A Study on Big Data Platform Architecture-based Conceptual Measurement Model Using Comparative Analysis for Social Commerce
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With the Big Data growing in the popularity, more and more architectures are being proposed, but no unified solution is at hand. The main objective of this thesis is to specify and prepare a conceptual measurement model on which selected Big Data Platform Architecture will be mapped and compared. The conceptual measurement model is split into multiple components with the specification of responsibilities, and the their scope of the conceptual measurement model is set. All architectures mapped in a single structure are then evaluated and compared. Our research contains a brief overview of the Big Data concept and a summary of technologies used in evaluated architectures, if available. In addition, social commerce is a new paradigm infrastructure model of commercial market, which use of Web 3.0 technologies and social data to support their exchange activities. While it is popular, being a subset of commercial market, has been increasing tremendously since its introduction in 2012, there exists a general particular of research on their framework and applications’ effectiveness, especially in areas beyond the general social commerce practices. This research develops a comprehensive social commerce framework in big data environment that has four key components. Then our research applies the Big Data Platform (BDP) to analyze the usability and adaptability with the framework by applying it to social commerce successful companies. Accordingly, we provide a set of metrics for social commerce-based Big Data Platform design. The proposed our framework and they are used to lead the design and evaluation in social commerce environment. This research makes a contribution to the social commerce in proposing a new design framework and also, we have to examine in providing inside for effective approach of concerning with their social business.
Abstract
With the Big Data growing in the popularity, more and more architectures are being proposed, but no unified solution is at hand. The main objective of this thesis is to specify and prepare a conceptual measurement model on which selected Big Data Platform Architecture will be mapped and compared. The conceptual measurement model is split into multiple components with the specification of responsibilities, and the their scope of the conceptual measurement model is set. All architectures mapped in a single structure are then evaluated and compared. Our research contains a brief overview of the Big Data concept and a summary of technologies used in evaluated architectures, if available. In addition, social commerce is a new paradigm infrastructure model of commercial market, which use of Web 3.0 technologies and social data to support their exchange activities. While it is popular, being a subset of commercial market, has been increasing tremendously since its introduction in 2012, there exists a general particular of research on their framework and applications’ effectiveness, especially in areas beyond the general social commerce practices. This research develops a comprehensive social commerce framework in big data environment that has four key components. Then our research applies the Big Data Platform (BDP) to analyze the usability and adaptability with the framework by applying it to social commerce successful companies. Accordingly, we provide a set of metrics for social commerce-based Big Data Platform design. The proposed our framework and they are used to lead the design and evaluation in social commerce environment. This research makes a contribution to the social commerce in proposing a new design framework and also, we have to examine in providing inside for effective approach of concerning with their social business.
참고문헌 (16)
- P. Gaikwad, "Anomaly detection for scientific workflow applications on networked clouds", High Performance Computing & Simulation (HPCS), 2016International Conference on, pp.645~652 (2016), IEEE
- N. Pandeeswari, "Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN", Mob. Netw. Appl, 21(3), pp.494~505 (2016)
- R. J. Hyndman, "Large-scale unusual time series detection", 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp.1616~1619 (2015), IEEE
- B. Agrawal, "R2Time: A Framework to Analyse Open TSDB Time-Series Data in HBase", Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference, pp.970~975 (2014)
- M. Solaimani, "Spark-basedanomaly detection over multi-source VMware performance data in real-time", Computational Intelligence in Cyber Security (CICS), 2014 IEEE Symposium, pp.1~8 (2014)
- Y. Chen, "A new one-class SVM for anomaly detection", Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference, pp.3567~3571 (2013)
- L. Yu, "A scalable, non-parametric anomaly detection framework for hadoop", Proceedings of the 2013ACM Cloud and Autonomic Computing Conference, pp.22~ (2013)
- M. Zaharia, "Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing", Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, pp.2~ (2012), USENIX Association
- V. Chandrasekaran, "Rank-sparsity incoherence for matrix decomposition", SIAM Journal on Optimization, 21(2), pp.572~596 (2011)
- C. Wang, "Statistical techniques for online anomaly detection in data centers", Integrated Network Management (IM), 2011 IFIP, 2011, 385~392[DOI]
- 김정식, "A Study on Adaptive Smart Platform for Intelligent Software in Big Data Environment", 한국지식정보기술학회 논문지, 15(3), pp.347~355 (2020), 한국지식정보기술학회[DOI]
- C. Wang, "Online detection of utility cloud anomalies using metric distributions", Network Operations and Management Symposium (NOMS), 2010 IEEE, pp.96~103 (2010)
- J. Wright, "Robust principal component analysis : Exact recovery of corrupted low-rank matrices via convex optimization", Advances in neural information processing systems, 2080~2088 (2009)
- K. H. Ramah, "NETWORKING 2006. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems", pp.136~147 (2006), Springer
- M. Jahrer, "Ensemble of collaborative filtering and feature engineered models for click through rate prediction", KDDCup Workshop (2012)
- D. E. Bloom, "Global population aging: Facts, challenges, solutions & perspectives", Daedalus, 144(2), pp.80~92 (2015)