RDA and Linked Data: A New Way to Look at the Henson Collection

In 2005, the University of Maryland acquired over 70 digital videos spanning 35 years of Jim Henson’s groundbreaking work in television and film. To support in-house discovery and use, the collection was cataloged in detail using AACR2 and MARC21, and a web-based finding aid was also created. In the past year, I created an "r-ball" (a linked data set described using RDA) of these same resources. The presentation will compare and contrast these three ways of accessing the Jim Henson Works collection, with insights gleaned from providing resource discovery using RIMMF (RDA in Many Metadata Formats).

RISKS TO FOOD AVAILABILITY AND ACCESS FROM CLIMATE POLICIES: AN INTEGRATED ASSESSMENT OF REGIONAL FOOD AVAILABILITY AND ACCESS WITH ALTERNATIVE CLIMATE MITIGATION STRATEGIES TO 2050

Although mitigating GHG emissions is necessary to reduce the overall negative climate change impacts on crop yields and agricultural production, certain mitigation measures may generate unintended consequences to food availability and access due to land use competition and economic burden of mitigation. Prior studies have examined the co-impacts on food availability and global producer prices caused by alternative climate policies. More recent studies have looked at the reduction in total caloric intake driven by both changing income and changing food prices under one specific climate policy. However, due to inelastic calorie demand, consumers’ well-being are likely further reduced by increased food expenditures. Built upon existing literature, my dissertation explores how alternative climate policy designs might adversely affect both caloric intake and staple food budget share to 2050, by using the Global Change Assessment Model (GCAM) and a post-estimated metric of food availability and access (FAA). My dissertation first develop a set of new metrics and methods to explore new perspectives of food availability and access under new conditions. The FAA metric consists of two components, the fraction of GDP per capita spent on five categories of staple food and total caloric intake relative to a reference level. By testing the metric against alternate expectations of the future, it shows consistent results with previous studies that economic growth dominates the improvement of FAA. As we increase our ambition to achieve stringent climate targets, two policy conditions tend to have large impacts on FAA driven by competing land use and increasing food prices. Strict conservation policies leave the competition between bioenergy and agriculture production on existing commercial land, while pricing terrestrial carbon encourages large-scale afforestation. To avoid unintended outcomes to food availability and access for the poor, pricing land emissions in frontier forests has the advantage of selecting more productive land for agricultural activities compared to the full conservation approach, but the land carbon price should not be linked to the price of energy system emissions. These results are highly relevant to effective policy-making to reduce land use change emissions, such as the Reduced Emissions from Deforestation and Forest Degradation (REDD).

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.

Creating Common Ground: Architecture For Tactical Learning and Creative Convergence

Certain environments can inhibit learning and stifle enthusiasm, while others enhance learning or stimulate curiosity. Furthermore, in a world where technological change is accelerating we could ask how might architecture connect resource abundant and resource scarce innovation environments? Innovation environments developed out of necessity within urban villages and those developed with high intention and expectation within more institutionalized settings share a framework of opportunity for addressing change through learning and education. This thesis investigates formal and informal learning environments and how architecture can stimulate curiosity, enrich learning, create common ground, and expand access to education. The reason for this thesis exploration is to better understand how architects might design inclusive environments that bring people together to build sustainable infrastructure encouraging innovation and adaptation to change for years to come. The context of this thesis is largely based on Colin McFarlane’s theory that the “city is an assemblage for learning” The socio-spatial perspective in urbanism, considers how built infrastructure and society interact. Through the urban realm, inhabitants learn to negotiate people, space, politics, and resources affecting their daily lives. The city is therefore a dynamic field of emergent possibility. This thesis uses the city as a lens through which the boundaries between informal and formal logics as well as the public and private might be blurred. Through analytical processes I have examined the environmental devices and assemblage of factors that consistently provide conditions through which learning may thrive. These parameters that make a creative space significant can help suggest the design of common ground environments through which innovation is catalyzed.