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An Innovative Framework for Supporting Frequent Pattern Mining Problems in IoT Environments

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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Abstract

In the current era of big data, high volumes of a wide variety of data of different veracity can be easily generated or collected at a high velocity from rich sources of data include devices from the Internet of Things (IoT). Embedded in these big data are useful information and valuable knowledge. Hence, frequent pattern mining and its related research problem of association rule mining, which aim to discover implicit, previously unknown and potentially useful information and knowledge—in the form of sets of frequently co-occurring items or rules revealing relationships between these frequent sets—from these big data have drawn attention of many researchers. For instance, since introduction of the research problems of association rule mining or frequent pattern mining, numerous information system and engineering approaches have been developed. These include the development of serial algorithms, distributed and parallel algorithms, as well as MapReduce-based big data mining algorithms. These algorithms can be run in local computers, distributed and parallel environments, as well as clusters, grids and clouds. In this paper, we describe some of these algorithms and discuss how to mine frequent patterns or association rules in fogs—i.e., edges of the computing network.

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References

  1. Frawley, W.J., Piatetsky-Shapiro, G., Matheus, C.J.: Knowledge discovery in databases: an overview. AI Mag. 13(3), 57–70 (1992)

    Google Scholar 

  2. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD 1993, pp. 207–216 (1993)

    Article  Google Scholar 

  3. Leung, C.K., Jiang, F., Cruz, E.M.D., Elango, V.S.: Association rule mining in collaborative filtering. In: Collaborative Filtering Using Data Mining and Analysis, pp. 159–179 (2017)

    Google Scholar 

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

    Google Scholar 

  5. Leung, C.K.: Frequent itemset mining with constraints. In: Encyclopedia of Database Systems, 2nd edn (2018). https://doi.org/10.1007/978-0-387-39940-9_170

  6. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD 2000, pp. 1–12 (2000)

    Article  Google Scholar 

  7. Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings of IEEE ICDM 2001, pp. 441–448 (2001)

    Google Scholar 

  8. Jiang, F., Leung, C.K., Zhang, H.: B-mine: frequent pattern mining and its application to knowledge discovery from social networks. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) APWeb 2016. LNCS, vol. 9931, pp. 316–328. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45814-4_26

    Chapter  Google Scholar 

  9. Zaki, M.J.: Scalable algorithms for association mining. IEEE TKDE 12(3), 372–390 (2000)

    Google Scholar 

  10. Zaki, M.J.: Fast vertical mining using diffsets. In: Proceedings of ACM KDD 2003, pp. 326–335 (2003)

    Google Scholar 

  11. Shenoy, P., Bhalotia, J.R., Bawa, M., Shah, D.: Turbo-charging vertical mining of large databases. In: Proceedings of ACM SIGMOD 2000, pp. 22–33 (2000)

    Article  Google Scholar 

  12. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of IEEE ICDE 1995, pp. 3–14 (1995)

    Google Scholar 

  13. Jiang, F., Leung, C.K., Sarumi, O.A., Zhang, C.Y.: Mining sequential patterns from uncertain big DNA data in the spark framework. In: Proceedings of IEEE BIBM 2016, pp. 874–881 (2016)

    Google Scholar 

  14. Chanda, A.K., Ahmed, C.F., Samiullah, M., Leung, C.K.: A new framework for mining weighted periodic patterns in time series databases. Expert Syst. Appl. 79, 207–224 (2017)

    Article  Google Scholar 

  15. Leung, C.K., Khan, Q.I.: DSTree: a tree structure for the mining of frequent sets from data streams. In: Proceedings of IEEE ICDM 2006, pp. 928–932 (2006)

    Google Scholar 

  16. Shajib, M.B., Samiullah, M., Ahmed, C.F., Leung, C.K., Pazdor, A.G.M.: An efficient approach for mining frequent patterns over uncertain data streams. In: Proceedings of IEEE ICTAI 2016, pp. 980–984 (2016)

    Google Scholar 

  17. Ramamohanarao, K.: Contrast pattern mining and its applications. In: Proceedings of ADC 2010, pp. 5–8 (2010)

    Google Scholar 

  18. Carmichael, C.L., Hayduk, Y., Leung, C.K.: Visually contrast two collections of frequent patterns. In: Proceedings of IEEE ICDM Workshops 2011, pp. 1128–1135 (2011)

    Google Scholar 

  19. 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, vol. 5012, pp. 653–661. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68125-0_61

    Chapter  Google Scholar 

  20. Leung, C.K.-S.: Uncertain frequent pattern mining. In: Aggarwal, C.C., Han, J. (eds.) Frequent Pattern Mining, pp. 339–367. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07821-2_14

    Chapter  MATH  Google Scholar 

  21. Leung, C.K., MacKinnon, R.K., Jiang, F.: Finding efficiencies in frequent pattern mining from big uncertain data. World Wide Web (WWW) 20(3), 571–594 (2017)

    Article  Google Scholar 

  22. Li, Y., Bailey, J., Kulik, L., Pei, J.: Mining probabilistic frequent spatio-temporal sequential patterns with gap constraints from uncertain databases. In: Proceedings of IEEE ICDM 2013, pp. 448–457 (2013)

    Google Scholar 

  23. Zaki, M.J.: Parallel and distributed association mining: a survey. IEEE Concurr. 7(4), 14–25 (1999)

    Article  Google Scholar 

  24. Tanbeer, S.K., Ahmed, C.F., Jeong, B.: Parallel and distributed frequent pattern mining in large databases. In: Proceedings of IEEE HPCC 2009, pp. 407–414 (2009)

    Google Scholar 

  25. Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE TKDE 8(6), 962–969 (1996)

    Google Scholar 

  26. Chandru, V., Mueller, F.: Hybrid MPI/OpenMP programming on the Tilera manycore architecture. In: Proceedings of HPCS 2016, pp. 326–333 (2016)

    Google Scholar 

  27. Utrera, G., Gil, M., Martorell, X.: In search of the best MPI-OpenMP distribution for optimum Intel-MIC cluster performance. In: Proceedings of HPCS 2015, pp. 429–435 (2015)

    Google Scholar 

  28. Rosa, A., Chen, L.Y., Binder, W.: Predicting and mitigating jobs failures in big data clusters. In: Proceedings of IEEE/ACM CCGrid 2015, pp. 221–230 (2015)

    Google Scholar 

  29. Ertl, B., Stevanovic, U., Hayrapetyan, A., Wegh, B., Hardt, M.: Identity harmonization for federated HPC, grid and cloud services. In: Proceedings of HPCS 2016, pp. 621–627 (2016)

    Google Scholar 

  30. 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 (2017)

    Google Scholar 

  31. Leung, C.K.: Big data analysis and mining, Chap. 30. In: Encyclopedia of Information Science and Technology, 4th edn (2017)

    Google Scholar 

  32. Leung, C.K.-S., Hayduk, Y.: Mining frequent patterns from uncertain data with MapReduce for big data analytics. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7825, pp. 440–455. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37487-6_33

    Chapter  Google Scholar 

  33. Fumarola, F., Malerba, D.: A parallel algorithm for approximate frequent itemset mining using MapReduce. In: Proceedings of HPCS 2014, pp. 335–342 (2014)

    Google Scholar 

  34. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of OSDI 2004, pp. 137–150 (2004)

    Google Scholar 

  35. Noor, S., Uddin, V.: MapReduce for multi-view object recognition. In: Proceedings of HPCS 2016, pp. 575–582 (2016)

    Google Scholar 

  36. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. FGCS 25(6), 599–616 (2009)

    Article  Google Scholar 

  37. Han, Z., Leung, C.K.: FIMaaS: scalable frequent itemset mining-as-a-service on cloud for non-expert miners. In: Proceedings of BigDAS 2015, pp. 84–91 (2015)

    Google Scholar 

  38. Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: Proceedings of VLDB 1995, pp. 432–444 (1995)

    Google Scholar 

  39. Wang, K., Tang, L., Han, J., Liu, J.: Top down FP-Growth for association rule mining. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 334–340. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47887-6_34

    Chapter  Google Scholar 

  40. Zaiane, O.R., El-Hajj, M., Lu, P.: Fast parallel association rule mining without candidacy generation. In: Proceedings of IEEE ICDM 2001, pp. 665–668 (2001)

    Google Scholar 

  41. Yu, K.-M., Zhou, J., Hsiao, W.C.: Load balancing approach parallel algorithm for frequent pattern mining. In: Malyshkin, V. (ed.) PaCT 2007. LNCS, vol. 4671, pp. 623–631. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73940-1_63

    Chapter  Google Scholar 

  42. Chen, D., Lai, C., Hu, W., Chen, W.G., Zhang, Y., Zheng, W.: Tree partition based parallel frequent pattern mining on shared memory systems. In: Proceedings of IEEE IPDPS 2006 (2006)

    Google Scholar 

  43. Lin, M., Lee, P., Hsueh, S.: Apriori-based frequent itemset mining algorithms on MapReduce. In: Proceedings of ICUIMC 2012 (2012). Article no. 76

    Google Scholar 

  44. Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2011)

    Book  Google Scholar 

  45. Li, H., Wang, Y., Zhang, D., Zhang, M., Chang, E.Y.: PFP: parallel FP-growth for query recommendation. In: Proceedings of ACM RecSys 2008, pp. 107–114 (2008)

    Google Scholar 

  46. Zhang, Z., Ji, G., Tang, M.: MREclat: an algorithm for parallel mining frequent itemsets. In: Proceedings of CBD 2013, pp. 177–180 (2013)

    Google Scholar 

  47. Snady, M., Emin, A., Bart, G.: Frequent itemset mining for big data. In: Proceedings of IEEE BigData 2013, pp. 111–118 (2013)

    Google Scholar 

  48. Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for Internet of Things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments. SCI, vol. 546, pp. 169–186. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05029-4_7

    Chapter  Google Scholar 

  49. Dastjerdi, A.V., Buyya, R.: Fog computing: helping the Internet of Things realize its potential. IEEE Comput. 49(8), 112–116 (2016)

    Article  Google Scholar 

  50. Linthicum, D.S.: Connecting fog and cloud computing. IEEE Cloud Comput. 4(2), 18–20 (2017)

    Article  Google Scholar 

  51. Cannataro, M., Cuzzocrea, A., Pugliese, A.: A probabilistic approach to model adaptive hypermedia systems. In: Proceedings of WebDyn 2001, pp. 12–30 (2001)

    Google Scholar 

  52. Cuzzocrea, A., Furfaro, F., Saccà, D.: Enabling OLAP in mobile environments via intelligent data cube compression techniques. J. Intell. Inf. Syst. 33(2), 95–143 (2009)

    Article  Google Scholar 

  53. Cuzzocrea, A.: Accuracy control in compressed multidimensional data cubes for quality of answer-based OLAP tools. In: Proceedings of SSDBM 2006, pp. 301–310 (2006)

    Google Scholar 

  54. Cuzzocrea, A., Fortino, G., Rana, O.F.: Managing data and processes in cloud-enabled large-scale sensor networks: state-of-the-art and future research directions. In: Proceedings of IEEE/ACM CCGrid 2013, pp. 583–588 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Manitoba, Winnipeg, Canada

    Peter Braun, Carson K. Leung, Adam G. M. Pazdor & Syed K. Tanbeer

  2. University of Trieste and ICAR-CNR, Trieste, Italy

    Alfredo Cuzzocrea

  3. University of Messina, Messina, Italy

    Giorgio Mario Grasso

Authors
  1. Peter Braun
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  2. Alfredo Cuzzocrea
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  3. Carson K. Leung
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  4. Adam G. M. Pazdor
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  5. Syed K. Tanbeer
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  6. Giorgio Mario Grasso
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Corresponding author

Correspondence to Alfredo Cuzzocrea .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Braun, P., Cuzzocrea, A., Leung, C.K., Pazdor, A.G.M., Tanbeer, S.K., Grasso, G.M. (2018). An Innovative Framework for Supporting Frequent Pattern Mining Problems in IoT Environments. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10964. Springer, Cham. https://doi.org/10.1007/978-3-319-95174-4_49

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  • DOI: https://doi.org/10.1007/978-3-319-95174-4_49

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