Univ., Diss. u.d.T.: Getty, Michael: A constraint-based approach to the meter of Beowulf--Stanford, 1998.

Agriculture is currently the leading cause of stream impairment in the United States. As the population continues to grow as well as the demand for biofuels, more pressure is being exerted on land to produce greater quantities of food. To satisfy the need for increased production marginal forest and grasslands have been converted to agriculture, fertilizers and equipment have rapidly evolved, and land has been taken out of conservation programs. Unfortunately, water quality impairment often accompanies these efforts to increase crop production. To reduce the impacts of agriculture on water quality, best management practices (BMPs) have been developed and tested at the field scale, with fewer studies focusing on the effects of agricultural land cover and BMPs (e.g., riparian buffers) on water quality at the watershed scale. Thus, a study was designed to assess the effects of riparian buffers and agricultural land cover on water quality at the watershed scale. Within Richland and Silver Creek watersheds, tributaries of the Lower Kaskaskia River Watershed in Illinois, forty-three catchments ranging from 12 to 50 km2 were selected across an agricultural to urban land cover gradient. Between January 18, 2008 and August 3, 2009, grab samples were collected twice a month during the wet portion of the year and once a month during the dry portion of the year and analyzed for nutrients (ammonium, nitrate, and orthophosphate), bacteria (total coliform, fecal coliform, and E. coli), and total suspended solids (TSS). Correlation analyses were performed on the data to determine relationships between the water quality variables, whole-catchment land cover (agriculture, forest, and urban), and percent forest canopy cover within 50 m of the stream using two different stream layers (National Hydrologic Dataset (NHD), and Flow Accumulation Boundaries (FAB)). Also, riparian buffer characteristics were quantified in headwater streams to determine if they were more highly correlated with water quality variables than in larger order streams. The percent of agricultural land cover within a watershed was significantly correlated with TSS (r = 0.4556, p = 0.0021) and ammonium-N (r = 0.3043, p = 0.0473) during baseflow, and TSS (r = 0.2837, p = 0.0652), ammonium-N (r = 0.5306, p = 0.0003), nitrate-N (r = 0.2654, p = 0.0854), and orthophosphate (r = 0.3783, p = 0.0124) during stormflow. Total amount of enrolled Conservation Reserve Program (CRP) land within Richland Creek and Silver Creek watersheds were found not to be correlated with water quality. A possible reason for these results could be because only a very small percent of lands in Richland Creek and Silver Creek were enrolled in CRP. Whole-catchment land cover in most cases explained more variance than percent forest canopy cover within 50 m of streams for the water quality parameters analyzed. There were only slight differences between the two stream layers (NHD and FAB). However, the headwater streams of the FAB stream layer explained more variance in critical water quality parameters, ammonium-N (r = -0.5309, p = 0.002) during baseflow and ammonium-N (r = -0.6107 p <0.0001), and orthophosphate (r = -0.5273 p = 0.0003) during stormflow. Having an understanding of the impacts that riparian buffers and headwater streams have on water quality is key for watershed managers to focus restoration efforts in the most critical areas for maintaining stream quality.

The aim of this research was creating metrics for measuring social connectedness in social media. This thesis made use of social capital theory in order to inform the construction of original metrics. The methodology used in this thesis involved conducting a literature review into the use of social capital theory in social media, proposing new metrics, implementation in software, validation, evaluation against other measures and finally demonstrating the utility of the new metrics. A preliminary case study verified the suitability of using Facebook as a context for developing the metrics. The main practical work outlined in this thesis aimed to validate Social Capital in Social Media (SCiSM) metrics against the Internet Social Capital Scale (ISCS) (Williams 2006). The SCiSM metrics were developed to relate to bonding social capital, bridging social capital and total social capital (Putnam 2000). The methodology used to validate the SCiSM metrics was Meneely (2012) and involved using two independent data sets to validate the SCiSM metrics using both correlations and linear regression. Statistical analysis found a strong positive correlation between ISCS and SCiSM whilst regression analysis demonstrated that the relationship between SCiSM and ISCS was concerned with ranking rather than an absolute number. SCiSM was evaluated against other social capital metrics used in the literature such as degree centrality. It was found that SCiSM had a higher number of significant correlations with the ISCS than other measures. The SCiSM metrics were then used to analyse the two independent data sets in order to demonstrate their utility. The first data set, taken from a Facebook group, was analysed using a paired t-test. It was found that bonding social capital increased over a twelve week period but that bridging social capital did not. The second data set, which was taken from Facebook status updates, was analysed using correlations. The result was that there was a positive correlation between number of Facebook friends and bonding social capital. However it was also found that there was a negative correlation between number of Facebook friends and bridging social capital. This suggests that there is a dilution effect in the usefulness of large friend networks for bridging social capital. In conclusion the problem that this research has addressed is providing a means to improve understanding of social capital in social media.

Concussions occur at an unacceptable rate in the sport of ice hockey. Efforts are made to improve its prevention by modifying protective equipment and implementing rules of conduct; yet the effectiveness of these methods remains unknown as there is a lack of evidence pointing to a mechanical metric able to adequately predict concussion. The purpose of this thesis was to identify metrics which best characterize concussion following ice hockey collisions and provide values reflecting concussion risk. The first study reported effective mass of shoulder checks, extended elbow strikes, and tucked-in elbow strikes using 15 competitive ice hockey players as subjects. The results were used to guide the impact mass and compliance of laboratory reconstructions of real-world ice hockey collisions done in the second study. Analysis of these reconstruction showed that concussions following shoulder and elbow to head collisions in ice hockey occurred at low peak linear and angular accelerations and that impulse duration played a large role in the mechanism of injury. The results also indicated that concussion risk estimations are specific to the mechanism of loading. A 50% likelihood of concussion following a shoulder check to the head was established for peak angular accelerations of 9.2, 6.9, 4.6, and 2.2 krad/s^2 for impulse durations of 15, 20, 25, and 30 ms, respectively. A 50% likelihood of concussion following an extended elbow to the head was established for peak linear accelerations of 23, 15, and 7 g for impulse durations of 15, 20, and 25 ms, respectively. Finally, the third study reported brain tissue stress and strain comparable to the ones obtained reconstructing concussive impacts in American football, rugby, and Australian rules football despite having lower peak linear and angular acceleration values. This thesis has provided a new sport concussion data set acquired using a methodology guided by the biomechanics of ice hockey player volunteer testing, has identified metrics which can adequately predict concussion, and has established concussion risk levels. This information will be of use to helmet manufacturing companies, companies developing concussion detection sensors, and governing bodies in their efforts to eliminate concussion from the sport of ice hockey.

The aim of the thesis is to prove measurability of the Data Quality which is a relatively subjective measure and thus is difficult to measure. In doing this various aspects of measuring the quality of data are analyzed and a Complex Data Quality Monitoring System is introduced with the aim to provide a concept for measuring/monitoring the overall Data Quality in an organization. The system is built on a metrics hierarchy decomposed into particular detailed metrics, dimensions enabling multidimensional analyses of the metrics, and processes being measured by the metrics. The first part of the thesis (Chapter 2 and Chapter 3) is focused on dealing with Data Quality, i.e. provides various definitions of Data Quality, gives reasoning for the importance of Data Quality in a company, and presents some of the most common tools and solutions that target to managing Data Quality in an organization. The second part of the thesis (Chapter 4 and Chapter 5) builds on the previous part and leads into measuring Data Quality using metrics, i.e. contains definition and purpose of Data Quality Metrics, places them into the multidimensional context (dimensions, hierarchies) and states five possible decompositions of Data Quality metrics into detail. The third part of the thesis (Chapter 6) contains the proposed Complex Data Quality Monitoring System including description of Data Quality Management related dimensions and processes, and most importantly detailed definition of bottom-level metrics used for calculation of the overall Data Quality.

Context: Since the beginnings of software engineering, metrics (such as SLOCs) and practices have been used in an attempt to measure and improve the features of software development projects, their process, or their contributors. Measuring and enhancing software ecosystem features brings a new complexity level because a software ecosystem comprises several interrelated software projects. Over the past two decades, software ecosystems have gained considerable attention, and researchers have proposed various metrics and practices to measure and improve software ecosystems' health. Objective: This thesis presents a systematic literature review that aims to build comprehensive taxonomies for software ecosystem health metrics and practices. These taxonomies synthesize the results of previous categorizations and update them with newer metrics and practices proposed since then. This study also aims to collect and synthesize all the definitions, metrics, and practices proposed to define, measure, and improve software ecosystem health in the literature. Method: I conducted a systematic literature review and identified 40 primary studies related to defining and measuring software ecosystem health. I extracted the definitions, metrics, and practices for software ecosystem health from the primary studies, and then I categorized the metrics and practices to build the taxonomies. Results: I identified a total of 7 different definitions for software ecosystem health, 142 different metrics, and 174 various practices for software ecosystem health. Our taxonomies for software ecosystem health metrics and practices have three categories (niche creation, productivity, and robustness). Each of these categories has several sub-categories of metrics and practices. Conclusion: Software ecosystems have a wide range of stakeholders that have different perspectives regarding software ecosystem health. To satisfy this spectrum, researchers have proposed various metrics and practices to measure and improve software ecosystems' health. To improve unifying contrasting opinions, I conducted this study. The metrics and practices proposed are diverse in both purpose and the data required to compute them. Some metrics are presented along with a method on how to compute them. In contrast, others are defined abstractly without an operational approach to calculate them, and some are mentioned without a clear rationale. Furthermore, the same metric or practice is often proposed in more than one publication using different names. This thesis addresses these alignment problems. / Graduate

This work is aimed at design and implementation of system for supporting metrics in software development projects. The procedure of design and application of measurement methods is described. Further metrics data analysis in three domains is described: frequency domain, time domain and relationship domain; together with forms of visualization. Finally, the requirements for system for supporting metrics are enlisted, along with design of architecture and details of implementation.

In the field of cybersecurity, the top-level management make use of metrics to decide if the organization is doing well to protect itself from cyber attacks or is in tatters leaving itself susceptible against the vast threats looming around. Not only that but metrics are even used to measure the performance of the security team. The aim of this thesis is to show how economics is closely related to cybersecurity and how metrics play an important role in policy making of an organization. Furthermore, I scrutinize one of the leading security score providers for the way they detect botnet infection. Botnet infection is a part of compromised system group in their score card categories that amounts to 55\% of the total security score. So, it becomes essential for the security score providers to have the right method of grading a company since it will have an impact on how they use their resources to protect itself from outside threat and the insurance premium they pay to cover any successful cyber attacks. I have found out that the data on which the botnet infection vector is graded has false positives. I shed light on security analyst and security team on a whole in their role in making decisions according to the security score. It is even the duty of the security team to work ethically, that is, the aim should not be to improve the security score rather the aim should be to protect the organization from outside attacks and if it happens to increase the security rating then be it so.

Fixed and mobile communication service providers (CSPs) are facing fierce competition among each other. In a globally saturated market, the primary di↵erentiator between CSPs has become customer satisfaction, typically measured by the Net Promoter Score (NPS) for a subscriber. The NPS is the answer to the question: ”How likely is it that you will recommend this product/company to a friend or colleague?” The responses range from 0 representing not at all likely to 10 representing extremely likely. In this thesis, we aim to identify which, if any, network performance metrics contribute to subscriber satisfaction. In particular, we investigate the relationship between the NPS survey results and 11 network performance metrics of the respondents of a major mobile operator in South Africa. We identify the most influential performance metrics by fitting both linear and non-linear statistical models to the February 2018 survey dataset and test the models on the June 2018 dataset. We find that metrics such as Call Drop Rate, Call Setup Failure Rate, Call Duration and Server Setup Latency are consistently selected as significant features in models of NPS prediction. Nevertheless we find that all the tested statistical and machine learning models, whether linear or non-linear, are poor predictors of NPS scores in a month, when only the network performance metrics in the same month are provided. This suggests that either NPS is driven primarily by other factors (such as customer service interactions at branches and contact centres) or are determined by historical network performance over multiple months.

This paper describes the measurement-based software maintenance process defined and implemented at Lockheed-Martin, Moorestown, NJ. The documented process includes extensive data collection, a tightly controlled but highly accessible database, data analysis techniques supported by software tools, and process assessment and improvement activities. The methods and techniques used are presented in a 'how to' fashion so that other organizations can leverage our efforts to define and implement a measurement-based process of their own. Our approach is an evolutionary one, rather than a revolutionary organizational upheaval. We describe the benefits gained from our process, including statistically validated metric results, and the subsequent process improvements implemented. This paper describes solutions to the 'real-world' issues faced by an organization which successfully implemented a measurement-based software maintenance process.