By Chengqi Zhang, Wei Huang, Yong Shi, Philip S. Yu, Yangyong Zhu, Yingjie Tian, Peng Zhang, Jing He
This ebook constitutes the refereed court cases of the second one overseas convention on facts technological know-how, ICDS 2015, held in Sydney, Australia, in August 8-9, 2015.
The 19 revised complete papers and five brief papers awarded have been conscientiously reviewed and chosen from 31 submissions. The papers specialise in the next issues: mathematical concerns in facts technological know-how; immense facts matters and functions; info caliber and knowledge training; data-driven clinical study; review and size in facts provider; huge info mining and data administration; case examine of knowledge technology; social affects of knowledge science.
Read Online or Download Data Science: Second International Conference, ICDS 2015 Sydney, Australia, August 8–9, 2015. Proceedings PDF
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Survey of signed network research. J. Softw. 25(1), 1–15 (2014) 2. : Analysis of community structure in networks of correlated data. Phys. Rev. E 80(1), 016114-1–016114-5 (2009) 3. : Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19(10), 1333–1348 (2007) 4. : Community mining in signed social networks - an automated approach, vol. 2, pp. 152–157. IPCSIT © IACSIT Press, Singapore (2011) 5. : Density-based shrinkage for revealing hierarchical and overlapping community structure in networks.
B) Location-based sequential pattern mining, describes this case. The sequence is divided into two different ones by location A and B. Two rental patterns of location A (red, grey) and location B (yellow, green) replace the previous traditional pattern with more accuracy. g. daily. Detecting high utility items in usage-time figure can help find out more accurate pattern (yellow, red, green) in rental behavior prediction. To implement high efficient sequential pattern mining by fleet tracking in rental industry, we provide a novel framework to conduct rental pattern mining with proposed modeling algorithms.
Measuring the benefits of ERP on supply management maturity model: a “Big Data” method. Int. J. Oper. Prod. Manage. 35, 2–25 (2015) 3. : Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute (2011) 4. : Policies for knowledge refreshing in databases. Omega 37, 16– 28 (2009) 5. : Modeling data and process quality in multi-input. Multi-output Inf. Syst. Manage. Sci. 31, 150–162 (1985) 6. : Optimal update policies for distributed materialized views. Manage.