Abstract
Advances in information technology prompt a tremendous usage growth of the Internet. Online activities, such as e-commerce, social interaction, etc., have drawn increasing attentions in regard to the provision of personalized services which require best and comprehensive understanding of users. As an approach, this study outlines a general framework based on human (or consumer) contexts for the discovery and creation of business intelligence. Three major portions are discussed. First, the collection of human contexts, including activity logs in both cyber and physical worlds, is modeled. Second, data analysis was performed via proposed mining algorithms that concern potential fusion at different levels according to situations and ultimate purposes. Third, sustenance of developed model is then concentrated. An open platform was developed to support the evolutionary process of human models, and to allow contributions (e.g., data sharing, accessing, etc.) from third parties.
References
Ankur N, Abhinav S and Naga KKP, (2012) “High Performance Offline and Online Distributed Collaborative Filtering,” Data Mining (ICDM), 2012 I.E. 12th International Conference, Brussels, pp. 549–558
Cao B, Shen D, Sun J, Yang Q and Chen Z (2007) “Feature selection in a kernel space,” in International Conference on Machine Learning, Oregon, pp. 121–128
Chandramouli B, Goldstein J and Duan S (2012) “Temporal Analytics on Big Data for Web Advertising,” Data Engineering (ICDE) 2012 I.E. 28th International Conference, Wasington, pp. 90–101
Cook RD, Weisberg S (1982) Criticism and influence analysis in regression. Sociol Methodol 13:313–361
Ewaryst T, Adrian K (2009) Internet-technical development and applications. Springer, Tesco, p 255
Følstad A, Hornbæk K and Ulleberg P (2013) “Social design feedback: evaluations with users in online ad-hoc groups,” Human-centric Computing and Information Sciences, vol. 3, no. 18
Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining in bioinformatics using Weka. Bioinformatics 20(15):2479–2481
Heckerman D (1997) Bayesian networks for data mining. Data Min Knowl Disc 1(1):79–119
Ho TK, Hull JJ, Srihari SN (1994) Decision in multiple classifier systems. IEEE Trans Pattern Anal Mach Intell 16:66–75
Holmes G, Donkin A and Witten IH (1994) “WEKA: A machine learning workbench,” Proceedings of 2nd Australian and New Zealand Conference on Intelligent Information Systems, Brisbane, pp. 357–361
Ibrahim N, Mohammad M and Alagar V (2013) “Publishing and discovering context-dependent services,” Human-centric Computing and Information Sciences, vol. 3, no. 1
Jolliffe IT (1986) Principal component analysis. Springer, Berlin, p 487
Kittler JV, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20:226–239
Kuncheva LI, Bezdek JC, Duin RPW (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recogn 34:299–314
Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51:181–207
Li W, Gong W, Liang Y and W. Chen (2005) “Feature selection based on KPCA, SVM and GSFS for face recognition,” in International Conference on Advances in Pattern Recognition, Bath, pp. 344–350
Linden G, Smith B, York J (2003) Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Comput 7(1):76–80
Ma J, Wen J, Huang R, Huang B (2011) Cyber-individual meets brain informatics. IEEE Int Syst Spec Issue Brain Inform 26(5):30–37
McCrae RR, John OP (1992) An introduction to the five-factor model and its applications, Special issue: the five-factor model: issues and applications. J Pers 60:175–215
Mehrabian A (1996) Analysis of the big-five personality factors in terms of the PAD temperament model. Aust J Psychol : Melb 48(2):86–92
Milenova BL and Campos MM (2005) “Mining high-dimensional data for information fusion: a database-centric approach,” Information Fusion, 2005 8th International Conference, Philadelphia, vol. 1, pp.638-645
Nakada H, Ogawa H, and Kudoh H, (2012) “Stream processing with BigData: SSS-MapReduce,” Cloud Computing Technology and Science (CloudCom), 2012 I.E. 4th International Conference, Taipei, pp. 618–621
Ortony A, Clore GL, Collins A (1988) The cognitive structure of emotions. Cambridge University Press, Cambridge
Rakesh A and Ramakrishnan S (1994) “Fast Algorithms for Mining Association Rules in Large Databases,” Proceedings of 20th International Conference on Very Large Data Bases, Morgan Kaufmann, pp. 487–499
Smirnov A, Pashkin M, Chilov N, Levashova T (2003) KSNet-Approach to Knowledge Fusion from Distributed Sources. Comput Inform 22(2):105–142
Thurau HT, Klee A (1998) The impact of customer satisfaction and relationship quality on customer retention: a critical reassessment and model development. Psychol Mark N J 14(8):737–764
Wang X, Ni Z and Cao H, (2007) “Research on Association Rules Mining Based-On Ontology in E-Commerce,” Wireless Communications, Networking and Mobile Computing (WiCom 2007), Shanghai, pp. 3549–3552
Wang, and Tang T (2005) “A New Data Mining Method based on Fusion Clustering Algorithm,” Neural Networks and Brain, 2005. ICNN&B ‘05. International Conference, Beijing, vol. 2, pp.706-711
Xiao YY and Aiming W (2010) “Genetic Algorithm Based Bayesian Network for Customers, Behavior Analysis,” Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP-2010), 2010 I.E. 6th International Conference, Darmstadt, pp. 406–409
Yan J, Liu N, Wang G, Zhang W, Jiang Y, and Chen Z (2009) “How much can Behavioral Targeting Help Online Advertising?,” the 18th international conference on World wide web (WWW ‘09), Madrid, pp. 261–270
Yang S, Huang R and Ma J (2012) “A Computational Personality-based and Event-driven Emotions Model in PAD Space,” Sino-foreign-interchange Workshop on Intelligence Science & Intelligent Data Engineering, LNCS, Springer, in press
Yang J, Yang JY (2002) Generalized K-L transform based combined feature extrac-tion. Pattern Recogn 35:295–297
Yang J, Yang JY, Zhang D, Lu J (2003) Feature fusion: parallel strategy vs. serial strategy. Pattern Recogn 36:1369–1381
Yaoxue Z, Yuezhi Z (2011) Separating computation and storage with storage virtualization. Comput Commun 34(13):1539–1548
Zhong N, Ma J, Huang R, Liu J, Yao Y, Zhang Y, Chen J (2013) Research challenges and persepctives on Wisdom Web of Things (W2T). J Supercomput 64(3):862–882
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The work is partially supported by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (No. 25330270).
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Huang, R., Sato, A., Tamura, T. et al. Towards next-generation business intelligence: an integrated framework based on DME and KID fusion engine. Multimed Tools Appl 76, 11509–11530 (2017). https://doi.org/10.1007/s11042-014-2387-2
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DOI: https://doi.org/10.1007/s11042-014-2387-2