Improving the use of asset-building financial services by Latino immigrants in Langley Park, Maryland

Gemstone Team ANSWER Poverty (Assessing the Need for Services Which Effectively Reduce Poverty)

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.

REDUCING TEACHER ATTRITION: THE ROLES AND RESPONSIBILITIES OF BUILDING PRINCIPALS IN A LARGE URBAN DISTRICT TO SUPPORT TEACHER RETENTION

The high rate of teacher attrition in urban schools is well documented. While this does not seem like a problem in Carter County, this equates to hundreds of teachers that need to be replaced annually. Since school year (SY) 2007-08, Carter County has lost over 7,100 teachers, approximately half of (50.1%) of whom resigned, often going to neighboring, higher-paying jurisdictions as suggested by exit survey data (SY2016-2020 Strategic Plan). Included in this study is a range of practices principals use to retain teachers. While the role of the principal is recognized as a critical element in teacher retention, few studies explore the specific practices principals implement to retain teachers and how they use their time to accomplish this task. Through interviews, observations, document analysis and reflective notes, the study identifies the practices four elementary school principals of high and relatively low attrition schools use to support teacher retention. In doing so, the study uses a qualitative cross-case analysis approach. The researcher examined the following leadership practices of the principal and their impact on teacher retention: (a) providing leadership, (b) supporting new teachers, (c) training and mentoring teaching staff, (d) creating opportunities for collaboration, (d) creating a positive school climate, and (e) promoting teacher autonomy. The following research questions served as a foundational guide for the development and implementation of this study: 1. How do principals prioritize addressing teacher attrition or retention relative to all of their other responsibilities? How do they allocate their time to this challenge? 2. What do principals in schools with low attrition rates do to promote retention that principals in high attrition schools do not? What specific practices or interventions are principals in these two types of schools utilizing to retain teachers? Is there evidence to support their use of the practices? The findings that emerge from the data revealed the various practices principals use to influence and support teachers do not differ between the four schools.