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An Approach to Parallelization of Remote Data Integration Tasks

References V. L. Sauter, Decision Support Systems for Business Intelligence. USA: Wiley, 2011, pp. 3-4. M. Casters, et al. , Pentaho Kettle Solutions: Building Open Source ETL Solutions with Pentaho Data Integration Canada: Wiley, 2010, pp. 18. A. D. Giordano, Data Integration Blueprint and Modeling: Techniques for a Scalable and Sustainable Architecture. USA: IBM Press, 2011, pp. 7-8. J. Wang, et al. , "A dynamic data integration model based on SOA

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Tool Integration to Support SPEM Model Transformations in Eclipse

: JUMI Publishing House Ltd., 2009, pp. 408-415. O. Nikiforova, V. Nikulsins, and U. Sukovskis, "Integration of MDA framework into the model of traditional software development," in Selected Papers from the Eighth International Baltic Conference Baltic DB&IS 2008 , H.-M. Haav and A. Kalja, Eds., Series "Frontiers in Artificial Intelligence and Applications", Databases and Information Systems V. IOS Press, 2009, pp. 229-242. V. Nikulsins, O. Nikiforova, and U. Sukovskis, "Mapping of MDA models into the software

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Practice and Challenge of International Peer Review: A Case Study of Research Evaluation of CAS Centers for Excellence

researchers engineers and industrialized personnel multidisciplinary research teams Forms of • Solve major scientific • Breakthrough key technologies • Technological services with open • Providing scientific suggestions and research problems • Provide systematic integration solutions sharing, efficient operation and constructive solutions for output • Open up new research • Develop new technologies and standards satisfactory users macro-decision-making and areas • Incubation of new industries and • Major

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Methods and Practices for Institutional Benchmarking based on Research Impact and Competitiveness: A Case Study of ShanghaiTech University

ShanghaiTech. We understand that any analytic result like the one presented here is only a beginning for further explorations. Consequently, new techniques and tools are needed to integrate original data, results. Such tools are needed to drill down, expand, connect, fuse, or otherwise analyze data, leading to reports that are read by researchers or decision-makers to explore new questions stimulated by the results. The authors are planning further improvements in the second and future phases of benchmarking. Acknowledgement The authors thank Xiaolin Zhang of

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Several Ideas on Integration of SCRUM Practices within Microsoft Solutions Framework

Unified Process: An Introduction, Addison-Wesley, 2004. [18] O. Nikiforova, V. Nikulsins and U. Sukovskis, “Integration of MDA framework into the model of traditional software development,” Frontiers in Artificial Intelligence and Applications, vol. 187, issue 1, 2009, pp. 229-239. https://doi.org/10.3233/978-1-58603-939-4-229

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Single Robot Localisation Approach for Indoor Robotic Systems through Integration of Odometry and Artificial Landmarks

Abstract

we present an integrated approach for robot localization that allows to integrate for the artificial landmark localization data with odometric sensors and signal transfer function data to provide means for different practical application scenarios. The sensor data fusion deals with asynchronous sensor data using inverse Laplace transform. We demonstrate a simulation software system that ensures smooth integration of the odometry-based and signal transfer - based localization into one approach.

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Measuring and Visualizing Research Collaboration and Productivity

gauge interdisciplinary research knowledge interchange ( Zhang, Rousseau, & Gläzel, 2016 ), including “Integration scores” ( Porter et al., 2007 ; 2008 ), Rao-Stirling diversity ( Rafols & Meyer, 2010 ; Stirling, 2007 ), and Diffusion scores ( Carley & Porter, 2012 ; Garner, Porter, & Newman, 2014 ) Cross-research domain knowledge interchange (Kwon et al., under review; Porter et al., 2013 ) Science overlay maps to visually represent the diversity of publication, citation, or citing sub-disciplinary involvement (Carley et al., under review; Leydesdorff et al

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Data-driven Discovery: A New Era of Exploiting the Literature and Data

Dr Ying Ding is an Associate Professor of Indiana University, USA, Co-Editor-in-Chief of Journal of Data and Information Science (JDIS). She is Associate Director of Data Science Online Program, and Director of Web Science Lab. She is Changjiang Scholar at Wuhan University and Elsevier Guest Professor at Tongji University. Her research interests include scholarly communication for knowledge discovery, semantic Web for drug discovery, social network analysis for research impact, and data integration and mediation in Web 2.0. She has published more than 200

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Trends Analysis of Graphene Research and Development

assembly and integration. Development, improvement and optimization of preparation methods and techniques will be needed to maximize all the outstanding qualities of graphene. In conclusion, graphene research and development has shown promising application potential across a wide range of fields, but challenges still exist in technological breakthrough in its preparation methods and processes in order to realize its industrialization for leading innovation in next-generation materials. Acknowledgement Special acknowledgement is to Mr. Matthew Toussant who gave

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