Global Competence: Determination of its Importance for Engineers Working in a Global Environment

We live and work in a world that is even more interconnected and interdependent than ever before. Engineers must now not only develop technical engineering competence, but must also develop additional skills and competencies including global competence to obtain success within a global engineering environment. The purpose of this study was to determine whether multinational companies considered global competence an important skill in mechanical engineering graduates when making hiring decisions. The study was an exploratory study that utilized an extensive literature review to identify eight global competencies for engineering success within a global environment and also included a survey instrument completed by Brigham Young University (BYU) mechanical engineering alumni in 48 states and 17 countries. The study focused on an evaluation of standard hiring technical engineering competencies with eight global competencies identified in the literature review. The study established that standard engineering technical competencies were the most important consideration when hiring mechanical engineers, but global competence was also considered important by a majority of all survey respondents with six of the eight global competencies rated important by 79 to 91% of respondents with an ability to communicate cross-culturally the highest-rated global competence. The importance of global competence in engineers when making hiring decisions, as considered by large companies who employed more than 10,000 employees or who had annual revenue exceeding $1 billion (US$) per year, was particularly strong. The majority of respondents (70%) indicated that companies were willing to provide training and experience to help engineers obtain success in a global engineering environment. In addition, a majority of respondents (59.9%) indicated that companies valued the efforts of higher educational engineering institutions to prepare engineers for success in a global environment with only 4.8% of respondents indicating that they did not value the efforts of higher education engineering institutions. However, only 27% of respondents agreed that colleges and universities were successful in this endeavor. Globalization is not a passing phenomenon, it is here to stay. Colleges and universities throughout the world need to recognize the importance of globalization and the interdependence and interconnectedness among the world’s population. Therefore, it is important to identify, develop, and provide opportunities for international collaboration and interaction among students and faculty throughout the world and to focus on developing global competence as an important outcome for engineering graduates.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.