Abstract
Data quality assessment outcomes are essential for analytical processes reliability, especially when they are related to temporal data. Such outcomes depend on efficiency and efficacy of (semi-)automated approaches that are determined by understanding the problem associated with each data defect. Despite the small number of works that describe temporal data defects regarding to accuracy, completeness and consistency, there is a significant heterogeneity of terminology, nomenclature, description depth and number of examined defects. To cover this gap, this work reports a taxonomy that organizes temporal data defects according to a five-step methodology. The proposed taxonomy enhances the descriptions and coverage of defects with regard to the related works, and also supports certain requirements of data quality assessment, including the design of visual analytics solutions to support data quality assessment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abedjan, Z., Akcora, C.G., Ouzzani, M., Papotti, P., Stonebraker, M.: Temporal rules discovery for web data cleaning. Proc. VLDB Endowment 9(4), 336–347 (2015)
Berti-Equille, L., et al.: Assessment and analysis of information quality: a multidimensional model and case studies. Int. J. Inf. Qual. 2(4), 300–323 (2011)
Borovina Josko, J.M.: Uso de propriedades visuais-interativas na avaliação da qualidade de dados. Doctoral dissertation, Universidade de São Paulo (2016)
Borovina Josko, J.M., Ferreira, J.E.: Visualization properties for data quality visual assessment: an exploratory case study. Inf. Vis. 16(2), 93–112 (2017)
Josko, J.M.B., Oikawa, M.K., Ferreira, J.E.: A formal taxonomy to improve data defect description. In: Gao, H., Kim, J., Sakurai, Y. (eds.) DASFAA 2016. LNCS, vol. 9645, pp. 307–320. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32055-7_25
Chomicki, J., Toman, D.: Time in database systems. Handbook of Temporal Reasoning in Artificial Intelligence. 1, 429–467 (2005)
Combi, C., Montanari, A., Sala, P.: A uniform framework for temporal functional dependencies with multiple granularities. In: Pfoser, D. et al. (eds.) International Symposium on Spatial and Temporal Databases. SSTD 2011. LNCS, vol. 6849, pp. 404–421. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22922-0_24
Gschwandtner, T., Gärtner, J., Aigner, W., Miksch, S.: A taxonomy of dirty time-oriented data. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds.) CD-ARES 2012. LNCS, vol. 7465, pp. 58–72. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32498-7_5
Jensen, C.S., Snodgrass, R.T.: Temporal specialization and generalization. IEEE Trans. Knowl. Data Eng. 6(6), 954–974 (1994)
Laranjeiro, N., Soydemir, S.N., Bernardino, J.: A survey on data quality: classifying poor data. In: 21st Pacific Rim International Symposium on Dependable Computing, pp. 179–188. IEEE Press, Zhangjiajie (2015)
Meiri, I.: Combining qualitative and quantitative constraints in temporal reasoning. Artif. Intell. 87(1–2), 343–385 (1996)
Scannapieco, M., Catarci, T.: Data quality under a computer science perspective. Arch. Comput. 2, 1–15 (2002)
Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95 (1996)
Wijsen, J.: Temporal integrity constraints. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 2976–2982. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-39940-9
Yu, Y., Zhu, Y., Li, S., Wan, D.: Time series outlier detection based on sliding window prediction. Math. Probl. Eng. 1, 1–14 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Borovina Josko, J.M. (2019). A Formal Taxonomy of Temporal Data Defects. In: Hacid, H., Sheng, Q., Yoshida, T., Sarkheyli, A., Zhou, R. (eds) Data Quality and Trust in Big Data. QUAT 2018. Lecture Notes in Computer Science(), vol 11235. Springer, Cham. https://doi.org/10.1007/978-3-030-19143-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-19143-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19142-9
Online ISBN: 978-3-030-19143-6
eBook Packages: Computer ScienceComputer Science (R0)