Parameter dependencies for reusable performance specifications of software components

To avoid design-related per­for­mance problems, model-driven performance prediction methods analyse the response times, throughputs, and re­source utilizations of software architectures before and during implementation. This thesis proposes new modeling languages and according model transformations, which allow a reusable description of usage profile dependencies to the performance of software components. Predictions based on this new methods can support performance-related design decisions

Formulation of tradeoffs in planning under uncertainty

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1988.Includes bibliographical references.by Michael Paul Wellman.Ph.D

New Techniques for Learning Parameters in Bayesian Networks.

PhDOne of the hardest challenges in building a realistic Bayesian network (BN) model is to construct the node probability tables (NPTs). Even with a fixed predefined model structure and very large amounts of relevant data, machine learning methods do not consistently achieve great accuracy compared to the ground truth when learning the NPT entries (parameters). Hence, it is widely believed that incorporating expert judgment or related domain knowledge can improve the parameter learning accuracy. This is especially true in the sparse data situation. Expert judgments come in many forms. In this thesis we focus on expert judgment that specifies inequality or equality relationships among variables. Related domain knowledge is data that comes from a different but related problem. By exploiting expert judgment and related knowledge, this thesis makes novel contributions to improve the BN parameter learning performance, including: • The multinomial parameter learning model with interior constraints (MPL-C) and exterior constraints (MPL-EC). This model itself is an auxiliary BN, which encodes the multinomial parameter learning process and constraints elicited from the expert judgments. • The BN parameter transfer learning (BNPTL) algorithm. Given some potentially related (source) BNs, this algorithm automatically explores the most relevant source BN and BN fragments, and fuses the selected source and target parameters in a robust way. • A generic BN parameter learning framework. This framework uses both expert judgments and transferred knowledge to improve the learning accuracy. This framework transfers the mined data statistics from the source network as the parameter priors of the target network. Experiments based on the BNs from a well-known repository as well as two realworld case studies using different data sample sizes demonstrate that the proposed new approaches can achieve much greater learning accuracy compared to other state-of-theart methods with relatively sparse data.China Scholarship Counci

Model-Based Performance Prediction for Concurrent Software on Multicore Architectures

Model-based performance prediction is a well-known concept to ensure the quality of software.Current approaches are based on a single-metric model, which leads to inaccurate predictions for modern architectures. This thesis presents a multi-strategies approach to extend performance prediction models to support multicore architectures.We implemented the strategies into Palladio and significantly increased the performance prediction power

Strategies to Improve Millennial Employee Engagement in the Luxury Resort Industry

Millennials are estimated to compose half of the workforce by 2020. Many hospitality researchers have studied Millennial employee engagement, but less is known about how to apply strategies that are authentically engaging for Millennials. The purpose of this study was to explore Millennial employee engagement strategies. The research questions for this study were used to examine the engagement strategies of luxury resort leaders and how Millennial employees perceived engagement. A single case study design was used to gather interview, questionnaire, and company document data from employees of a luxury resort in Hawai`i. Kahn\u27s employee engagement theory served as the basis for the conceptual framework. Six non-Millennial department heads participated in semistructured interviews by purposeful sampling and 11 Millennial employees completed an online, anonymous questionnaire. Saldana\u27s 2-cycle coding analysis was used to determine themes based upon the conceptual framework, participant descriptions of engagement, and commonalities among effective strategies. The 3 most significant themes were the importance of (a) interpersonal respect, (b) interpersonal trust, and (c) meaningful relationships. Another worthwhile finding was the difference in perceptions of engagement aspects between Millennials and other generations. To fully engage Millennial employees, luxury resort leaders should focus on thoughtful communication, empathy, and relationship-building strategies. The implications for social change include the potential to foster happy, productive Millennial employees who contribute to the performance of their organizations. When resort leaders increase their skills to build respect, trust, and meaningful relationships, they improve workplace culture for all employees

A selective list of acronyms and abbreviations

A glossary of acronyms, abbreviations, initials, code words, and phrases used at the John F. Kennedy Space Center is presented. The revision contains more than 12,100 entries