How Reliable Is Your Outsourcing Service for Data Mining? A Metamorphic Method for Verifying the Result Integrity

  • Jiewei Zhang
  • Xiaoyuan XieEmail author
  • Zhiyi Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11293)


Association rules mining is an important and classic research topic in Data Mining, and has been widely applied in many real-life cases. The primary time and memory consumption in association rules mining is from its first step - frequent itemsets mining. With the development of cloud computing, outsourcing this task to third-party service providers will save efforts in system development, deployment, operation, etc. Outsourcing, however, actually brings risks and difficulties in verifying the results returned by these services. In this paper, we focus on verifying the integrity of the results returned by outsourcing services. We propose a metamorphic-based method, which is light-weight and requires not much complicated process. The key point of our method is the construction of a set of metamorphic relations (MRs). Through analysis and experimental research, we show that our approach delivers quite satisfactory results.


Frequent itemsets mining Outsourcing Result integrity verification Metamorphic-based method 


  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, vol. 22, pp. 207–216. ACM (1993)Google Scholar
  2. 2.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I., et al.: Fast discovery of association rules. Adv. Knowl. Discov. Data Min. 12(1), 307–328 (1996)Google Scholar
  3. 3.
    Alwidian, J., Hammo, B.H., Obeid, N.: WCBA: weighted classification based on association rules algorithm for breast cancer disease. Appl. Soft Comput. 62, 536–549 (2018)CrossRefGoogle Scholar
  4. 4.
    Aravindhan, R., Shanmugalakshmi, R., Ramya, K.: Circumvention of nascent and potential Wi-Fi phishing threat using association rule mining. Wirel. Pers. Commun. 94(4), 2331–2361 (2017)CrossRefGoogle Scholar
  5. 5.
    Barr, E.T., Harman, M., McMinn, P., Shahbaz, M., Yoo, S.: The oracle problem in software testing: a survey. IEEE Trans. Softw. Eng. 41(5), 507–525 (2015)CrossRefGoogle Scholar
  6. 6.
    Berry, M.J., Linoff, G.: Data Mining Techniques: For Marketing, Sales, and Customer Support. Wiley, Hoboken (1997)Google Scholar
  7. 7.
    Borgelt, C.: Efficient implementations of Apriori and Eclat. In: 2003 Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (2003)Google Scholar
  8. 8.
    Chan, W.K., Cheung, S.C., Leung, K.R.: A metamorphic testing approach for online testing of service-oriented software applications. Int. J. Web Serv. Res. 4(2), 61–81 (2007)CrossRefGoogle Scholar
  9. 9.
    Chen, T.Y., Cheung, S.C., Yiu, S.M.: Metamorphic testing: a new approach for generating next test cases. Technical report, Technical Report HKUST-CS98-01, Department of Computer Science, Hong Kong University of Science and Technology, Hong Kong (1998)Google Scholar
  10. 10.
    Chen, T.Y., Ho, J.W., Liu, H., Xie, X.: An innovative approach for testing bioinformatics programs using metamorphic testing. BMC Bioinformatics 10(1), 24 (2009)CrossRefGoogle Scholar
  11. 11.
    Dong, B., Liu, R., Wang, H.W.: Trust-but-verify: verifying result correctness of outsourced frequent itemset mining in data-mining-as-a-service paradigm. IEEE Trans. Serv. Comput. 9(1), 18–32 (2016)CrossRefGoogle Scholar
  12. 12.
    Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu., C., Tseng, V.S.: SPMF: a Java open-source pattern mining library (2016).
  13. 13.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, vol. 29, pp. 1–12. ACM (2000)Google Scholar
  14. 14.
    Kotsiantis, S., Kanellopoulos, D.: Association rules mining: a recent overview. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 71–82 (2006)Google Scholar
  15. 15.
    Kuo, F.C., Chen, T.Y., Tam, W.K.: Testing embedded software by metamorphic testing: a wireless metering system case study. In: 2011 Proceedings of IEEE 36th Conference on Local Computer Networks, pp. 291–294. IEEE (2011)Google Scholar
  16. 16.
    Pang, H., Jain, A., Ramamritham, K., Tan, K.L.: Verifying completeness of relational query results in data publishing. In: 2005 Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 407–418. ACM (2005)Google Scholar
  17. 17.
    Rolfsnes, T., Moonen, L., Di Alesio, S., Behjati, R., Binkley, D.: Aggregating association rules to improve change recommendation. Empir. Softw. Eng. 23(2), 987–1035 (2018)CrossRefGoogle Scholar
  18. 18.
    Weyuker, E.J.: On testing non-testable programs. Comput. J. 25(4), 465–470 (1982)CrossRefGoogle Scholar
  19. 19.
    Wong, W.K., Cheung, D.W., Hung, E., Kao, B., Mamoulis, N.: An audit environment for outsourcing of frequent itemset mining. PVLDB 2(1), 1162–1173 (2009)Google Scholar
  20. 20.
    Xie, M., Wang, H., Yin, J., Meng, X.: Integrity auditing of outsourced data. In: 2007 Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 782–793. VLDB Endowment (2007)Google Scholar
  21. 21.
    Xie, X., Ho, J., Murphy, C., Kaiser, G., Xu, B., Chen, T.Y.: Application of metamorphic testing to supervised classifiers. In: 2009 Proceedings of the Ninth International Conference on Quality Software, pp. 135–144. IEEE (2009)Google Scholar
  22. 22.
    Xie, X., Ho, J.W., Murphy, C., Kaiser, G., Xu, B., Chen, T.Y.: Testing and validating machine learning classifiers by metamorphic testing. J. Syst. Softw. 84(4), 544–558 (2011)CrossRefGoogle Scholar
  23. 23.
    Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W., et al.: New algorithms for fast discovery of association rules. In: 1997 Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, vol. 97, pp. 283–286 (1997)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina

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