"Change is inevitable even for persistent information. Effectively managing change of persistent information, which includes the specification, execution and the maintenance of any derived information, is critical and must be addressed by all database systems. Today, for every data model there exists a well-defined set of change primitives that can alter both the structure (the schema) and the data. Several proposals also exist for incrementally propagating a primitive change to any derived information (or view). However, existing support is lacking in two ways. First, change primitives as presented in literature are very limiting in terms of their capabilities allowing users to simply add or remove schema elements. More complex types of changes such the merging or splitting of schema elements are not supported in a principled manner. Second, algorithms for maintaining derived information often do not account for the potential heterogeneity between the source and the target. The goal of this dissertation is to provide solutions that address these two key issues. The first part of this dissertation addresses the challenge of expressing a rich complex set of changes. We propose the SERF (Schema Evolution through an Extensible, Re-usable and Flexible) framework that allows users to perform a wide range of complex user-defined schema transformations. Our approach combines existing schema evolution primitives using OQL (object query language) as the glue logic. Within the context of this work, we look at the different domains in which SERF can be applied, including web site management. To further enrich our framework, we also investigate the optimization and verification of SERF transformations. The second part of this dissertation addresses the problem of maintaining views in the face of source changes when the source and the view are not in the same data model. With today's increasing heterogeneity in information structure, it is critical that maintenance of views addresses the data model boundaries. However, view definitions that go across data models are limited to hard-coded algorithms, thereby making it difficult to develop general maintenance algorithms. We provide a two-step solution for this problem. We have developed a cross algebra, that defines views such that there is no restriction that forces the view and the source data models to be the same. We then define update propagation algorithms that can propagate changes from source to target irrespective of the exact translation and the data models. We validate our ideas by applying them to translation and change propagation between the XML and relational data models."

<p>This dissertation investigates the influence of spatially explicit animal behavior active subsidy distribution patterns. Active subsidies are animal-transported consumption and resources transfers from donor to recipient ecosystems. Active subsidies influence ecosystem structure, function and services in recipient ecosystems. Even though active subsidies affect ecosystem dynamics, most ecosystem models consider the influence of spatially-explicit animal behavior on active subsidy distributions, limiting the ability to predict corresponding spatial impacts across ecosystems. Spatial subsidy research documents the need for systematic models and analyses frameworks to provide generally insights into the relationship between animal space use behavior and active subsidy patterns, and advance knowledge of corresponding ecosystem impacts for a variety of taxa and ecological scenarios.</p> <p> </p> <p>To advance spatial subsidy research, this dissertation employs a combined individual-based and movement ecology approach in abstract modeling frameworks to systematically investigate the influence of 1) animal movement behavior given mortality (chapter 2), 2) animal sociality (chapter 3) and 3) landscape heterogeneity (chapter 4) on active subsidy distribution. This dissertation shows that animal movement behavior, sociality and landscape heterogeneity influence the extent and intensity of active distribution and impacts in recipient ecosystems. Insights from this dissertation demonstrate that accounting for these factors in the development of ecosystem models will consequentially enhance their utility for predicting active subsidy spatial patterns and impacts. This dissertation advances spatial subsidy research by providing a road map for developing a comprehensive, unifying framework of the relationship between animal behavior and active subsidy distributions.</p>

In this dissertation research we study and address the unique challenges involved in information sharing and dissemination of large-scale group communication applications. We focus on system architectures and various techniques for efficient and scalable information dissemination in distributed P2P environments. Our solutions are developed by targeting at utilizing three representative P2P overlay networks: structured P2P network based on consistent hashing techniques, unstructured Gnutella-like P2P network, and P2P GeoGrid based on geographical location and proximity of end nodes. We have made three unique contributions to the general field of large-scale information sharing and dissemination. First, we propose a landmark-based peer clustering techniques to grouping end-system nodes by their network proximity, and a communication management technique addresses load balancing and reliability of group communication applications in structured P2P network. Second, we develop a utility-based P2P group communication service middleware, consisting of a utility-based topology management and a utility-aware P2P routing, for providing scalable and efficient group communication services in an unstructured P2P overlay network of heterogeneous peers. Third, we propose an overlay network management protocol that is aware of the geographical location of end-system nodes and a set of routing and adaptation techniques, aiming at building decentralized information dissemination service networks to support location-based applications and services. Although different overlay networks require different system designs for building scalable and efficient information dissemination services, we have employed two common design philosophies: (1) exploiting end-system heterogeneity and (2) utilizing proximity information of end-system nodes to localize most of the communication traffic, and (3) using randomized shortcuts to accelerate long-distant communications. We have demonstrated our design philosophies and the performance improvements in the above three types of P2P overlay networks. Concretely, by assigning more workloads to more powerful peers, we can greatly increase the system scalability and reduce the variation of workload distribution. By clustering end-system nodes based on their IP-network proximity or their geographical proximity, and utilizing randomized shortcuts, we can reduce the end-to-end communication latency, balance peer workloads against service request hotspots across the overlay network, and significantly enhance the scalability and efficiency of large-scale decentralized information dissemination and group communication.