Skip to main content
SpringerLink
Log in

Big Data Computing and Mining in a Smart World

  • Conference paper
  • First Online:
Big Data Analyses, Services, and Smart Data (BIGDAS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 899))

Included in the following conference series:

Abstract

A smart world can be considered as a convergence of the physical world, cyber world, social world, and thinking world. In these four worlds, huge amounts of valuable data are generated and gathered at a rapid rate from a broad range of data sources. Although the quality of these big data depend on their degrees of uncertainty, rich sets of valuable information and useful knowledge can be mined from the big data. This paper focuses on big data computing and mining, which aims to (a) analyze these rich sets of big data, and (b) discover implicit, previously unknown, and potentially useful information and knowledge from the big data. In particular, we present data science solutions for discover frequent patterns. Through our presentation, we discuss how these solutions interconnecting (a) big data generated and collected from the physical world, (b) frequent pattern mining algorithms in the cyber world, (c) social interactions among social individuals in the social world, and (d) user preference and interest reflecting the user cognitive thinking in the thinking world. We show these interactions through our discussion on mining coronavirus disease 2019 (COVID-19) data in a smart world environment. The interconnections link the physical, cyber, social and thinking worlds together to establish a better environment towards big data computing and mining in a smart world.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ma, J., Yang, L.T., Apduhan, B.O., Huang, R., Barolli, L., Takizawa, M.: Towards a smart world and ubiquitous intelligence: a walkthrough from smart things to smart hyperspaces and UbicKids. Int. J. Pervasive Comput. Commun. 1(1), 53–68 (2005). https://doi.org/10.1108/17427370580000113

    Article  Google Scholar 

  2. Ning, H., Liu, H., Ma, J., Yang, L.T., Wan, Y., Ye, X., Huang, R.: From Internet to smart world. IEEE Access 3, 1994–1999 (2015). https://doi.org/10.1109/ACCESS.2015.2493890

    Article  Google Scholar 

  3. Ning, H., Liu, H.: Cyber-physical-social-thinking space based science and technology framework for the Internet of Things. Sci. China Inf. Sci. 58(3), 1–19 (2015). https://doi.org/10.1007/s11432-014-5209-2

    Article  Google Scholar 

  4. Leung, C.K., Braun, P., Cuzzocrea, A.: AI-based sensor information fusion for supporting deep supervised learning. Sensors 19(6), 1345:1–1345:12 (2019). https://doi.org/10.3390/s19061345

    Article  Google Scholar 

  5. Han, Z., Leung, C.K.: FIMaaS: scalable frequent itemset mining-as-a-service on cloud for non-expert miners. In: BigDAS 2015, pp. 84–91. ACM (2015). https://doi.org/10.1145/2837060.2837072

  6. Guo, L., Yin, H., Wang, Q., Cui, B., Huang, Z., Cui, L.: Group recommendation with latent voting mechanism. In: IEEE ICDE 2020, pp. 121–132 (2020). https://doi.org/10.1109/ICDE48307.2020.00018

  7. Jiang, F., Leung, C.K., Pazdor, A.G.M.: Web page recommendation based on bitwise frequent pattern mining. In: IEEE/WIC/ACM WI 2016, pp. 632–635. IEEE (2016). https://doi.org/10.1109/WI.2016.0111

  8. Leung, C.K., Jiang, F., Souza, J.: Web page recommendation from sparse big web data. In: IEEE/WIC/ACM WI 2018, pp. 592–597. IEEE (2018). https://doi.org/10.1109/WI.2018.00-32

  9. Leung, C.K., Kajal, A., Won, Y., Choi, J.M.C.: Big data analytics for personalized recommendation systems. In: IEEE DASC-PiCom-CBDCom-CyberSciTech 2019, pp. 1060–1065 (2019). https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00190

  10. Fariha, A., Ahmed, C.F., Leung, C.K., Abdullah, S.M., Cao, L.: Mining frequent patterns from human interactions in meetings using directed acyclic graphs. In: PAKDD 2013, Part I. LNCS (LNAI), vol. 7818, pp. 38–49. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37453-1_4

  11. Leung, C.K., Tanbeer, S.K., Cameron, J.J.: Interactive discovery of influential friends from social networks. Soc. Netw. Anal. Min. 4(1), 154:1–154:13 (2014). https://doi.org/10.1007/s13278-014-0154-z

  12. Deng, D., Mai, J.J., Leung, C.K., Cuzzocrea, A.: Cognitive-based hybrid collaborative filtering with rating scaling on entropy to defend shilling influence. In: ICNCC 2019, pp. 176–185. ACM (2019). https://doi.org/10.1145/3375998.3376040

  13. Audu, A.A., Cuzzocrea, A., Leung, C.K., MacLeod, K.A., Ohin, N.I., Pulgar-Vidal, N.C.: An intelligent predictive analytics system for transportation analytics on open data towards the development of a smart city. In: CISIS 2019. AISC, vol. 993, pp. 224–236. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22354-0_21

  14. Kitagawa, H., Saad, W., Shim, K., Tang, J. (eds.): Proceedings of the IEEE BigComp 2019 (2019)

    Google Scholar 

  15. Lee, W., et al. (eds.): Proceedings of the IEEE BigComp 2020 (2020)

    Google Scholar 

  16. Leung, C.K.: Big data mining and computing in a smart world. In: IEEE UIC-ATC-ScalCom-CBDCom-IoP 2015, pp. ciii (2015). https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.341

  17. Leung, C.K., Braun, P., Hoi, C.S.H., Souza, J., Cuzzocrea, A.: Urban analytics of big transportation data for supporting smart cities. In: DaWaK 2019. LNCS, vol. 11708, pp. 24–33. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27520-4_3

  18. Lee, W., Leung, C.K. (eds.): Big Data Applications and Services 2017. AISC, vol. 770. Springer, Singapore (2017). https://doi.org/10.1007/978-981-13-0695-2

  19. Lee, W., Leung, C.K. (eds.): Special Issue on Selected Papers from Smart Data 2018 and Big Data Service 2018. Sensors, vol. 19. MDPI (2019)

    Google Scholar 

  20. Leung, C.K.: Big data mining applications and services. In: BigDAS 2015, pp. 1–8. ACM (2015). https://doi.org/10.1145/2837060.2837076

  21. Leung, C.K., Nasridinov, A. (eds.): Proceedings of the BigDAS 2015. ACM (2015)

    Google Scholar 

  22. Xu, L., Guo, T., Dou, W., Wang, W., Wei, J.: An experimental evaluation of garbage collectors on big data applications. PVLDB 12(5), 570–583 (2019). https://doi.org/10.14778/3303753.3303762

    Article  Google Scholar 

  23. Bellatreche, L., Leung, C.K., Xia, Y., Elbaz, D.: Advances in cloud and big data computing. Concurr. Comput.: Pract. Exp. 31(2), e5053:1–e5053:3 (2019). https://doi.org/10.1002/cpe.5053

    Article  Google Scholar 

  24. Hilprecht, B., Binnig, C., Röhm, U.: Learning a partitioning advisor for cloud databases. In: ACM SIGMOD 2020, pp. 143–157 (2020). https://doi.org/10.1145/3318464.3389704

  25. Jiang, F., Leung, C.K.: A data analytic algorithm for managing, querying, and processing uncertain big data in cloud environments. Algorithms 8(4), 1175–1194 (2015). https://doi.org/10.3390/a8041175

    Article  MATH  Google Scholar 

  26. Jiang, F., Leung, C.K., Middleton, R., Pazdor, A.G.M.: Big social data mining in a cloud computing environment. In: ICCBB 2018, pp. 58–65. IEEE (2018). https://doi.org/10.1109/ICCBB.2018.8756461

  27. Kobusinska, A., Leung, C.K., Hsu, C., Raghavendra, S., Chang, V.: Emerging trends, issues and challenges in Internet of Things, big data and cloud computing. Future Gener. Comput. Syst. (FGCS) 87, 416–419 (2018). https://doi.org/10.1016/j.future.2018.05.021

    Article  Google Scholar 

  28. Bleifuß, T., Bornemann, L., Johnson, T., Kalashnikov, D.V., Naumann, F., Srivastava, D.: Exploring change - a new dimension of data analytics. PVLDB 12(2), 85–98 (2018). https://doi.org/10.14778/3282495.3282496

    Article  Google Scholar 

  29. Cuzzocrea, A., Mumolo, E., Leung, C.K., Grasso, G.M.: An innovative deep-learning algorithm for supporting the approximate classification of workloads in big data environments. In: IDEAL 2019, Part II. LNCS, vol. 11872, pp. 225–237. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33617-2_24

  30. Dewan, U., Ahmed, C.F., Leung, C.K., Rizvee, R.A., Deng, D., Souza, J.: An efficient approach for mining weighted frequent patterns with dynamic weights. In: ICDM 2019, pp. 13–27 (2019)

    Google Scholar 

  31. Lakshmanan, L.V.S., Leung, C.K., Ng, R.T.: The segment support map: scalable mining of frequent itemsets. ACM SIGKDD Explor. 2(2), 21–27 (2000). https://doi.org/10.1145/380995.381005

    Article  Google Scholar 

  32. Leung, C.K.: Uncertain frequent pattern mining. In: Frequent Pattern Mining, pp. 417–453. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07821-2_14

  33. Leung, C.K., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 653–661. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68125-0_61

  34. Yoo, K., Leung, C.K., Nasridinov, A. (eds.): Special Issue on Big Data Analysis and Visualization. Applied Sciences, vol. 10. MDPI (2020)

    Google Scholar 

  35. Al-Dubai, A., et al. (eds.): Proceedings of the IEEE IUCC-DSCI-SmartCNS 2019 (2019)

    Google Scholar 

  36. Chen, J., Yang, LT. (eds.): Proceedings of the IEEE DSDIS-CPSCom-GreenCom-iThings 2015 (2015)

    Google Scholar 

  37. Dierckens, K.E., Harrison, A.B., Leung, C.K., Pind, A.V.: A data science and engineering solution for fast k-means clustering of big data. In: IEEE TrustCom-BigDataSE-ICESS 2017, pp. 925–932 (2017). https://doi.org/10.1109/TrustCom/BigDataSE/ICESS.2017.332

  38. Leung, C.K., Zhang, Y.: An HSV-based visual analytic system for data science on music and beyond. IJACDT 8(1), 68–83 (2019). https://doi.org/10.4018/IJACDT.2019010105

    Article  Google Scholar 

  39. Parameswaran, A.: Enabling data science for the majority. PVLDB 12(12), 2309–2322 (2019). https://doi.org/10.14778/3352063.3352148

    Article  Google Scholar 

  40. Sarumi, O., Leung, C.K.: Scalable data science and machine learning algorithm for gene prediction. In: BigDAS 2019, pp. 118–126 (2019)

    Google Scholar 

  41. Leung, C.K., Zhang, Y., Hoi, C.S.H., Souza, J., Wodi, B.H.: Big data analysis and services: visualization of smart data to support healthcare analytics. In: IEEE iThings-GreenCom- CPSCom-SmartData 2019, pp. 1261–1268 (2019). https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00212

  42. 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

  43. Kim, J., Guo, T., Feng, K., Cong, G., Khan, A., Choudhury, F.M.: Densely connected user community and location cluster search in location-based social networks. In: ACM SIGMOD 2020, pp. 2199-2209. https://doi.org/10.1145/3318464.3380603

  44. Leung, C.K.: Mathematical model for propagation of influence in a social network. In: Encyclopedia of Social Network Analysis and Mining, 2nd edn. pp. 1261–1269. Springer, New York (2018). https://doi.org/10.1007/978-1-4939-7131-2

  45. 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

    Article  Google Scholar 

  46. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, pp. 487–499. Morgan Kaufmann (1994)

    Google Scholar 

  47. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD 2000, pp. 1–12 (2000). https://doi.org/10.1145/335191.335372

  48. Braun, P., Cuzzocrea, A., Leung, C.K., Pazdor, A.G.M., Souza, J.: Item-centric mining of frequent patterns from big uncertain data. Procedia Comput. Sci. 126, 1875–1884 (2018). https://doi.org/10.1016/j.procs.2018.08.075

    Article  Google Scholar 

  49. 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/10.1145/335191.335376

  50. Zaki, M.J.: Scalable algorithms for association mining. IEEE TKDE 12(3), 372–390 (2000). https://doi.org/10.1109/69.846291

    Article  Google Scholar 

  51. Zaki, M.J.: Fast vertical mining using diffsets. In: ACM KDD 2003, pp. 326–335 (2003). https://doi.org/10.1145/956750.956788

  52. 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

  53. Cuzzocrea, A., Leung, C.K., Jiang, F., MacKinnon, R.K.: Complex mining from uncertain big data in distributed environments: problems, definitions and two effective and efficient algorithms. In: Big Data Management and Processing, pp. 297-332. Taylor & Francis (2017) https://doi.org/10.1201/9781315154008-15

  54. Kumar, S., Mohbey, K.K.: A review on big data based parallel and distributed approaches of pattern mining. JKSU-CIS (2019). https://doi.org/10.1016/j.jksuci.2019.09.006

  55. Leung, C.K., Jiang, F., Pazdor, A.G.M.: Bitwise parallel association rule mining for web page recommendation. In: IEEE/WIC/ACM WI 2017, pp. 662–669. ACM (2017). https://doi.org/10.1145/3106426.3106542

  56. Quoc, P.H.V., Küng, J.: FPO tree and DP3 algorithm for distributed parallel frequent itemsets mining. ESWA 140, 112874:1–112874:13 (2020). https://doi.org/10.1016/j.eswa.2019.112874

    Article  Google Scholar 

  57. Zaki, M.J.: Parallel and distributed association mining: a survey. IEEE Concurr. 7(4), 14–25 (1999). https://doi.org/10.1109/4434.806975

    Article  Google Scholar 

  58. Braun, P., Cuzzocrea, A., Jiang, F., Leung, C.K., Pazdor, A.G.M.: MapReduce-based complex big data analytics over uncertain and imprecise social networks. In: DaWaK 2017. LNCS, vol. 10440, pp. 130–145. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64283-3_10

  59. Luna, J.M., Padillo, F., Pechenizkiy, M., Ventura, S.: Apriori versions based on MapReduce for mining frequent patterns on big data. IEEE Trans. Cybern. 48(10), 2851–2865 (2018). https://doi.org/10.1109/TCYB.2017.2751081

    Article  Google Scholar 

  60. Leung, C.K., Zhang, H., Souza, J., Lee, W.: Scalable vertical mining for big data analytics of frequent itemsets. In: DEXA 2018, Part I. LNCS, vol. 11029, pp. 3–17. Springer, Cham (2018) https://doi.org/10.1007/978-3-319-98809-2_1

  61. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. CACM 51(1), 107–113 (2008). https://doi.org/10.1145/1327452.1327492

    Article  Google Scholar 

  62. Hoi, C.S.H., Leung, C.K., Tran, K., Cuzzocrea, A., Bochicchio, M., Simonetti, M.: Supporting social information discovery from big uncertain social key-value data via graph-like metaphors. In: ICCC 2018. LNCS (LNISA), vol. 10971, pp. 102-116. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94307-7_8

  63. Braun, P., Cuzzocrea, A., Leung, C.K., Pazdor, A.G.M., Souza, J., Tanbeer, S.K.: Pattern mining from big IoT data with fog computing: models, issues, and research perspectives. In: IEEE/ACM CCGrid 2019, pp. 854–891. IEEE (2019). https://doi.org/10.1109/CCGRID.2019.00075

  64. Lee, D., Cho, J., Park, D.: Efficient partitioning of on-cloud remote executable code and on-chip software for complex-connected IoT. In: IEEE BigComp 2019, pp. 627–630 (2019). https://doi.org/10.1109/BIGCOMP.2019.8679228

  65. Leung, C.K., Deng, D., Hoi, C.S.H., Lee, W.: Constrained big data mining in an edge computing environment. In: Big Data Applications and Services 2017. AISC, vol. 770, pp. 61–68. Springer, Singapore (2017). https://doi.org/10.1007/978-981-13-0695-2_8

  66. Zhou, D., Ouyang, M., Kuang, Z., Li, Z., Zhou, J.P., Cheng, X.: Incremental association rule mining based on matrix compression for edge computing. IEEE Access 7, 173044–173053 (2019). https://doi.org/10.1109/ACCESS.2019.2956823

    Article  Google Scholar 

  67. Hoi, C.S.H., Khowaja, D., Leung, C.K.: Constrained frequent pattern mining from big data via crowdsourcing. In: Big Data Applications and Services 2017. AISC, vol. 770, pp. 69–79. Springer, Singapore (2017). https://doi.org/10.1007/978-981-13-0695-2_9

  68. Leung, C.K.: Frequent itemset mining with constraints. In: Encyclopedia of Database Systems, 2nd edn. pp. 1531–1536. Springer, New York (2018) https://doi.org/10.1007/978-1-4614-8265-9_17

  69. Nijssen, S., Zimmermann, A.: Constraint-based pattern mining. In: Frequent Pattern Mining, pp. 147–163. Springer, Cham (2014) https://doi.org/10.1007/978-3-319-07821-2_7

  70. Sarumi, O.A., Leung, C.K.: Exploiting anti-monotonic constraints for mining palindromic motifs from big genomic data. In: IEEE BigData 2019, pp. 4864–4873 (2019). https://doi.org/10.1109/BigData47090.2019.9006397

  71. Fan, C., Hao, H., Leung, C.K., Sun, L.Y., Tran, J.: Social network mining for recommendation of friends based on music interests. In: IEEE/ACM ASONAM 2018, pp. 833–840. IEEE (2018). https://doi.org/10.1109/ASONAM.2018.8508262

  72. Leung, C.K., Cuzzocrea, A., Mai, J.J., Deng, D., Jiang, F.: Personalized DeepInf: enhanced social influence prediction with deep learning and transfer learning. In: IEEE BigData 2019, pp. 2871–2880 (2019). https://doi.org/10.1109/BigData47090.2019.9005969

  73. Singh, S.P., Leung, C.K., Jiang, F., Cuzzocrea, A.: A theoretical approach to discover mutual friendships from social graph networks. In: iiWAS 2019, pp. 212–221. ACM (2019). https://doi.org/10.1145/3366030.3366077

  74. Zarrinkalam, F., Fani, H., Bagheri, E.: Social user interest mining: methods and applications. In: ACM KDD 2019, pp. 3235–3236 (2019). https://doi.org/10.1145/3292500.3332279

  75. Leung, C.K., Jiang, F., Poon, T.W., Crevier, P.: Big data analytics of social network data: who cares most about you on Facebook? In: Highlighting the Importance of Big Data Management and Analysis for Various Applications, pp. 1–15. Springer, Cham (2018) https://doi.org/10.1007/978-3-319-60255-4_1

  76. Mai, M., Leung, C.K., Choi, J.M.C., Kwan, L.K.R.: Big data analytics of Twitter data and its application for physician assistants: who is talking about your profession in Twitter? In: Data Management and Analysis, pp. 17–32. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-32587-9_2

  77. Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., Zhang, J.: Urban traffic prediction from spatio-temporal data using deep meta learning. In: ACM KDD 2019, pp. 1720–1730 (2019). https://doi.org/10.1145/3292500.3330884

  78. De Guia, J., Devaraj, M., Leung, C.K.: DeepGx: deep learning using gene expression for cancer classification. In: IEEE/ACM ASONAM 2019, pp. 913–920. ACM (2019). https://doi.org/10.1145/3341161.3343516

  79. Han, P., Yang, P., Zhao, P., Shang, S., Liu, Y., Zhou, J., Gao, X., Kalnis, P.: GCN-MF: disease-gene association identification by graph convolutional networks and matrix factorization. In: ACM KDD 2019, pp. 705–713 (2019). https://doi.org/10.1145/3292500.3330912

  80. Kulkarni, A., Sood, K., Kaul, S., Vasuja, R. (eds.): Big Data Analytics in Healthcare. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31672-3

  81. Lee, P.: The unreasonable effectiveness, and difficulty, of data in healthcare. In: ACM KDD 2019, pp. 3–4 (2019). https://doi.org/10.1145/3292500.3330645

  82. Pawliszak, T., Chua, M., Leung, C.K., Tremblay-Savard, O.: Operon-based approach for the inference of rRNA and tRNA evolutionary histories in bacteria. BMC Genom. 21(Supplement 2), 252:1–252:14 (2020). https://doi.org/10.1186/s12864-020-6612-2

    Article  Google Scholar 

  83. Sarumi, O.A., Leung, C.K., Adetunmbi, A.O.: Spark-based data analytics of sequence motifs in large omics data. Procedia Comput. Sci. 126, 596–605 (2018). https://doi.org/10.1016/j.procs.2018.07.294

    Article  Google Scholar 

  84. Souza, J., Leung, C.K., Cuzzocrea, A.: An innovative big data predictive analytics framework over hybrid big data sources with an application for disease analytics. In: AINA 2020. AISC, vol. 1151, pp. 669–680. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44041-1_59

  85. Wu, P., Cheng, C., Kaddi, C.D., Venugopalan, J., Hoffman, R., Wang, M.D.: Omic and electronic health record big data analytics for precision medicine. IEEE Trans. Biomed. Eng. 64(2), 263–273 (2017). https://doi.org/10.1109/TBME.2016.2573285

    Article  Google Scholar 

Download references

Acknowledgements

This project is partially supported by (i) Natural Sciences and Engineering Research Council of Canada (NSERC) as well as (ii) University of Manitoba.

Author information

Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Carson K. Leung

Authors
  1. Carson K. Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carson K. Leung .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Inha University, Incheon, Korea (Republic of)

    Wookey Lee

  2. Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada

    Carson K. Leung

  3. Department of Computer Science, Chungbuk National University, Cheongju, Korea (Republic of)

    Aziz Nasridinov

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Leung, C.K. (2021). Big Data Computing and Mining in a Smart World. 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_2

Download citation

Publish with us

Policies and ethics

Search

Navigation