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<p> Mobile phones have become so popular in navigation. Empirical studies, however, have implied several pitfalls of using these mobile systems. First of all, the small size of the mobile screen fragmentizes the map information so that users have to interact with the display frequently for fear of disorientation. In addition, generated navigation guides with continuous displays of routing information relieves users&rsquo; cognitive load, but the excessive reliance on the displayed spatial information keeps the users being mindless of the environment that impacts their acquisition of spatial knowledge. Later, landmarks as important referents were suggested to help users integrate the current surroundings with further decision points to support their sense of direction and cognitive mapping. But the small display limited users to access the landmarks located out of the view. To address this issue, designs attempt to display distant landmarks at the edge of screen as a way to overcome the aforementioned limits. In order to enhance spatial orientation while using mobile devices, this study introduced an improved design that not only display the direction but also the distance concept of distant landmarks by changing in size as an indicator of the distance from a user&rsquo;s location. Built on this, two kinds of mechanism were designed to present the distance concept by icons of different sizes: one is based on ratio scale that icons change in size continuously based on an established ratio to the actual distance between the user and the distant location. The other mechanism is based on ordinal scale which assigns one of three different sizes to a certain range of distance implying near, middle, and far. A formal user study was carried out to compare efficiency of these two mechanisms in four types of distance comparison tasks. Results show that ordinal icons are more effective than ratio icons in visualizing relative distances between two distant landmarks. But for both mechanisms, users have challenges distinguishing distant landmarks from local landmarks when displayed on screen simultaneously. A further step is to explore some other feasible options of representing distance.</p>

<p> Scientific datasets associated with a research project can proliferate over time as a result of activities such as sharing datasets among collaborators, extending existing datasets with new measurements, and extracting subsets of data for analysis. As such datasets begin to accumulate, it becomes increasingly difficult for a scientist to keep track of their derivation history, which complicates data sharing, provenance tracking, and scientific reproducibility. Understanding what relationships exist between datasets can help scientists recall their original derivation history. For instance, if dataset <i>A</i> is contained in dataset <i>B,</i> then the connection between <i>A</i> and <i>B</i> could be that <i>A</i> was extended to create <i>B.</i> </p><p> We present a relationship-identification methodology as a solution to this problem. To examine the feasibility of our approach, we articulated a set of relevant relationships, developed algorithms for efficient discovery of these relationships, and organized these algorithms into a new system called ReConnect to assist scientists in relationship discovery. We also evaluated existing alternative approaches that rely on flagging differences between two spreadsheets and found that they were impractical for many relationship-discovery tasks. Additionally, we conducted a user study, which showed that relationships do occur in real-world spreadsheets, and that ReConnect can improve scientists' ability to detect such relationships between datasets. </p><p> The promising results of ReConnect's evaluation encouraged us to explore a more automated approach for relationship discovery. In this dissertation, we introduce an automated end-to-end prototype system, ReDiscover, that identifies, from a collection of datasets, the pairs that are most likely related, and the relationship between them. Our experimental results demonstrate the overall effectiveness of ReDiscover in predicting relationships in a scientist's or a small group of researchers' collections of datasets, and the sensitivity of the overall system to the performance of its various components.</p>