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2020 | OriginalPaper | Chapter

Functional Dependency Discovery on Distributed Database: Sampling Verification Framework

Authors : Chenxin Gu, Jie Cao

Published in: Data Science

Publisher: Springer Singapore

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Abstract

In relational databases, functional dependencies discovery is a very important database analysis technology, which has a wide range of applications in knowledge discovery, database semantic analysis, data quality assessment and database design. The existing functional dependencies discovery algorithms are mainly designed for centralized data, which are usually only applicable when the data size is small. With the rapid development of the database scale of the times, the distributed environment function dependence discovery has more and more important practical significance. A functional dependencies discovery algorithm for big data in distributed environment is proposed. The basic idea is to first perform functional dependencies discovery on the sampled data set, and then globally verify the functional dependencies that may be globally established, so that all functional dependencies can be discovered. Parallel computing can be used to improve discovery efficiency while ensuring correctness.

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Literature
1.
Meng, X., Ci, X.: Big data management: concepts, techniques and challenges. J. Comput. Res. Dev. 50(1), 146–169 (2013)
2.
Liu, X., Liu, X.: An important aspect of big data: data usability. J. Comput. Res. Dev. 50(6), 1147–1162 (2013)
3.
Huhtala, Y., Karkkainen, J., Porkka, P., et al.: TANE: an efficient algorithm for discovering functional and approximate dependencies. Comput. J. 42(2), 100–111 (1999)CrossRef
4.
Novelli, N., Cicchetti, R.: FUN: an efficient algorithm for mining functional and embedded dependencies. In: Proceedings of the 8th International Conference on Database Theory, pp. 189–203. ACM, New York (2001)
5.
Yao, H., Hamilton, H.J., Butz, C.J.: FD_Mine: discovering functional dependencies in a database using equivalences. In: IEEE International Conference on Data Mining. IEEE Computer Society (2002)
6.
Abedjan, Z., Schulze, P., Naumann, F.: DFD: efficient functional dependency discovery (2014)
7.
Wyss, C., Giannella, C., Robertson, E.: FastFDs: a heuristic-driven, depth-first algorithm for mining functional dependencies from relation instances. In: Proceedings of the 3rd International Conference on Data Warehousing and Knowledge Discovery, pp. 101–110. ACM, New York (2001)
8.
Flach, P.A., Savnik, I.: Database dependency discovery: a machine learning approach. AI Commun. 12(3), 139–160 (1999)MathSciNet
9.
King, R.S., Legendre, J.J.: Discovery of functional and approximate functional dependencies in relational databases. J. Appl. Math. Decis. Sci. 7(1), 49–59 (2003)MathSciNetCrossRef
10.
Allard, P., Ferré, S., Ridoux, O.: Discovering functional dependencies and association rules by navigating in a lattice of OLAP views. In: Proceedings of the Concept Lattices and Their Applications, vol. 1, no. 1, pp. 199–210 (2010)
11.
Cabrerizo, F.J., Alonso, S., Herrera-Viedma, E.: A consensus model for group decision making problems with unbalanced fuzzy linguistic information. Int. J. Inf. Technol. Decis. Making 08(01), 109–131 (2009)CrossRef
12.
Ye, F., Liu, J., Qian, J., Xue, X.: A framework for mining functional dependencies from large distributed databases. In: Proceedings of 2010 International Conference on Artificial Intelligence and Computational Intelligence, pp. 109–113. IEEE, Alamitos (2010)
13.
Peng, Y., Kou, G., Shi, Y., et al.: A descriptive framework for the field of data mining and knowledge discovery. Int. J. Inf. Technol. Decis. Making 07(04), 639–682 (2008)CrossRef
14.
Liu, J., Li, J., Liu, C., et al.: Discover dependencies from data-a review. IEEE Trans. Knowl. Data Eng. 24(2), 251–264 (2012)CrossRef
15.
Lopes, S., Petit, J., Lakhal, L.: Efficient discovery of functional dependencies and armstrong relations. In: Proceedings of the 7th International Conference on Extending Database Technology, pp. 350–364. ACM, New York (2000)
16.
Yu, M., Zhao, X., Xu, Z.: Survey on using dependencies to improve data consistency. J. Comput. Appl. 38(S2), 72–76 + 102 (2018)
17.
Fan, W., Geerts, F., Ma, S., et al.: Detecting inconsistencies in distributed data. In: Proceedings of the 26th International Conference on Data Engineering, pp. 64–75. IEEE, Alamitos (2010)
18.
Yang, Q., Wu, X.: 10 challenging problems in data mining research. Int. J. Inf. Technol. Decis. Making 05(04), 597–604 (2006)CrossRef
Metadata
Title
Functional Dependency Discovery on Distributed Database: Sampling Verification Framework
Authors
Chenxin Gu
Jie Cao
Copyright Year
2020
Publisher
Springer Singapore
Book
Data Science

Print ISBN: 978-981-15-2809-5

Electronic ISBN: 978-981-15-2810-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-2810-1_43

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