Abstract
With the growth of the number of datasets stored in data repositories, there has been a trend of using Data Lakes (DLs) to store such data. DLs store datasets in their raw formats without any transformations or preprocessing, with accessibility available using schema-on-read. This makes it difficult for analysts to find datasets that can be crossed and that belong to the same topic. To support them in this DL governance challenge, we propose in this paper an algorithm for categorizing datasets in the DL into pre-defined topic-wise categories of interest. We utilise a k-NN approach for this task which uses a proximity score for computing similarities of datasets based on metadata. We test our algorithm on a real-life DL with a known ground-truth categorization. Our approach is successful in detecting the correct categories for datasets and outliers with a precision of more than 90% and recall rates exceeding 75% in specific settings.
Keywords
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
Algergawy, A., Massmann, S., Rahm, E.: A clustering-based approach for large-scale ontology matching. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds.) ADBIS 2011. LNCS, vol. 6909, pp. 415–428. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23737-9_30
Algergawy, A., Schallehn, E., Saake, G.: A schema matching-based approach to XML schema clustering. In: Proceedings of the International Conference on Information Integration and Web-based Applications & Services, pp. 131–136. ACM (2008)
Alserafi, A., Abelló, A., Romero, O., Calders, T.: Towards information profiling: data lake content metadata management. In: DINA Workshop, ICDM (2016)
Alserafi, A., Calders, T., Abelló, A., Romero, O.: DS-prox: Dataset proximity mining for governing the data lake. In: Beecks, C., Borutta, F., Kröger, P., Seidl, T. (eds.) SISAP 2017. LNCS, vol. 10609, pp. 284–299. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68474-1_20
Baralis, E., Cerquitelli, T., Chiusano, S., Grimaudo, L., Xiao, X.: Analysis of Twitter data using a multiple-level clustering strategy. In: Cuzzocrea, A., Maabout, S. (eds.) MEDI 2013. LNCS, vol. 8216, pp. 13–24. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41366-7_2
Friedman, J., Hastie, T., Tibshirani, R., et al.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28(2), 337–407 (2000)
Gallinucci, E., Golfarelli, M., Rizzi, S.: Schema profiling of document-oriented databases. Inf. Syst. 75, 13–25 (2018)
Han, E.-H.S., Karypis, G., Kumar, V.: Text categorization using weight adjusted k-nearest neighbor classification. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 53–65. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45357-1_9
Hentech, H., Gouider, M.S., Farhat, A.: Clustering heterogeneous data streams with uncertainty over sliding window. In: Cuzzocrea, A., Maabout, S. (eds.) MEDI 2013. LNCS, vol. 8216, pp. 162–175. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41366-7_14
Lee, M.L., Yang, L.H., Hsu, W., Yang, X.: XClust: clustering XML schemas for effective integration. In: Proceedings of the International Conference on Information and Knowledge Management, pp. 292–299. ACM (2002)
Mahmoud, H.A., Aboulnaga, A.: Schema clustering and retrieval for multi-domain pay-as-you-go data integration systems. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 411–422. ACM (2010)
Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval, no. c (2009)
Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10(4), 334–350 (2001)
Shvaiko, P.: A survey of schema-based matching approaches. J. Data Semant. 3730, 146–171 (2005)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education, New York (2006)
Terrizzano, I., Schwarz, P., Roth, M., Colino, J.E.: Data wrangling: the challenging journey from the wild to the lake. In: 7th Biennial Conference on Innovative Data Systems Research CIDR 2015 (2015)
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
Alserafi, A., Abelló, A., Romero, O., Calders, T. (2019). Keeping the Data Lake in Form: DS-kNN Datasets Categorization Using Proximity Mining. In: Schewe, KD., Singh, N. (eds) Model and Data Engineering. MEDI 2019. Lecture Notes in Computer Science(), vol 11815. Springer, Cham. https://doi.org/10.1007/978-3-030-32065-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-32065-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32064-5
Online ISBN: 978-3-030-32065-2
eBook Packages: Computer ScienceComputer Science (R0)