Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.

Three essays on hospital efficiency

This dissertation analyzes hospital efficiency using various econometric techniques. The first essay provides additional and recent evidence to the presence of contract management behavior in the U.S. hospital industry. Unlike previous studies, which focus on either an input-demand equation or the cost function of the firm, this paper estimates the two jointly using a system of nonlinear equations. Moreover, it addresses the longitudinal problem of institutions adopting contract management in different years, by creating a matched control group of non-adopters with the same longitudinal distribution as the group under study. The estimation procedure then finds that labor, and not capital, is the preferred input in U.S. hospitals regardless of managerial contract status. With institutions that adopt contract management benefiting from lower labor inefficiencies than the simulated non-contract adopters. These results suggest that while there is a propensity for expense preference behavior towards the labor input, contract managed firms are able to introduce efficiencies over conventional, owner controlled, firms. Using data for the years 1998 through 2007, the second essay investigates the production technology and cost efficiency faced by Florida hospitals. A stochastic frontier multiproduct cost function is estimated in order to test for economies of scale, economies of scope, and relative cost efficiencies. The results suggest that small-sized hospitals experience economies of scale, while large and medium sized institutions do not. The empirical findings show that Florida hospitals enjoy significant scope economies, regardless of size. Lastly, the evidence suggests that there is a link between hospital size and relative cost efficiency. The results of the study imply that state policy makers should be focused on increasing hospital scale for smaller institutions while facilitating the expansion of multiproduct production for larger hospitals. The third and final essay employs a two staged approach in analyzing the efficiency of hospitals in the state of Florida. In the first stage, the Banker, Charnes, and Cooper model of Data Envelopment Analysis is employed in order to derive overall technical efficiency scores for each non-specialty hospital in the state. Additionally, input slacks are calculated and reported in order to identify the factors of production that each hospital may be over utilizing. In the second stage, we employ a Tobit regression model in order to analyze the effects a number of structural, managerial, and environmental factors may have on a hospital’s efficiency. The results indicated that most non-specialty hospitals in the state are operating away from the efficient production frontier. The results also indicate that the structural make up, managerial choices, and level of competition Florida hospitals face have an impact on their overall technical efficiency.

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

Questionnaire Construction

Questionnaires used in survey research can elicit excellent data for analysis for any part of the industry. The author discusses how to design questions, construct the survey, and watch for errors in conducting the re- search so that the results secured advance scientific inquiry.

Does U.S counter-drug policy affect nationalism in the Anglophone Caribbean? A comparative study on the impact of counter-drug policy on nationalism in Jamaica and Trinidad and Tobago

This dissertation examined the effect of United States counter-drug policy on nationalism in small states, focusing on Jamaica and Trinidad and Tobago. The states were selected for their roles and geostrategic importance in the illegal drug trade; Jamaica being the largest drug producing country in the Anglophone Caribbean and having strong links to the trade of Colombian cocaine, and Trinidad being a mere seven miles from the South American coast. Since U.S. counterdrug policies have frequently been viewed in the region as imperialistic, this dovetails into ideas on the perceptions of smallness and powerlessness of Caribbean nations. Hence, U.S. drug policies affect every vulnerability faced by the Caribbean, individually and collectively. Thus, U.S. drug policy was deemed the most appropriate independent variable, with nationalism as the dependent variable. In both countries four Focus Groups and one Delphi Study were conducted resulting in a total of 60 participants. Focus Group participants, recruited from the general population, were asked about their perception of the illegal drug trade in the country and the policies their government had created. They were also asked their perception on how deeply involved the U.S. was in the creation of these policies and their opinions on whether this involvement was positive or negative. The Delphi Study participants were experts in the field of local drug policies and also gave their interpretations of the role the U.S. played in local policy creation. Coupled with this data, content analysis was conducted on various newspaper articles, press releases, and speeches made regarding the topic. In comparing both countries, it was found that there is a disconnect between government actions and the knowledge and perceptions of the general public. In Trinidad and Tobago this disconnect was more apparent given the lack of awareness of local drug policies and the utter lack of faith in government solutions. The emerging conclusion was that the impact of U.S. drug policy on nationalism was more visible in Trinidad and Tobago where there was a weaker civil society-government relationship, while the impact on nationalism was more obscure in Jamaica, which had a stronger civil-society government relationship.