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Automatic Discovery of Technology Networks for Industrial-Scale R&D IT Projects via Data Mining | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 3، دوره 47، شماره 1، تیر 2015، صفحه 17-22 اصل مقاله (578.19 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2015.517 | ||
| نویسندگان | ||
| S. Azimi1؛ H. Veisi* 2؛ R. Rahmani2 | ||
| 1Faculty Member, Department of New Sciences & Technology, Tehran University, Tehran, Iran | ||
| 2Assistant Professor, Department of New Sciences & Technology, Tehran University, Tehran, Iran | ||
| چکیده | ||
| Industrial-Scale R&D IT Projects depend on many sub-technologies which need to be understood and have their risks analysed before the project can begin for their success. When planning such an industrial-scale project, the list of technologies and the associations of these technologies with each other is often complex and form a network. Discovery of this network of technologies is time consuming for a human to perform, due to the large number of technologies and due to the fact that the technologies are constantly changing. In this paper, a method is provided for the automatic discovery of the network of associations of Industrial IT technologies as a networked graph, using data mining and web-mining algorithms. The proposed process is an approach to form a dynamic weighted graph of technologies. A numeric value is calculated as similarity between technologies. A combination of data mining and web mining techniques have been used to achieve the results. The main objective is to invent a computerized reproducible method so that by the help of it, technological relation can be extracted and updated constantly. This method consists of six phases, of which four phases are performed automatically by novel algorithms introduced in this paper. The analysis of more than 8 million terms suggests that the proposed method provides acceptable results. This paper also provided recommendations to improve the suggested method. | ||
| کلیدواژه‌ها | ||
| Technology Mining؛ Technology Graph؛ Data Mining؛ Text Mining؛ Similarity Algorithms؛ Web Robots | ||
| عنوان مقاله [English] | ||
| Automatic Discovery of Technology Networks for Industrial-Scale R&D IT Projects via Data Mining | ||
| نویسندگان [English] | ||
| S. Azimi1؛ H. Veisi2؛ R. Rahmani2 | ||
| 1Faculty Member, Department of New Sciences & Technology, Tehran University, Tehran, Iran | ||
| 2Assistant Professor, Department of New Sciences & Technology, Tehran University, Tehran, Iran | ||
| چکیده [English] | ||
| Industrial-Scale R&D IT Projects depend on many sub-technologies which need to be understood and have their risks analysed before the project can begin for their success. When planning such an industrial-scale project, the list of technologies and the associations of these technologies with each other is often complex and form a network. Discovery of this network of technologies is time consuming for a human to perform, due to the large number of technologies and due to the fact that the technologies are constantly changing. In this paper, a method is provided for the automatic discovery of the network of associations of Industrial IT technologies as a networked graph, using data mining and web-mining algorithms. The proposed process is an approach to form a dynamic weighted graph of technologies. A numeric value is calculated as similarity between technologies. A combination of data mining and web mining techniques have been used to achieve the results. The main objective is to invent a computerized reproducible method so that by the help of it, technological relation can be extracted and updated constantly. This method consists of six phases, of which four phases are performed automatically by novel algorithms introduced in this paper. The analysis of more than 8 million terms suggests that the proposed method provides acceptable results. This paper also provided recommendations to improve the suggested method. | ||
| کلیدواژه‌ها [English] | ||
| Technology Mining, Technology Graph, Data Mining, Text Mining, Similarity Algorithms, Web Robots | ||
| مراجع | ||
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[1] Flyvbjerg, Bent, and Alexander Budzier. "Why your IT project may be riskier than you think." Harvard Business Review 89.9 (2011): 601-603. [2] Yacov Y. Haimes, Risk Modeling, Assessment, and Management, 2005, Wiley Books, 615-629 [3] https://en.wikipedia.org/wiki/NHS_Connecting_for_Health, viewed at 2014 [4] Alan L. Porter, “Technology futures analysis: Toward integration of the field and new methods”, Technological Forecasting & Social Change 71 (2004) 287–303 [5] Alan L. Porter, “Quick Technology Intelligence Processes”, eu-us seminar: new technology foresight, forecasting & assessment methods-seville 13-14 may 2004 [6] Belver c. Griffith –“Mapping the Scientific Literature.” NATO Advances Study Institutes Series Volume 10, 1975, pp 457-481 [7] Melkérs, J., bibliometrics as a Tool for Analysis of R&D impact, in Evaluating R&D impacts: Methods and Pracüce, B. Bozeman and J. Melkers, eds., Kluwer, Boston, 1993, pp. 43-61 [8] Donghua Zhu, Alan Porter, Scott Cunningham, Judith Carlisie, Anustup Nayak. "A process for mining science & technology documents databases", illustrated for the case of "knowledge discovery and data mining". Ci. Inf. vol.28 n.1 Brasilia Jan. 1999 [9] Behrang QasemiZadeh, "Towards Technology Structure Mining from Scientic Literature" [10] Information Technology Encolopedia, http://whatis.techtarget.com/glossaries, Viewed at 2014 [11] The Tech Terms Computer Dictionary, http://www.techterms.com/, Viewed at 2014 [12] Category:Information technology, http://en.wikipedia.org/wiki/Category:Information_technology, Viewed at 2014 [13] Tech dictionary for IT professionals and educators, www.webopedia.com, Viewed at 2014 [14] Advancing Technology for Humanity, http://www.ieee.org/, Viewed at 2014 [15] Han, Jiawei, Jian Pei, and Yiwen Yin. "Mining frequent patterns without candidate generation." ACM SIGMOD Record. Vol. 29. No. 2. ACM, 2000. [16] Agrawal, Rakesh, and Ramakrishnan Srikant. "Mining sequential patterns." Data Engineering, 1995. Proceedings of the Eleventh International Conference on. IEEE, 1995. [17] T. P. Martin and M. Azmi-Murad, An Incremental Algorithm to find Asymmetric Word Similarities for Fuzzy Text Mining, Soft Computing as Transdisciplinary Science and Technology Advances in Soft Computing Volume 29, 2005, pp 838-8 [18] Zhao, Qiankun, and Sourav S. Bhowmick. "Association rule mining: A survey." Nanyang Technological University, Singapore (2003). | ||
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