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Data and Visual Analytics for Emerging Databases

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Proceedings of the 7th International Conference on Emerging Databases

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 461))

Abstract

With advances in technology, high volumes of valuable data of different veracity can be generated at a high velocity in wide varieties of data sources in various real-life applications. Examples of these big data include social media data. As a popular data mining tasks, frequent pattern mining discovers implicit, previously unknown and potentially useful knowledge in the form of sets of frequently co-occurring items or events. Many existing data mining algorithms return to users with long textual lists of frequent patterns, which may not be easily comprehensible. Given a picture is worth a thousand words, having a visual means for humans to interact with computers would be beneficial. In this paper, we present a framework for data and visual analytics for emerging databases. In particular, our data and visual analytic framework focuses on mining and analyzing social media data, as well as visualizing the mined ‘following’ patterns that reveal those groups of frequently followed social entities in a social network.

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References

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

    Google Scholar 

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

    Google Scholar 

  3. Bernhard, S.D., Leung, C.K., Reimer, V.J., Westlake, J.: Clickstream prediction using sequential stream mining techniques with Markov chains. In: IDEAS 2016, pp. 24–33 (2016)

    Google Scholar 

  4. Braun, P., Cuzzocrea, A., Leung, C.K., Pazdor, A.G.M., Tanbeer, S.K.: Mining frequent patterns from IoT devices with fog computing. In: HPCS 2017, pp. 691–698. IEEE (2017)

    Google Scholar 

  5. Choudhery, D., Leung, C.K.: Social media mining: prediction of box office revenue. In: IDEAS 2017, pp. 20–29 (2017). doi:10.1145/3105831.3105854

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

    Google Scholar 

  7. Dubois, P.M.J., Han, Z., Jiang, F., Leung, C.K.: An interactive circular visual analytic tool for visualization of web data. In: IEEE/WIC/ACM WI 2016, pp. 709–712 (2016)

    Google Scholar 

  8. Duong, V.T.T., Khan, K.-U., Jeong, B.-S., Lee, Y.-K.: Top-k frequent induced subgraph mining using sampling. In: EDB 2016, pp. 110–113 (2016)

    Google Scholar 

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

    Google Scholar 

  10. Hoque, M.N., Ahmed, C.F., Lachiche, N., Leung, C.K., Zhang, H.: Reframing in clustering. In: IEEE ICTAI 2016, pp. 350–354 (2016)

    Google Scholar 

  11. Jiang, F., Leung, C.K.: Mining interesting “following” patterns from social networks. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 308–319. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10160-6_28

  12. 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, Part I. LNCS, vol. 9931, pp. 316–328. Springer, Heidelberg (2016). doi:10.1007/978-3-319-45814-4_26

  13. Lee, J.H., Kim, J.M., Choi, Y.S.: SNS data visualization for analyzing spatial-temporal distribution of social anxiety. In: EDB 2016, pp. 1106–1109 (2016)

    Google Scholar 

  14. Lee, R.C., Cuzzocrea, A., Lee, W., Leung, C.K.: Majority voting mechanism in interactive social network clustering. In: ACM WISM 2017, Article no. 14 (2017). doi:10.1145/3102254.3102268

  15. Lee, W., Song, J.J.S., Leung, C.K.-S.: Categorical data skyline using classification tree. In: Du, X., Fan, W., Wang, J., Peng, Z., Sharaf, M.A. (eds.) APWeb 2011. LNCS, vol. 6612, pp. 181–187. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20291-9_19

    Chapter  Google Scholar 

  16. Leung, C.K.: Mining frequent itemsets from probabilistic datasets. In: EDB 2013, pp. 137–148 (2013)

    Google Scholar 

  17. Leung, C.K., Carmichael, C.L., Hayduk, Y., Jiang, F., Kononov, V.V., Pazdor, A.G.M.: Data mining meets HCI: data and visual analytics of frequent patterns. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML-PKDD 2016, Part III. LNCS (LNAI), vol. 9853, pp. 289–293. Springer, Heidelberg (2016). doi:10.1007/978-3-319-46131-1_37

  18. Leung, C.K., Carmichael, C.L.: FpVAT: a visual analytic tool for supporting frequent pattern mining. ACM SIGKDD Explor. 11(2), 39–48 (2009)

    Article  Google Scholar 

  19. Leung, C.K., Carmichael, C.L., Johnstone, P., Xing, R.R., Yuen, D.S.H.: Interactive visual analytics of big data. In: Ontologies and Big Data Considerations for Effective Intelligence, pp. 1–26 (2017)

    Google Scholar 

  20. Leung, C.K., Dela Cruz, E.M., Cook, T.L., Jiang, F.: Mining ‘following’ patterns from big sparse social networks. In: IEEE/ACM ASONAM 2016, pp. 923–930 (2016)

    Google Scholar 

  21. Leung, C.K.-S., Irani, P.P., Carmichael, C.L.: FIsViz: a frequent itemset visualizer. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 644–652. Springer, Heidelberg (2008). doi:10.1007/978-3-540-68125-0_60

    Chapter  Google Scholar 

  22. Leung, C.K., Irani, P.P., Carmichael, C.L.: WiFIsViz: effective visualization of frequent itemsets. In: IEEE ICDM 2008, pp. 875–880 (2008)

    Google Scholar 

  23. Leung, C.K., Jiang, F.: Big data analytics of social networks for the discovery of “following” patterns. In: Madria, S., Hara, T. (eds.) DaWaK 2015. LNCS, vol. 9263, pp. 123–135. Springer, Heidelberg (2015). doi:10.1007/978-3-319-22729-0_10

  24. Leung, C.K.-S., Jiang, F.: RadialViz: an orientation-free frequent pattern visualizer. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS (LNAI), vol. 7302, pp. 322–334. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30220-6_27

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

    Google Scholar 

  26. Leung, C.K., Jiang, F., Pazdor, A.G.M., Peddle, A.M.: Parallel social network mining for interesting ‘following’ patterns. Concurrency Comput. Pract. Exper. 28(15), 3994–4012 (2016)

    Article  Google Scholar 

  27. Leung, C.K., Jiang, F., Poon, T.W., Crevier, P.-E.: 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 (2018). doi:10.1007/978-3-319-60255-4_1

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  32. MacKinnon, R.K., Leung, C.K.: Stock price prediction in undirected graphs using a structural support vector machine. In: IEEE/WIC/ACM WI-IAT 2015, vol. 1, pp. 548–555 (2015)

    Google Scholar 

  33. Mateo, M.A.F., Leung, C.K.: Design and development of a prototype system for detecting abnormal weather observations. In: C3S2E 2008, pp. 45–59 (2008)

    Google Scholar 

  34. Moens, S., Aksehirli, E., Goethals, B.: Frequent itemset mining for big data. In: IEEE BigData 2013, pp. 111–118 (2013)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    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). doi:10.1007/3-540-47887-6_34

    Chapter  Google Scholar 

  40. You, Y.S., Lee, S., Kim, J.: Design and development of visualization tool for movie review and sentiment analysis. In: EDB 2016, pp. 117–123 (2016)

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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Acknowledgement

This project is partially supported by NSERC (Canada) and University of Manitoba.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Carson K. Leung

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  1. Carson K. Leung
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Correspondence to Carson K. Leung .

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Editors and Affiliations

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

    Wookey Lee

  2. Department of Information and Communication Engineering, Inha University, Incheon, Korea (Republic of)

    Wonik Choi

  3. Department of Computer Science and Engineering, Sogang University, Seoul, Korea (Republic of)

    Sungwon Jung

  4. Department of Library and Information Science, Yonsei University, Seoul, Korea (Republic of)

    Min Song

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Leung, C.K. (2018). Data and Visual Analytics for Emerging Databases. In: Lee, W., Choi, W., Jung, S., Song, M. (eds) Proceedings of the 7th International Conference on Emerging Databases. Lecture Notes in Electrical Engineering, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-10-6520-0_21

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  • DOI: https://doi.org/10.1007/978-981-10-6520-0_21

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  • Publisher Name: Springer, Singapore

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  • Online ISBN: 978-981-10-6520-0

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