Abstract
Nowadays, big data are everywhere. These big data can be of different degrees of veracity (e.g., precise, imprecise and uncertain data). Many of them are open data and are stored in relational databases. Embedded in these big data are valuable information and knowledge, which can be discovered by data mining. Frequent pattern mining is a popular data mining task within the realm of data science. In this paper, we present data science techniques for vertical data mining—in particular, vertical mining of frequently occurring patterns—from these relational data. For illustration, we discuss applications of the vertical data mining for the discovery of knowledge and useful information from real-life epidemiological data about coronavirus disease 2019 (COVID-19) and economic data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, Z., Wang, Y., Narasayya, V.R., Chaudhuri, S.: Customizable and scalable fuzzy join for big data. PVLDB 12(12), 2106–2117 (2019). https://doi.org/10.14778/3352063.3352128
Lee, W., Leung, C.K. (eds.): Big Data Applications and Services 2017. AISC, vol. 770. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0695-2
Leung, C.K.: Big data analysis and mining. In: Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics, pp. 15–27 (2019). https://doi.org/10.4018/978-1-5225-7598-6.ch002
Schäfer, N., Michel, S.: JODA: a vertically scalable, lightweight JSON processor for big data transformations. In: IEEE ICDE 2020, pp. 1726–1729 (2020). https://doi.org/10.1109/ICDE48307.2020.00155
Siddiqui, T., Jindal, A., Qiao, S., Patel, H., Le, W.: Cost models for big data query processing: learning, retrofitting, and our findings. In: ACM SIGMOD 2020, pp. 99–113 (2020). https://doi.org/10.1145/3318464.3380584
Leung, C.K.: Mining uncertain data. Wiley Interdisc. Rev.: Data Mining Knowl. Discovery 1(4), 316–329 (2011). https://doi.org/10.1002/widm.31
Leung, C.K.-S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 653–661. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68125-0_61
Ma, C., Cheng, R., Lakshmanan, L.V.S., Grubenmann, T., Fang, Y., Li, X.: LINC: a motif counting algorithm for uncertain graphs. PVLDB 13(2), 155–168 (2019). https://doi.org/10.14778/3364324.3364330
Leung, C.K., Zhang, Y.: An HSV-based visual analytic system for data science on music and beyond. Int. J. Art, Culture Des. Technol. (IJACDT) 8(1), 68–83 (2019). https://doi.org/10.4018/ijacdt.2019010105
Martins, R., Chen, J., Chen, Y., Feng, Y., Dillig, I.: Trinity: an extensible synthesis framework for data science. PVLDB 12(12), 1914–1917 (2019). https://doi.org/10.14778/3352063.3352098
Parameswaran, A.: Enabling data science for the majority. PVLDB 12(12), 2309–2322 (2019). https://doi.org/10.14778/3352063.3352148
Ullman, J.D.: The battle for data science. IEEE Data Eng. Bull. 43(2), 8–14 (2020)
Zhang, Y., Ives, Z.G.: Finding related tables in data lakes for interactive data science. In: ACM SIGMOD 2020, pp. 1951–1966 (2020). https://doi.org/10.1145/3318464.3389726
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, pp. 487–499. Morgan Kaufmann (1994)
Leung, C.K.: Frequent itemset mining with constraints. In: Liu, L, Özsu, M.T. (eds.) Encyclopedia of Database Systems, 2nd edn., pp. 1531–1536. Springer, New York (2018). https://doi.org/10.1007/978-1-4614-8265-9_17
Leung, C.K., Khan, Q.I.: DSTree: a tree structure for the mining of frequent sets from data streams. In: IEEE ICDM 2006, pp. 928–932 (2006). https://doi.org/10.1109/ICDM.2006.62
Bian, S., Guo, Q., Wang, S., Yu, J.X.: Efficient algorithms for budgeted influence maximization on massive social networks. PVLDB 13(9), 1498–1510 (2020). https://doi.org/10.14778/3397230.3397244
Jiang, F., Leung, C.K., Tanbeer, S.K.: Finding popular friends in social networks. In: CGC 2012, pp. 501–508. IEEE (2012). https://doi.org/10.1109/CGC.2012.99
Leung, C.K.-S., Tanbeer, S.K., Cameron, J.J.: Interactive discovery of influential friends from social networks. Soc. Netw. Anal. Mining 4(1), 154:1–154:13 (2014). https://doi.org/10.1007/s13278-014-0154-z
Tanbeer, S.K., Leung, C.K., Cameron, J.J.: Interactive mining of strong friends from social networks and its applications in e-commerce. JOCEC 24(2–3), 157–173 (2014). https://doi.org/10.1080/10919392.2014.896715
Lee, T., Matsushima, S., Yamanishi, K.: Grafting for combinatorial binary model using frequent itemset mining. Data Mining Knowl. Discovery 34(1), 101–123 (2020). https://doi.org/10.1007/s10618-019-00657-9
Leung, C.K., Zhang, H., Souza, J., Lee, W.: Scalable vertical mining for big data analytics of frequent itemsets. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R.R. (eds.) DEXA 2018. LNCS, vol. 11029, pp. 3–17. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98809-2_1
Zaki, M.J.: Scalable algorithms for association mining. IEEE TKDE 12(3), 372–390 (2000). https://doi.org/10.1109/69.846291
Zaki, M.J.: Fast vertical mining using diffsets. In: ACM KDD 2003, pp. 326–335 (2003). https://doi.org/10.1145/956750.956788
Shenoy, P., Bhalotia, J.R., Bawa, M., Shah, D.: Turbo-charging vertical mining of large databases. In: ACM SIGMOD 2000, pp. 22–33 (2000). https://doi.org/10.1145/342009.335376
Leung, C.K.: Pattern mining for knowledge discovery. In: IDEAS 2019, pp. 34:1–34:5. ACM (2019). https://doi.org/10.1145/3331076.3331099
Budhia, B.P., Cuzzocrea, A., Leung, C.K.: Vertical frequent pattern mining from uncertain data. In: KES 2012. FAIA, vol. 243, pp. 1273–1282 (2012). https://doi.org/10.3233/978-1-61499-105-2-1273
Leung, C.K., Tanbeer, S.K., Budhia, B.P., Zacharias, L.C.: Mining probabilistic datasets vertically. In: IDEAS 2012, pp. 199–204. ACM (2012). https://doi.org/10.1145/2351476.2351500
Corrales-Garay, D., Ortiz-de-Urbina-Criado, M., Mora-Valentín, E.: A research agenda on open data impact process for open innovation. IEEE Access 8, 34696–34705 (2020). https://doi.org/10.1109/ACCESS.2020.2974378
Leung, C.K., Chen, Y., Shang, S., Wen, Y., Hryhoruk, C.C.J., Levesque, D.L., Braun, N.A., Seth, N., Jain, P.: Data mining on open public transit data for transportation analytics during pre-COVID-19 era and COVID-19 era. In: Barolli, L., Li, K. F., Miwa, H. (eds.) INCoS 2020. AISC, vol. 1263. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57795-7_13
Statistics Canada: Table 13-10-0774-01 detailed preliminary information on cases of COVID-19: 6 dimensions (aggregated data). Public Health Agency of Canada (2020). https://doi.org/10.25318/1310077401-eng
Statistics Canada: Table 13-10-0775-01 detailed preliminary information on cases of COVID-19: 4 dimensions (aggregated data). Public Health Agency of Canada (2020). https://doi.org/10.25318/1310077501-eng
Statistics Canada: Table 13-10-0781-01 detailed preliminary information on confirmed cases of COVID-19 (revised). Public Health Agency of Canada (2020). https://doi.org/10.25318/1310078101-eng
Acknowledgements
This project is partially supported by (i) Birla Institute of Technology & Science (BITS) - Pilani, (ii) China Scholarship Council (CSC), (iii) Mitacs (Canada), (iv) Nanjing University, (v) Natural Sciences and Engineering Research Council of Canada (NSERC), (vi) Tongji University, as well as (vii) University of Manitoba.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, P., Hoi, C.S.H., Leung, C.K., Yuan, Y., Zhang, X., Zhang, Z. (2021). Vertical Data Mining from Relational Data and Its Application to COVID-19 Data. In: Lee, W., Leung, C.K., Nasridinov, A. (eds) Big Data Analyses, Services, and Smart Data. BIGDAS 2018. Advances in Intelligent Systems and Computing, vol 899. Springer, Singapore. https://doi.org/10.1007/978-981-15-8731-3_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-8731-3_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8730-6
Online ISBN: 978-981-15-8731-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)