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In this thesis we present the holistic query evaluation model. We propose a novel query engine design that exploits the characteristics of modern processors when queries execute inside main memory. The holistic model (a) is based on template-based code generation for each executed query, (b) uses multithreading to adapt to multicore processor architectures and (c) addresses the optimization problem of scheduling multiple threads for intra-query parallelism. Main-memory query execution is a usual operation in modern database servers equipped with tens or hundreds of gigabytes of RAM. In such an execution environment, the query engine needs to adapt to the CPU characteristics to boost performance. For this purpose, holistic query evaluation applies customized code generation to database query evaluation. The idea is to use a collection of highly efficient code templates and dynamically instantiate them to create query- and hardware-specific source code. The source code is compiled and dynamically linked to the database server for processing. Code generation diminishes the bloat of higher-level programming abstractions necessary for implementing generic, interpreted, SQL query engines. At the same time, the generated code is customized for the hardware it will run on. The holistic model supports the most frequently used query processing algorithms, namely sorting, partitioning, join evaluation, and aggregation, thus allowing the efficient evaluation of complex DSS or OLAP queries. Modern CPUs follow multicore designs with multiple threads running in parallel. The dataflow of query engine algorithms needs to be adapted to exploit such designs. We identify memory accesses and thread synchronization as the main bottlenecks in a multicore execution environment. We extend the holistic query evaluation model and propose techniques to mitigate the impact of these bottlenecks on multithreaded query evaluation. We analytically model the expected performance and scalability of the proposed algorithms according to the hardware specifications. The analytical performance expressions can be used by the optimizer to statically estimate the speedup of multithreaded query execution. Finally, we examine the problem of thread scheduling in the context of multithreaded query evaluation on multicore CPUs. The search space for possible operator execution schedules scales fast, thus forbidding the use of exhaustive techniques. We model intra-query parallelism on multicore systems and present scheduling heuristics that result in different degrees of schedule quality and optimization cost. We identify cases where each of our proposed algorithms, or combinations of them, are expected to generate schedules of high quality at an acceptable running cost.

This thesis sets out to answer three simple questions: What tools are available for novice programmers to program GUIs? Are those tools fulfilling their role? Can anything be done to make better tools? Despite being simple questions, the answers are not so easily constructed. In answering the first question, it was necessary to examine the range of tools available and decide upon criteria which could be used to identify tools aimed specifically at the novice programmer (there being no currently agreed criteria for their identification). Having identified these tools, it was then necessary to construct a framework within which they could be sensibly compared. The answering of the second question required an investigation of what were the successful features of current tools and which features were less successful. Success or failure of given features was determined by research in both programming language design and studies of programmer satisfaction. Having discovered what should be retained and discarded from current systems, the answering of the third question required the construction of new systems through blending elements from visual languages, program editors and fourth generation languages. These final prototypes illustrate a new way of thinking about and constructing the next generation of GUI programming languages for the novice.

BACKGROUND:The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this 'names problem' has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science.RESULTS:The TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets.CONCLUSIONS:We show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ webcite and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/ webcite.

Approved for public release; distribution is unlimited / The purpose of this thesis is to develop a prototype web-enabled database to improve the process flow of data collection and manipulation in support of Army aviation operations. Data collection is focused around routine aviation operations and aviation maintenance with the intention of identifying a feasible replacement for the existing redundant manual and automated collection procedures. The web interface has the potential to reduce the logistical burden on unit's data collection procedures and provides tailorable, near real time information about aircraft maintenance status, individual training, and unit training to decision makers at all levels as a decision support tool. This thesis will describe the design considerations for a web-enabled database to include the development of detailed data and process models. / Major, United States Army

Query processing is a core task in probabilistic databases: Given a query and a database that encodes uncertainty in data by means of probability distributions, the problem is to compute possible query answers together with their respective probabilities of being correct. This thesis advances the state of the art in two aspects of query processing in probabilistic databases: complexity analysis and query evaluation techniques. A dichotomy is established for non-repeating, con- junctive relational algebra queries with negation that separates #P-hard queries from those with PTIME data complexity. A framework for computing proba- bilities of relational algebra queries is presented; the probability computation algorithm is based on decomposition methods and provides exact answers in the case of exhaustive decompositions, or anytime approximate answers with absolute or relative error guarantees in the case of partial decompositions. The framework is extended to queries with aggregation operators. An experimental evaluation of the proposed algorithms’ implementations within the SPROUT query engine complements the theoretical results. The SPROUT² system uses this query engine to compute answers to queries on uncertain, tabular Web data.

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Since the 1960s, the Petersburg Area Art League (PAAL) has obtained works of art for its permanent collection through purchases, private donations, and through the local art show, the Poplar Lawn Art Festival, later known as Artfest. Recently, however, the organization has decided to become a non-collecting institution in order to focus on its mission to promote the arts in Petersburg through gallery shows for local artists and educational programs. While PAAL’s staff members share a love of art and a dedication to the local community, they have not been trained in professional standards for handling museum collections as outlined by the American Association of Museums (AAM). Consequently, the PAAL collection had not been adequately documented or stored in a manner that protected the works from potential damage or degradation. This museums project was designed to help the Petersburg Area Art League meet AAM standards. During the summer of 2010, the collection of 150 artworks was accessioned; its storage facility was reorganized; a database was created; and a collections management policy that would ensure the continued care of the collection after the completion of this project was written and approved. This paper describes challenges encountered and resolved during the two-month project and provides a reference for those who wish to take on similar projects in the future.