Infrastructure systems modeling using data visualization and trend extraction

“Current infrastructure systems modeling literature lacks frameworks that integrate data visualization and trend extraction needed for complex systems decision making and planning. Critical infrastructures such as transportation and energy systems contain interdependencies that cannot be properly characterized without considering data visualization and trend extraction. This dissertation presents two case analyses to showcase the effectiveness and improvements that can be made using these techniques. Case one examines flood management and mitigation of disruption impacts using geospatial characteristics as part of data visualization. Case two incorporates trend analysis and sustainability assessment into energy portfolio transitions. Four distinct contributions are made in this work and divided equally across the two cases. The first contribution identifies trends and flood characteristics that must be included as part of model development. The second contribution uses trend extraction to create a traffic management data visualization system based on the flood influencing factors identified. The third contribution creates a data visualization framework for energy portfolio analysis using a genetic algorithm and fuzzy logic. The fourth contribution develops a sustainability assessment model using trend extraction and time series forecasting of state-level electricity generation in a proposed transition setting. The data visualization and trend extraction tools developed and validated in this research will improve strategic infrastructure planning effectiveness”--Abstract, page iv

MindSeer: a portable and extensible tool for visualization of structural and functional neuroimaging data

Three-dimensional (3-D) visualization of multimodality neuroimaging data provides a powerful technique for viewing the relationship between structure and function. A number of applications are available that include some aspect of 3-D visualization, including both free and commercial products. These applications range from highly specific programs for a single modality, to general purpose toolkits that include many image processing functions in addition to visualization. However, few if any of these combine both stand-alone and remote multi-modality visualization in an open source, portable and extensible tool that is easy to install and use, yet can be included as a component of a larger information system. We have developed a new open source multimodality 3-D visualization application, called MindSeer, that has these features: integrated and interactive 3-D volume and surface visualization, Java and Java3D for true cross-platform portability, one-click installation and startup, integrated data management to help organize large studies, extensibility through plugins, transparent remote visualization, and the ability to be integrated into larger information management systems. We describe the design and implementation of the system, as well as several case studies that demonstrate its utility. These case studies are available as tutorials or demos on the associated website: http://sig.biostr.washington.edu/projects/MindSeer MindSeer provides a powerful visualization tool for multimodality neuroimaging data. Its architecture and unique features also allow it to be extended into other visualization domains within biomedicine

Visualization of Network Data Provenance

Visualization facilitates the understanding of scientific data both through exploration and explanation of the visualized data. Provenance also contributes to the understanding of data by containing the contributing factors behind a result. The visualization of provenance, although supported in existing workflow management systems, generally focuses on small (medium) sized provenance data, lacking techniques to deal with big data with high complexity. This paper discusses visualization techniques developed for exploration and explanation of provenance, including layout algorithm, visual style, graph abstraction techniques, and graph matching algorithm, to deal with the high complexity. We demonstrate through application to two extensively analyzed case studies that involved provenance capture and use over three year projects, the first involving provenance of a satellite imagery ingest processing pipeline and the other of provenance in a large-scale computer network testbed

Environmental science applications with Rapid Integrated Mapping and analysis System (RIMS)

The Rapid Integrated Mapping and analysis System (RIMS) has been developed at the University of New Hampshire as an online instrument for multidisciplinary data visualization, analysis and manipulation with a focus on hydrological applications. Recently it was enriched with data and tools to allow more sophisticated analysis of interdisciplinary data. Three different examples of specific scientific applications with RIMS are demonstrated and discussed. Analysis of historical changes in major components of the Eurasian pan-Arctic water budget is based on historical discharge data, gridded observational meteorological fields, and remote sensing data for sea ice area. Express analysis of the extremely hot and dry summer of 2010 across European Russia is performed using a combination of near-real time and historical data to evaluate the intensity and spatial distribution of this event and its socioeconomic impacts. Integrative analysis of hydrological, water management, and population data for Central Asia over the last 30 years provides an assessment of regional water security due to changes in climate, water use and demography. The presented case studies demonstrate the capabilities of RIMS as a powerful instrument for hydrological and coupled human-natural systems research

A Visual Analytics Framework for Reviewing Streaming Performance Data

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.Comment: This is the author's preprint version that will be published in Proceedings of IEEE Pacific Visualization Symposium, 202

Integrated 3D Bridge-Condition Visualization (BCV) to Facilitate Element-Based Bridge Condition Rating (EBCR)

Routine inspection and maintenance records are essential for bridges to function well throughout their intended lifespan. Although existing bridge management systems are efficient at data storage, it is difficult to conduct comprehensive data analysis and management due to the lack of data integration mechanism. Engineers have to manually put many pieces of bridge drawings and inspection data together to make maintenance or repair/rehab decisions. Building information modeling (BIM) can be applied in bridge asset management area, including bridge inspection/rating to help to integrate the many data pieces. In this project we developed a 3D bridge inspection data management system, using I-680 Mormon Bridge as a case, to integrate 3D visualization with bridge inspection and maintenance records for visualized data analysis and active data management. This system can be used in managing inspection data in other bridges, and other infrastructures with only minor modifications

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining

Background New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. Results We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. Conclusion The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies

Supporting Project Comprehension with Revision Control System Repository Analysis

Context: Project comprehension is an activity relevant to all aspects of software engineering, from requirements specification to maintenance. The historical, transactional data stored in revision control systems can be mined and analysed to produce a great deal of information about a project. Aims: This research aims to explore how the data-mining, analysis and presentation of revision control systems can be used to augment aspects of project comprehension, including change prediction, maintenance, visualization, management, profiling, sampling and assessment. Method: A series of case studies investigate how transactional data can be used to support project comprehension. A thematic analysis of revision logs is used to explore the development process and developer behaviour. A benchmarking study of a history-based model of change prediction is conducted to assess how successfully such a technique can be used to augment syntax-based models. A visualization tool is developed for managers of student projects with the aim of evaluating what visualizations best support their roles. Finally, a quasi-experiment is conducted to determine how well an algorithmic model can automatically select a representative sample of code entities from a project, in comparison with expert strategies. Results: The thematic analysis case study classified maintenance activities in 22 undergraduate projects and four real-world projects. The change prediction study calculated information retrieval metrics for 34 undergraduate projects and three real-world projects, as well as an in-depth exploration of the model's performance and applications in two selected projects. File samples for seven projects were generated by six experts and three heuristic models and compared to assess agreement rates, both within the experts and between the experts and the models. Conclusions: When the results from each study are evaluated together, the evidence strongly shows that the information stored in revision control systems can indeed be used to support a range of project comprehension activities in a manner which complements existing, syntax-based techniques. The case studies also help to develop the empirical foundation of repository analysis in the areas of visualization, maintenance, sampling, profiling and management; the research also shows that students can be viable substitutes for industrial practitioners in certain areas of software engineering research, which weakens one of the primary obstacles to empirical studies in these areas

A Methodology for a Performance Information Model to support Facility Management

Current facility management practice relies on different systems which require new technologies to integrate and manage information more easily. Building information modeling offers a good opportunity to improve facility information management by providing a unified platform for various data sources rather than an intuitive information interface. Although current research trends reveal that there is a continuously growing interest in facility management aided by building information modeling, an integrated model is still hard to obtain. This paper aims at developing a novel methodology based on building information modeling and facility management systems integration, underpinned by a performance information model. The implementation process of a performance information model is described, including information technologies involved, the data and process requirements, and the building performance assessment methods used. A first pilot case-study has been conducted with regards to surgery rooms in healthcare buildings. The proposal can support condition-based maintenance work schedule, as well as the achievement of organizational, environmental, and technical requirements. Among the practical implications found: Improved technological and environmental performances assessment; better visualization of building condition; improved decision-making process; facilitated maintenance tasks planning and maintenance records management