. 27(8), 2245–2260 (2015)
Stonebraker, M.: SQL databases v. NoSQL databases. Commun. ACM 53(4), 10–11 (2010)
Kulkarni, D.: A fine-grained access control model for key-value systems. In: Proceedings of the Third ACM Conference on Data and Application Security and Privacy (2013)
Kitchenham, B., et al.: Systematic literature reviews in software engineering – a tertiary study. Inf. Softw. Technol. 52, 792–805 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Tiufiakov, N., Dahanayake, A., Zudilova, T. (2018). Data Provenance in Citizen Science Databases. In: Benczúr, A., et al. New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-00063-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00062-2
Online ISBN: 978-3-030-00063-9
eBook Packages: Computer ScienceComputer Science (R0)