A path-based framework for analyzing large markov models

Reliability and dependability modeling can be employed during many stages of analysis of a computing system to gain insights into its critical behaviors. To provide useful results, realistic models of systems are often necessarily large and complex. Numerical analysis of these models presents a formidable challenge because the sizes of their state-space descriptions grow exponentially in proportion to the sizes of the models. On the other hand, simulation of the models requires analysis of many trajectories in order to compute statistically correct solutions. This dissertation presents a novel framework for performing both numerical analysis and simulation. The new numerical approach computes bounds on the solutions of transient measures in large continuous-time Markov chains (CTMCs). It extends existing path-based and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship makes it possible for the approach to explore multiple paths at the same time,· thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow it to analyze very large models using a very small amount of storage. Often, path-based techniques must compute many paths to obtain tight bounds. In addition to presenting the basic path-based approach, we also present algorithms for computing more paths and tighter bounds quickly. One resulting approach is based on the concept of path composition whereby precomputed subpaths are composed to compute the whole paths efficiently. Another approach is based on selecting important paths (among a set of many paths) for evaluation. Many path-based techniques suffer from having to evaluate many (unimportant) paths. Evaluating the important ones helps to compute tight bounds efficiently and quickly.

Public firm sponsorship of open source software: A knowledge-based approach to open innovation

As an emerging innovation paradigm gaining momentum in recent years, the open innovation paradigm is calling for greater theoretical depth and more empirical research. This dissertation proposes that open innovation in the context of open source software sponsorship may be viewed as knowledge strategies of the firm. Hence, this dissertation examines the performance determinants of open innovation through the lens of knowledge-based perspectives. Using event study and regression methodologies, this dissertation found that these open source software sponsorship events can indeed boost the stock market performance of US public firms. In addition, both the knowledge capabilities of the firms and the knowledge profiles of the open source projects they sponsor matter for performance. In terms of firm knowledge capabilities, internet service firms perform better than other firms owing to their advantageous complementary capabilities. Also, strong knowledge exploitation capabilities of the firm are positively associated with performance. In terms of the knowledge profile of sponsored projects, platform projects perform better than component projects. Also, community-originated projects outperform firm-originated projects. Finally, based on these findings, this dissertation discussed the important theoretical implications for the strategic tradeoff between knowledge protection and sharing.