The Application of an Exogenous Linear and Radial Electrical Field to an In Vitro Chronic Diabetic Ulcer Model for Evaluation as a Potential Treatment

Chronic diabetic ulcers affect approximately 15% of patients with diabetes worldwide. Currently, applied electric fields are being investigated as a reliable and cost-effective treatment. This in vitro study aimed to determine the effects of a constant and spatially variable electric field on three factors: endothelial cell migration, proliferation, and angiogenic gene expression. Results for a constant electric field of 0.01 V demonstrated that migration at short time points increased 20-fold and proliferation at long time points increased by a factor of 1.40. Results for a spatially variable electric field did not increase directional migration, but increased proliferation by a factor of 1.39 and by a factor of 1.55 after application of 1.00 V and 0.01 V, respectively. Both constant and spatially variable applied fields increased angiogenic gene expression. Future research that explores a narrower range of intensity levels may more clearly identify the optimal design specifications of a spatially variable electric field.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

WORKPLACE HAPPINESS: ORGANIZATIONAL ROLE AND THE RELIABILITY OF SELF-REPORTING

Happier employees are more productive. Organizations across industry, no doubt, try to improve their employees’ happiness with the objective to achieve higher profitability and company value. While this issue has drawn increasing attention in high tech and other industries, little is known about the happiness of project management professionals. More research is needed to explore the current situation of workplace happiness of project management professionals and the driving factors behind it. This thesis explores the workplace happiness (subjective well-being) of project management professionals based on the exploratory statistical analysis of a survey 225 professionals in the state of Maryland, conducted in October 2014. The thesis applies Structural Equation Modeling and multiple regression analysis to the dataset and shows no significant impact of gender, age, work experience, and some other demographic traits on workplace happiness, also named well-being. Statistically significant factors for workplace happiness include: creating pleasant work environment, promoting open organization and well-managed team, and good organization to work for. With respect to the reliability of self-reporting, the study finds that the comprehensive appraisal tool designed by Happiness Works and New Economics Foundation can give a more reliable happiness evaluation. Two key factors, i.e. career perspectives and free to be self, can help alleviate the overconfidence of workplace happiness.