Databases and Information Systems Commons^™

Ums Data Governance Annual Report 2018, University Of Maine System Data Advisory Committee

General University of Maine Publications

The newly formed University of Maine System Data Governance program was launched to improve the System's capacity to collect information and deploy resources in service to the students and in response to Maine's dire demographic and workforce challenges.

The UMS campuses and administrative units make up a complex system that requires a strategic approach to data collection and analysis. From understanding the intricacies of distance education and online programs, to aligning human resource and financial department codes, unified data governance is essential to ensuring the integrity and reliability of the University of Maine System's data.

Principles And Guidelines For Advancement Of Touchscreen-Based Non-Visual Access To 2d Spatial Information, Hari Prasath Palani

Electronic Theses and Dissertations

Graphical materials such as graphs and maps are often inaccessible to millions of blind and visually-impaired (BVI) people, which negatively impacts their educational prospects, ability to travel, and vocational opportunities. To address this longstanding issue, a three-phase research program was conducted that builds on and extends previous work establishing touchscreen-based haptic cuing as a viable alternative for conveying digital graphics to BVI users. Although promising, this approach poses unique challenges that can only be addressed by schematizing the underlying graphical information based on perceptual and spatio-cognitive characteristics pertinent to touchscreen-based haptic access. Towards this end, this dissertation empirically identified a …

Towards An Efficient, Scalable Stream Query Operator Framework For Representing And Analyzing Continuous Fields, John Whittier

Electronic Theses and Dissertations

Advancements in sensor technology have made it less expensive to deploy massive numbers of sensors to observe continuous geographic phenomena at high sample rates and stream live sensor observations. This fact has raised new challenges since sensor streams have pushed the limits of traditional geo-sensor data management technology. Data Stream Engines (DSEs) provide facilities for near real-time processing of streams, however, algorithms supporting representing and analyzing Spatio-Temporal (ST) phenomena are limited.

This dissertation investigates near real-time representation and analysis of continuous ST phenomena, observed by large numbers of mobile, asynchronously sampling sensors, using a DSE and proposes two novel stream …