Several hundred thousand miles of energy pipelines span the whole of North America -- responsible for carrying the natural gas and liquid petroleum that power the continent's homes and economies. These pipelines, so crucial to everyday goings-on, are closely monitored by various operating companies to ensure they perform safely and smoothly. Happenings like earthquakes, erosion, and extreme weather, however -- and human factors like vehicle traffic and construction -- all pose threats to pipeline integrity. As such, there is a tremendous need to measure and indicate useful, actionable data for each region of interest, and operators often use computer-based decision support systems (DSS) to analyze and allocate resources for active and potential hazards. We designed and implemented a geospatial data service, REST API for Pipeline Integrity Data (RAPID) to improve the amount and quality of data available to DSS. More specifically, RAPID -- built with a spatial database and the Django web framework -- allows third-party software to manage and query an arbitrary number of geographic data sources through one centralized REST API. Here, we focus on the process and peculiarities of creating RAPID's model and query interface for pipeline integrity management; this contribution describes the design, implementation, and validation of that model, which builds on existing geospatial standards.

The amount of information available on both public and private networks continues to grow at a phenomenal rate. This information is contained within a wide variety of objects, including documents, e-mail archives, medical records, manuals, pictures and music. To be of any value, this data must be easily searchable and accessible. Information Retrieval (IR) is concerned with the ability to find and gain access to relevant information. As electronic data repositories continue to proliferate, so too, grows the variety of methods used to locate and access the information contained therein. Similarly, the introduction of innovative retrieval strategies—and the optimization of older strategies—emphasizes the need for an infrastructure capable of measuring and comparing the performance of competing Information Retrieval solutions, but such an environment does not yet exist. The purpose of this research is to develop an infrastructure wherein Information Retrieval solutions may be evaluated and compared. In 1979, an expert in the field believed the need for a system-independent benchmarking utility was long overdue—twenty-five years later, progress in this area has been minimal. Contrastingly, new theories have emerged; new techniques have been introduced; all with the goal of improving retrieval performance. The need for a system-independent analysis of retrieval performance is more critical now.

The aim of this degree project is to investigate alternatives to the relational database paradigm when storing hierarchical geospatial data. The document paradigm is found suitable and is therefore further examined. A benchmark suite is developed in order to test the relative performance of the paradigms for the relevant type of data. MongoDB and Microsoft SQL Server are chosen to represent the two paradigms in the benchmark. The results indicate that the document paradigm has potential when working with hierarchical structures. When adding geospatial elements to the data, the results are inconclusive. / Det här examensarbetet ämnar undersöka alternativ till den relationella databasparadigmen för lagring av hierarkisk geospatial data. Dokumentparadigmen identiferas som särskilt lämplig och undersöks därför vidare. En benchmark-svit utvecklas för att undersöka de två paradigmens relativa prestanda vid lagring av den undersökta typen av data. MongoDB och Microsoft SQL Server väljs som representanter för de två paradigmen i benchmark-sviten. Resultaten indikerar att dokumentparadigmen har god potential för hierarkisk data. Inga tydliga slutsatser kan dock dras gällande den geospatiala aspekten.

The California state legislature introduces approximately 5,000 new bills each legislative session. While the legislative hearings are recorded on video, the recordings are not easily accessible to the public. The lack of official transcripts or summaries also increases the effort required to gain meaningful insight from those recordings. Therefore, the news media and the general population are largely oblivious to what transpires during legislative sessions. Digital Democracy, a project started by the Cal Poly Institute for Advanced Technology and Public Policy, is an online platform created to bring transparency to the California legislature. It features a searchable database of state legislative committee hearings, with each hearing accompanied by a transcript that was generated by an internal transcription tool. This thesis presents SKEWER, a pipeline for building a spoken-word knowledge graph from those transcripts. SKEWER utilizes a number of natural language processing tools to extract named entities, phrases, and sentiments from the transcript texts and aggregates the results of those tools into a graph database. The resulting graph can be queried to discover knowledge regarding the positions of legislators, lobbyists, and the general public towards specific bills or topics, and how those positions are expressed in committee hearings. Several case studies are presented to illustrate the new knowledge that can be acquired from the knowledge graph.

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This dissertation addresses issues for the efficient access to Web databases and services. We propose a distributed ontology for a meaningful organization of and efficient access to Web databases. Next, we dedicate most of our work on presenting a comprehensive query infrastructure for the emerging concept of Web services. The core of this query infrastructure is to enable the efficient delivery of Web services based on the concept of Quality of Web Service. Treating Web services as first class objects is a fundamental step towards achieving the envisioned Semantic Web. Semantics-aware processing of information requires intensive use of Web services. In our research, we propose a new query model where queries are resolved by combining Web service invocations. To efficiently deploy such scheme, we propose an optimization strategy based on aggregating Quality of Web service (QoWS) of different Web services. QoWS is adjusted through a dynamic rating scheme and multilevel matching. Web service rating provides an assessment of their behavior. The multilevel matching allows a larger solution space by enabling similar and partial answers. / Ph. D.