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Switching between windows on a computer is a frequent activity, but finding and switching to the target window can be inefficient. This thesis aims to better understand and support window switching. It explores two issues: (1) the lack of knowledge of how people currently interact with and switch between windows and (2) how window switching can be supported better. Having a good understanding of how users interact with windows is important for informing the design of new and improved window management tools. However, there have been relatively few empirical studies of window manipulation on commonly used operating systems, and those that do exist may no longer reflect current use. To address this lack of knowledge a three week log-based longitudinal study of window use by 25 participants was conducted using the custom-made tool PyLogger, which recorded actual window switching behaviour. However, the analysis of longitudinal log data, such as the data gathered by PyLogger, is problematic as it is difficult to extract meaningful characterisations. Therefore, this thesis also presents a visualisation tool called Window Watcher that assists understanding and interpreting the low level event logs of window use generated by PyLogger. Window Watcher’s design objectives are described, and examples demonstrate the ways that it summarises and elucidates window use. The results of the PyLogger study provide an empirical characterisation of interaction with windows, and results include the following: (1) the participants had fewer windows open and visible than in previous studies; (2) window switching is a frequent activity; (3) several findings related to specific window switching tools, including that acquiring a particular window by navigating through application-grouped items on the Taskbar is slow, and that Alt+Tab is seldom used for retrieving anything other than the most recently used window; (4) an updated classification of stereotypical window management styles (pilers, maximisers, near maximisers, and splatterers); and (5) there are strong window and application revisitation patterns. Finally, implications of the results of the log study for the design of window switching tools are discussed. The findings from the PyLogger study led to the development of a new window switcher called SCOTZ (for Spatially Consistent Thumbnail Zones). SCOTZ is a window switching interface which shows all windows grouped by application and allocates more space to the most frequently revisited applications. The two design objectives of SCOTZ are (1) to provide a spatially stable layout of applications and windows, and (2) to support revisitation to recently and frequently used windows. Additional design objectives are to support various display sizes, to support both keyboard and mouse input, to provide possibilities for application launching and to provide options for end-user customisation. The design and features of SCOTZ are described, followed by theoretical and empirical validation of its underlying design principles. Findings include that (1) spatially stable layouts allow for faster acquisition of targets than recency and random layouts, (2) the instability inevitably caused by size morphing does not severely impact user performance, (3) size morphing leads to an overall performance advantage because of the Fitts’ Law targetting time advantage of increased target size, and (4) size morphing facilitates finding items because of guided search. Also, findings from an empirical study demonstrate that SCOTZ yields performance and preference benefits over existing window switching tools. Finally, as SCOTZ employs a treemap algorithm to generate the layout of the application zones the suitability of various treemap algorithms for the purpose of SCOTZ is explored, particularly in terms of spatial stability. In previous work, many different treemap algorithms have been proposed, often with the aim being to optimise performance across several criteria, including spatial stability. However, none of the existing treemaps are stable when data updates, and when items are added/deleted, and when many changes have taken place (i.e., the cumulative effect of data changes). Therefore, this thesis introduces the novel ‘Hilbert’ and ‘Moore’ treemap algorithms, which are designed to achieve high spatial stability. Their performance is theoretically assessed in comparison to other treemaps by using various metrics, including a novel ‘location drift’ metric to better capture spatial stability than the commonly used ‘distance change’ metric. The theoretical evaluation demonstrates that Hilbert and Moore treemaps perform well across all stability metrics. An empirical study examines the validity and usefulness of the location drift metric, showing that location drift can explain some effects on user performance that distance change alone can not.

No description available.

Despite advances in multivariate visualisations and computer graphics, allowing for effective implementations, most particle physics analyses still rely on conventional data visualisations. The currently available software implementing these techniques has been found to be inadequate for use with the large volume of multivariate data produced from modern particle physics experiments. After a design and development period, a novel piece of software, DataViewer, was produced. DataViewer was used as part of a physics analysis at the CMS experiment, searching for an associated Higgs decaying through a dark sector into collimated groups of electrons, called Electron Jets. Observation of such a signature could explain astrophysical anomalies found by numerous telescopes. The full 2011 dataset, equivalent to an integrated luminosity of 4.83 fb^(-1) at a centre of mass energy of sqrt(s) = 7 TeV, recorded by the experiment was analysed. DataViewer was found to be extremely powerful in rapidly identifying interesting attributes of the signature which could then be exploited in the analysis. Additionally it could be used for cross checking other complex techniques, including multivariate classifiers. No evidence was found for the production of a Higgs boson in association with a Z boson, where the Higgs subsequently decays to Electron Jets. Upper limits on the production of benchmark models were set at the 95% Confidence Level.

Indiana University-Purdue University Indianapolis (IUPUI) / The purpose of this work is to prove that the visualization is at least as powerful as the best automatic feature selection algorithms. This is achieved by applying our visualization technique to the online review classification into fake and genuine reviews. Our technique uses radial chart and color overlaps to explore the best feature selection through visualization for classification. Every review is treated as a radial translucent red or blue membrane with its dimensions determining the shape of the membrane. This work also shows how the dimension ordering and combination is relevant in the feature selection process. In brief, the whole idea is about giving a structure to each text review based on certain attributes, comparing how different or how similar the structure of the different or same categories are and highlighting the key features that contribute to the classification the most. Colors and saturations aid in the feature selection process. Our visualization technique helps the user get insights into the high dimensional data by providing means to eliminate the worst features right away, pick some best features without statistical aids, understand the behavior of the dimensions in different combinations.

Masters of Science / Plant genome analysis is currently experiencing a boost due to reduced costs associated with the development of next generation sequencing technologies. Knowledge on genetic background can be applied to guide targeted plant selection and breeding, and to facilitate natural product discovery and biological engineering. In medicinal plants, secondary metabolites are of particular interest because they often represent the main active ingredients associated with health-promoting qualities. Plant polyphenols are a highly diverse family of aromatic secondary metabolites that act as antimicrobial agents, UV protectants, and insect or herbivore repellents. Most of the genome mining tools developed to understand genetic materials have very seldom addressed secondary metabolite genes and biosynthesis pathways. Little significant research has been conducted to study key enzyme factors that can predict a class of secondary metabolite genes from polyketide synthases. The objectives of this study were twofold: Primarily, it aimed to identify the biological properties of secondary metabolite genes and the selection of a specific gene, naringenin-chalcone synthase or chalcone synthase (CHS). The study hypothesized that data science approaches in mining biological data, particularly secondary metabolite genes, would enable the compulsory disclosure of some aspects of secondary metabolite (SM). Secondarily, the aim was to propose a proof of concept for classifying or predicting plant genes involved in polyphenol biosynthesis from data science techniques and convey these techniques in computational analysis through machine learning algorithms and mathematical and statistical approaches. Three specific challenges experienced while analysing secondary metabolite datasets were: 1) class imbalance, which refers to lack of proportionality among protein sequence classes; 2) high dimensionality, which alludes to a phenomenon feature space that arises when analysing bioinformatics datasets; and 3) the difference in protein sequences lengths, which alludes to a phenomenon that protein sequences have different lengths. Considering these inherent issues, developing precise classification models and statistical models proves a challenge. Therefore, the prerequisite for effective SM plant gene mining is dedicated data science techniques that can collect, prepare and analyse SM genes.

Code clones are identical or similar code fragments in a single software system or across multiple systems. Frequent copy-paste-modify activities and reuse of existing systems result in maintenance difficulties and security issues. Addressing these problems requires analysts to undertake code clone analysis, which is an intensive process to discover problematic clones in existing software. To improve the efficiency of this process, tools for code clone detection and analysis, such as Kam1n0 and CCFinder, were created. Kam1n0 is an efficient code clone search engine that facilitates assembly code analysis. However, Kam1n0 search results can contain millions of function-clone pairs, and efficiently exploring and comprehensively understanding the resulting data can be challenging. This thesis presents a design study whereby we collaborated with analyst stakeholders to identify requirements for a tool that visualizes and scales to millions of function-clone pairs. These requirements led to the design of an interactive visual tool, CloneCompass, consisting of novel TreeMap Matrix and Adjacency Matrix visualizations to aid in the exploration of assembly code clones extracted from Kam1n0. We conducted a preliminary evaluation with the analyst stakeholders, and we show how CloneCompass enables these users to visually and interactively explore assembly code clones detected by Kam1n0 with suspected vulnerabilities. To further validate our tool and extend its usability to source code clones, we carried out a Linux case study, where we explored the clones in the Linux kernel detected by CCFinder and gained a number of insights about the cloning activities that may have occurred in the development of the Linux kernel. / Graduate

<p dir="ltr">Invasive species wreak havoc on global ecosystems, with negative ecological and economic consequences. Human activities, primarily stemming from globalization, trade, and increased travel, have played a significant role in accelerating species invasions. To manage and possibly mitigate these challenges, humans can harness data analysis to predict and control species invasions. Addressing this issue requires an understanding of the spatiotemporal dynamics of invasions. This research developed an innovative visualization tool designed to illustrate complex spatiotemporal data pertaining to species invasion routes. By analyzing pest invasion records spanning from 1905 to 2020, the tool presents the invasion trajectories of four non-native species in the eastern United States. Implementing spatial tools such as road networks and terrain, the visualization clarifies the spatiotemporal progression of these invasions, allowing users to intuitively determine invasion epicenters, and identify propagation pathways. Additional features enable the examination of correlations between highway systems, terrain, and invasion dynamics. Following a comprehensive training and exploration phase with domain experts, the efficacy of the tool was proven. The findings underline the proposed solution’s potential to enhance users’ comprehension of invasion dynamics, highlight intrusion centers, and indicate the influence of external factors on species expansion. This study not only validates the visualization tool’s capability but also serves as a foundation for future spatiotemporal research endeavors.</p>

Gentrification, a process conceived to result in displacement of lower-income urban residents, is difficult to measure quantitatively due to its qualitative, social impacts. Additionally, the phenomenon is a wicked problem, with no decisive definition or a set list of causes. Whereas researchers have instigated attempts to numerically measure gentrification, there is a lack of a systematic and universal approach to evaluate the concept. To investigate this issue, an iterative process took place using gentrification theory and explorative work. A test index was created using the inner boroughs of the UK’s capital, London, aiming to use data which should be available within all cities. Indicators for the index based on the two main theories of gentrification were attained for three different time periods from governmental and census records, creating a longitudinal study to establish how an area has changed, and whether gentrification has occurred. The technique presents evidence of increasing socio-economic status within many of London’s inner  boroughs, with evidence of rising employment rates, house prices and managerial role residents. The highest scoring boroughs were areas considered to be undergoing super-gentrification. From the index, the next borough to super-gentrify will be Hammersmith &amp; Fulham. For first time gentrifying boroughs, their index changes sit within the middle of the borough rankings. It is believed that further analysis and advancements are required on the index to ensure prevention of data misuse, conclusive results, and further consideration of cultural, political or social changes, however new contributions have been made within this topic from considering gentrification from a wicked problem viewpoint.

Anecdotal observation of the way in which data visualisation techniques are utilised to present relationships in data to audiences informed the author's view that data visualisation had not evolved to utilise the capabilities of ubiquitous business computer equipment. In an information rich but attention poor business environment, a search for a new tool was undertaken to supplement those techniques available to help audiences understand statistical relationships in presentation data. This search resulted in the development of a practical software tool based on animated Venn diagrams (Dvenn) that attempted to exploit the inherent human ability to perceive quantities visually, a faculty described herein as visual numeracy. The exploitation of this faculty is considered here to be a valuable aid for group understanding of business presentation data. The development of the tool was an essential part of the research that was undertaken and the resulting software forms a significant portion of this practise based research. The aim of the software development was to develop a readily accessible tool that could be utilised in a non-specialist business environment to better facilitate an honest shared meaning of numerical data between a presenter and their audience. The development of the tool progressed through a number of iterations and the software that accompanies this work is an important component that needs to be viewed in conjunction with the text. The test of the final version was undertaken with undergraduate University students in an attempt to validate the efficacy of the data visualisation technique. The test of the Dvenn software was made against the mature yardstick of scatter-plots. Interestingly, the correlations presented by scatter-plot were not as readily identified as would have been assumed, however, the results for the Dvenn tests were not supportive of the technique for widespread adoption. Nevertheless, further research into the best method of harnessing visual numeracy would seem to be justified.