More "normal" than normal: scaling distributions and complex systems

One feature of many naturally occurring or engineered complex systems is tremendous variability in event sizes. To account for it, the behavior of these systems is often described using power law relationships or scaling distributions, which tend to be viewed as "exotic" because of their unusual properties (e.g., infinite moments). An alternate view is based on mathematical, statistical, and data-analytic arguments and suggests that scaling distributions should be viewed as "more normal than normal". In support of this latter view that has been advocated by Mandelbrot for the last 40 years, we review in this paper some relevant results from probability theory and illustrate a powerful statistical approach for deciding whether the variability associated with observed event sizes is consistent with an underlying Gaussian-type (finite variance) or scaling-type (infinite variance) distribution. We contrast this approach with traditional model fitting techniques and discuss its implications for future modeling of complex systems

Enabling near-term prediction of status for intelligent transportation systems: Management techniques for data on mobile objects

Location Dependent Queries (LDQs) benefit from the rapid advances in communication and Global Positioning System (GPS) technologies to track moving objects\u27 locations, and improve the quality-of-life by providing location relevant services and information to end users. The enormity of the underlying data maintained by LDQ applications - a large quantity of mobile objects and their frequent mobility - is, however, a major obstacle in providing effective and efficient services. Motivated by this obstacle, this thesis sets out in the quest to find improved methods to efficiently index, access, retrieve, and update volatile LDQ related mobile object data and information. Challenges and research issues are discussed in detail, and solutions are presented and examined. --Abstract, page iii

DEVELOPING AN EXPANDABLE GUI TOOL TO ENHANCE NETWORKING EDUCATION: GRAPHICAL USER INTERFACE FOR SHELL ENTRY (GUISE)

The lack of systemic education dedicated to computer networks and the general inadequacy of students' comprehension of the structure and the dynamics of the networks are arguably issues in most public schools. In the situation where the internet is a commodity, the increase in the threat of global attacks on many computing resources is exceptionally high, and so is the consequential importance of the global cyber workforce. Most people achieve their basic understanding through their routine use of computers at home, and it is both pragmatic and more effective to consider using basic home tools because of their didactic benefits. We designed and developed GUISE (GUI for Shell Entry) as an intuitive interface that makes command entry and network analysis easier for masses of users. GUISE leverages the operating system's capabilities and allows inexperienced users with no expertise in the computer networking domain to acquire enhanced network situational awareness in a competent manner. The ultimate benefit of the GUISE tool is providing its users with an educational aspect focused on the networking elements of their home computer infrastructure. That approach has the potential to directly support the growth of their networking literacy and proficiency, and their navigation of the networking landscape with enhanced confidence and safety.Outstanding ThesisCivilian, SFSApproved for public release. Distribution is unlimited

Data-driven approach for highlighting priority areas for protection in marine areas beyond national jurisdiction

One of the aims of the United Nations (UN) negotiations on the conservation and sustainable use of marine biodiversity in areas beyond national jurisdiction (ABNJ) is to develop a legal process for the establishment of area-based management tools, including marine protected areas, in ABNJ. Here we use a conservation planning algorithm to integrate 55 global data layers on ABNJ species diversity, habitat heterogeneity, benthic features, productivity, and fishing as a means for highlighting priority regions in ABNJ to be considered for spatial protection. We also include information on forecasted species distributions under climate change. We found that parameterizing the planning algorithm to protect at least 30% of these key ABNJ conservation features, while avoiding areas of high fishing effort, yielded a solution that highlights 52,545,634 km2 (23.7%) of ABNJ as high priority regions for protection. Instructing the planning model to avoid ABNJ areas with high fishing effort resulted in relatively minor shifts in the planning solution, when compared to a separate model that did not consider fishing effort. Integrating information on climate change had a similarly minor influence on the planning solution, suggesting that climate-informed ABNJ protected areas may be able to protect biodiversity now and in the future. This globally standardized, data-driven process for identifying priority ABNJ regions for protection serves as a valuable complement to other expert-driven processes underway to highlight ecologically or biologically significant ABNJ regions. Both the outputs and methods exhibited in this analysis can additively inform UN decision-making concerning establishment of ABNJ protected areas

Data-driven approach for highlighting priority areas for protection in marine areas beyond national jurisdiction

One of the aims of the United Nations (UN) negotiations on the conservation and sustainable use of marine biodiversity in areas beyond national jurisdiction (ABNJ) is to develop a legal process for the establishment of area-based management tools, including marine protected areas, in ABNJ. Here we use a conservation planning algorithm to integrate 55 global data layers on ABNJ species diversity, habitat heterogeneity, benthic features, productivity, and fishing as a means for highlighting priority regions in ABNJ to be considered for spatial protection. We also include information on forecasted species distributions under climate change. We found that parameterizing the planning algorithm to protect at least 30% of these key ABNJ conservation features, while avoiding areas of high fishing effort, yielded a solution that highlights 52,545,634 km2 (23.7%) of ABNJ as high priority regions for protection. Instructing the planning model to avoid ABNJ areas with high fishing effort resulted in relatively minor shifts in the planning solution, when compared to a separate model that did not consider fishing effort. Integrating information on climate change had a similarly minor influence on the planning solution, suggesting that climate-informed ABNJ protected areas may be able to protect biodiversity now and in the future. This globally standardized, data-driven process for identifying priority ABNJ regions for protection serves as a valuable complement to other expert-driven processes underway to highlight ecologically or biologically significant ABNJ regions. Both the outputs and methods exhibited in this analysis can additively inform UN decision-making concerning establishment of ABNJ protected areas