Abstract
High volumes of wide varieties of valuable data of different veracity can be easily generated or collected at a high velocity from various big data applications and services. A rich source of these big data is the Internet of Things (IoT), which can be viewed as a network of sensors, mobile devices, wearable devices, and other “things” that are capable to operate within the existing Internet infrastructure. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge—in terms of sets of frequently co-occurring items—embedded in these big data. Existing frequent pattern mining algorithms mostly run serially on a single local computer or in distributed and parallel environments on computer clusters, grids, or clouds. Many of these algorithms return large numbers of frequent patterns, of which only some may be of interest to the user. In this paper, we present a constrained big data mining algorithm that (i) focuses the mining to those frequent patterns that are interested to the users and (ii) runs in an edge computing environment, in which computation is performed at edges of the computing network.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, pp. 487–499 (1994)
Bierzynski, K., Escobar, A., Eberl, M.: Cloud, fog and edge: cooperation for the future? In: FMEC 2017, pp. 62–67. IEEE (2017)
Braun, P., Cuzzocrea, A., Doan, L.M.V., Kim, S., Leung, C.K., Matundan, J.F.A., Singh, R.R.: Enhanced prediction of user-preferred YouTube videos based on cleaned viewing pattern history. Procedia Computer Science 112, pp. 2230–2239. Elsevier (2017)
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 (2017)
Braun, P., Cuzzocrea, A., Keding, T.D., Leung, C.K., Pazdor, A.G.M., Sayson, D.: Game data mining: clustering and visualization of online game data in cyber-physical worlds. Procedia Computer Science 112, pp. 2259–2268. Elsevier (2017)
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)
Brown, J.A., Cuzzocrea, A., Kresta, M., Kristjanson, K.D.L., Leung, C.K., Tebinka, T.W.: A machine learning system for supporting advanced knowledge discovery from chess game data. In: IEEE ICMLA 2017, pp. 649–654 (2017)
Choudhery, D., Leung, C.K.: Social media mining: prediction of box office revenue. In: IDEAS 2017, pp. 20–29. ACM (2017)
Cuzzocrea, A., Grasso, G.M., Jiang, F., Leung, C.K.: Mining uplink-downlink user association in wireless heterogeneous networks. In: IDEAL 2016. LNCS, vol. 9937, pp. 533–541 (2016)
Cuzzocrea, A., Leung, C.K., MacKinnon, R.K.: Mining constrained frequent itemsets from distributed uncertain data. Future Generation Computer Systems 37, pp. 117–126. Elsevier (2014)
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 BigDataSE 2017, pp. 925–932 (2017)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD 2000, pp. 1–12 (2000)
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
Jang, S., Chi, S., Yoo, K., Nasridinov, A.: Improving availability of manufacturing plant using association rule. In: BigDAS-L 2016 (2016)
Jiang, F., Leung, C.K., Sarumi, O.A., Zhang, C.Y.: Mining sequential patterns from uncertain big DNA data in the Spark framework. In: IEEE BIBM 2016, pp. 874–881 (2016)
Lakshmanan, L.V.S., Leung, C.K., Ng, R.T.: The segment support map: scalable mining of frequent itemsets. ACM SIGKDD Explorations 2(2), pp. 21–27 (2000)
Lee, R.C., Cuzzocrea, A., Lee, W., Leung, C.K.: Majority voting mechanism in interactive social network clustering. In: ACM WISM 2017, art. 14 (2017)
Leung, C.K.: Big data mining and computing in a smart world. In: IEEE UIC-ATC-ScalCom-CBDCom-IoP 2015, pp. xcviii (2015)
Leung, C.K.: Big data mining applications and services. In: BigDAS 2015, pp. 1–8. ACM (2015) https://doi.org/10.1145/2837060.2837076
Leung, C.K.: Data and visual analytics for emerging databases. In: EDB 2017. LNEE, vol. 461, pp. 203–213 (2017) https://doi.org/10.1007/978-981-10-6520-0_21
Leung, C.K.: Frequent itemset mining with constraints. Encyclopedia of Database Systems, 2e. Springer (2016) https://doi.org/10.1007/978-1-4899-7993-3_170-2
Leung, C.K.: Mathematical model for propagation of influence in a social network. Encyclopedia of Social Network Analysis and Mining, 2e. Springer (2017) https://doi.org/10.1007/978-1-4614-7163-9_110201-1
Leung, C.K.: Mining frequent itemsets from probabilistic datasets. In: EDB 2013, pp. 137–148 (2013)
Leung, C.K., Braun, P., Enkhee, M., Pazdor, A.G.M., Sarumi, O.A., Tran, K.: Knowledge discovery from big social key-value data. In: IEEE CIT 2016, pp. 484–491 (2016)
Leung, C.K., Jiang, F.: Efficient mining of ‘following’ patterns from very big but sparse social networks. In: IEEE/ACM ASONAM 2017, pp. 1025–1032. ACM (2017)
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)
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 (2008)
Leung, C.K., Tanbeer, S.K., Cameron, J.J.: Interactive discovery of influential friends from social networks. Social Network Analysis and Mining 4(1), art. 154. Springer (2014)
Renart, E.G., Diaz-Montes, J., Parashar, M.: Data-driven stream processing at the edge. In: IEEE ICFEC 2017, pp. 31–40 (2017)
Sajjad, H.P., Danniswara, K., Al-Shishtawy, A., Vlassov, V.: SpanEdge: towards unifying stream proccessing over central and near-the-edge data centers. In: IEEE/ACM SEC 2016, pp. 168–178. IEEE (2016)
Song, Y., Yau, S.S., Yu, R., Zhang, X., Xue, G.: An approach to QoS-based task distribution in edge computing networks for IoT applications. In: IEEE EDGE 2017, pp. 32–39 (2017)
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)
Acknowledgements
This project is partially supported by (i) Natural Sciences and Engineering Research Council of Canada (NSERC) and (ii) University of Manitoba.
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Leung, C.K., Deng, D., Hoi, C.S.H., Lee, W. (2019). Constrained Big Data Mining in an Edge Computing Environment. In: Lee, W., Leung, C. (eds) Big Data Applications and Services 2017. BIGDAS 2017. Advances in Intelligent Systems and Computing, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-13-0695-2_8
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DOI: https://doi.org/10.1007/978-981-13-0695-2_8
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