Abstract
Over the last decades, various decision support technologies have gained massive ground in practice and theory. Out of these technologies, statistical reasoning was used widely to elucidate insights from data. Later, we have seen the emergence of online analytical processing (OLAP) and association rule mining, which both come with specific rationales and objectives. Unfortunately, both OLAP and association rule mining have been introduced with their own specific formalizations and terminologies. This made and makes it always hard to reuse results from one domain in another. In particular, it is not always easy to see the potential of statistical results in OLAP and association rule mining application scenarios. This paper aims to bridge the artificial gaps between the three decision support techniques, i.e., statistical reasoning, OLAP, and association rule mining and contribute by elaborating the semantic correspondences between their foundations, i.e., probability theory, relational algebra, and the itemset apparatus. Based on the semantic correspondences, we provide that the unification of these techniques can serve as a foundation for designing next-generation multi-paradigm data mining tools.
This work has been partially conducted in the project “ICT programme” which was supported by the European Union through the European Social Fund.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993). https://doi.org/10.1145/170036.170072
Chaudhuri, S., Dayal, U.: Data warehousing and olap for decision support. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, SIGMOD 1997, pp. 507–508. Association for Computing Machinery, New York (1997). https://doi.org/10.1145/253260.253373
Codd, E.F.: Providing olap (on-line analytical processing) to user-analysts: An it mandate. Available from Arbor Software’s web site-http://www.arborsoft.com/papers/coddTOC.html (1993)
Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A.M., Khalil, I. (eds.): DEXA 2019. LNCS, vol. 11706. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27615-7
Han, J., Fu, Y., Wang, W., Chiang, J., Zaïane, O.R., Koperski, K.: DBMiner: interactive mining of multiple-level knowledge in relational databases. In: Proceedings of SIGMOD’96 - the 1996 ACM SIGMOD International Conference on Management of Data, p. 550. Association for Computing Machinery (1996). https://doi.org/10.1145/233269.280356
Heinrichs, J.H., Lim, J.S.: Integrating web-based data mining tools with business models for knowledge management. Decis. Support Syst. 35(1), 103–112 (2003). https://doi.org/10.1016/S0167-9236(02)00098-2
Imieliński, T., Khachiyan, L., Abdulghani, A.: Cubegrades: generalizing association rules. Data Min. Knowl. Disc. 6(3), 219–257 (2002)
Kamber, M., Han, J., Chiang, J.: Metarule-guided mining of multi-dimensional association rules using data cubes. In: Proceedings of VLDB’1994 - the 20th International Conference on Very Large Data Bases, KDD 1997, pp. 207–210. AAAI Press (1997)
Kaushik, M., Sharma, R., Peious, S.A., Shahin, M., Yahia, S.B., Draheim, D.: A systematic assessment of numerical association rule mining methods. SN Comput. Sci. 2(5), 1–13 (2021)
Arakkal Peious, S., Sharma, R., Kaushik, M., Shah, S.A., Yahia, S.B.: Grand reports: a tool for generalizing association rule mining to numeric target values. In: Song, M., Song, I.-Y., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DaWaK 2020. LNCS, vol. 12393, pp. 28–37. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59065-9_3
Sharma, R., Kaushik, M., Peious, S.A., Yahia, S.B., Draheim, D.: Expected vs. unexpected: selecting right measures of interestingness. In: Song, M., Song, I.-Y., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DaWaK 2020. LNCS, vol. 12393, pp. 38–47. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59065-9_4
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. SIGMOD Rec. 25(2), 1–12 (1996)
Stigler, S.M.: The History of Statistics: The Measurement of Uncertainty Before 1900. Harvard University Press (1986)
Zhu, H.: On-line analytical mining of association rules. In: Master’s thesis. Simon Fraser University, Burnaby, Brithish Columbia, Canada (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sharma, R., Kaushik, M., Peious, S.A., Shahin, M., Yadav, A.S., Draheim, D. (2022). Towards Unification of Statistical Reasoning, OLAP and Association Rule Mining: Semantics and Pragmatics. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_48
Download citation
DOI: https://doi.org/10.1007/978-3-031-00123-9_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00122-2
Online ISBN: 978-3-031-00123-9
eBook Packages: Computer ScienceComputer Science (R0)