A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item TOWARDS EFFICIENT PRESENTATION AND INTERACTION IN VISUAL DATA ANALYSIS(2019) Cui, Zhe; JaJa, Joseph; Elmqvist, Niklas; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The "data explosion'' since the era of the Internet has increased data size tremendously, from several hundred Megabytes to millions of Terabytes. Large amounts of data may not fit into memory, and a proper way of handling and processing the data is necessary. Besides, analyses of such large scale data requires complex and time consuming algorithms. On the other hand, humans play an important role in steering and driving the data analysis, while there are often times when people have a hard time getting an overview of the data or knowing which analysis to run. Sometimes they may not even know where to start. There is a huge gap between the data and understanding. An intuitive way to facilitate data analysis is to visualize it. Visualization is understandable and illustrative, while using it to support fast and rapid data exploration of large scale datasets has been a challenge for a long time. In this dissertation, we aim to facilitate efficient visual data exploration of large scale datasets from two perspectives: efficiency and interaction. The former indicates how users could understand the data efficiently, this depends on various factors, such as how fast data is processed and how data is presented, while the latter focuses more on the users: how they deal with the data and why they interact with the system in a particular way. In order to improve the efficiency of data exploration, we have looked into two steps in the visualization pipeline: rendering and processing (computations). We first address visualization rendering of large dataset through a thorough evaluation of web-based visualization performance. We evaluate and understand the page loading effects of Scalable Vector Graphics (SVG), a popular image format for interactive visualization on the web browsers. To understand the scalability of individual elements in SVG based visualization, we conduct performance tests on different types of charts, in different phases of rendering process. From the results, we have figured out optimization techniques and guidelines to achieve better performance when rendering SVG visualization. Secondly, we present a pure browser based distributed computing framework (VisHive) that exploits computational power from co-located idle devices for visualization. The VisHive framework speeds up web-based visualization, which is originally designed for single computer and cannot make use of additional computational resources on the client side. It takes advantage of multiple devices that today's users often have access to. VisHive constructs visualization applications that can transparently connect multiple devices into an ad-hoc cluster for local computation. It requires no specific software to be downloaded for setup. To achieve a more interactive data analysis process, we first propose a proactive visual analytics system (DataSite) that enable users to analyze the data smoothly with a list of pre-defined algorithms. DataSite provides results through selecting and executing computations using automatic server-side computation. It utilizes computational resources exhaustively during data analysis to reduce the burden of human thinking. Analyzing results identified by these background processes are surfaced as status updates in a feed on the front-end, akin to posts in a social media feed. DataSite effectively turns data analysis into a conversation between the user and the computer, thereby reducing the cognitive load and domain knowledge requirements on users. Next we apply the concept of proactive data analysis to genomic data, and explore how to improve data analysis through adaptive computations in bioinformatics domain. We build Epiviz Feed, a web application that supports proactive visual and statistical analysis of genomic data. It addresses common and popular biological questions that may be asked by the analyst, and shortens the time of processing and analyzing the data with automatic computations. We further present a computational steering mechanism for visual analytics that prioritizes computations performed on the dataset leveraging the analyst's navigational behavior in the data. The web-based system, called Sherpa, provides computational modules for genomic data analysis, where independent algorithms calculate test statistics relevant to biological inferences about gene regulation in various tumor types and their corresponding normal tissues.Item SMART STRUCTURAL CONDITION ASSESSMENT METHODS FOR CIVIL INFRASTRUCTURES USING DEEP LEARNING ALGORITHM(2018) Liu, Heng; Zhang, Yunfeng; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Smart Structural Health Monitoring (SHM) technique capable of automated and accurate structural health condition assessment is appealing since civil infrastructural resilience can be enhanced by reducing the uncertainty involved in the process of assessing the condition state of civil infrastructures and carrying out subsequent retrofit work. Over the last decade, deep learning has seen impressive success in traditional pattern recognition applications historically faced with long-time challenges, which motivates the current research in integrating the advancement of deep learning into SHM applications. This dissertation research aims to accomplish the overall goal of establishing a smart SHM technique based on deep learning algorithm, which will achieve automated structural health condition assessment and condition rating prediction for civil infrastructures. A literate review on structural health condition assessment technologies commonly used for civil infrastructures was first conducted to identify the special need of the proposed method. Deep learning algorithms were also reviewed, with a focus on pattern recognition application, especially in the computer vision field in which deep learning algorithms have reported great success in traditionally challenging tasks. Subsequently, a technical procedure is established to incorporate a particular type of deep learning algorithm, termed Convolutional Neural Network which was found behind the many success seen in computer vision applications, into smart SHM technologies. The proposed method was first demonstrated and validated on an SHM application problem that uses image data for structural steel condition assessment. Further study was performed on time series data including vibration data and guided Lamb wave signals for two types of SHM applications - brace damage detection in concentrically braced frame structures or nondestructive evaluation (NDE) of thin plate structures. Additionally, discrete data (neither images nor time series data), such as the bridge condition rating data from National Bridge Inventory (NBI) data repository, was also investigated for the application of bridge condition forecasting. The study results indicated that the proposed method is very promising as a data-driven structural health condition assessment technique for civil infrastructures, based on research findings in the four distinct SHM case studies in this dissertation.