The world’s wealth in pizza: Improving the comprehension of large numbers through information visualization

Extreme numerical magnitudes are part of our daily lives, from science to economics to politics. Specifically for large monetary measures, however, there are no comprehensive models for visualization practitioners to promote their understanding. Previous works on this topic have provided a framework for the visual depiction of complex measures but did not assess its effectiveness in communicating the real magnitude of the presented measures. In this thesis I bring together findings from Information Visualization and numerical cognition to extend the existing framework and assess the effects of different strategies, with a focus on monetary measures. For this, I created three visualization prototypes and conducted a series of user tests focused on insight creation. User tests highlighted advantages and disadvantages for different strategies and yielded various findings for their implementation in Information Visualisation

Coordination, Division of Labor, and Open Content Communities: Template Messages in Wiki-Based Collections

In this paper we investigate how in commons based peer production a large community of contributors coordinates its efforts towards the production of high quality open content. We carry out our empirical analysis at the level of articles and focus on the dynamics surrounding their production. That is, we focus on the continuous process of revision and update due to the spontaneous and largely uncoordinated sequence of contributions by a multiplicity of individuals. We argue that this loosely regulated process, according to which any user can make changes to any entry, while allowing highly creative contributions, has to come into terms with potential issues with respect to the quality and consistency of the output. In this respect, we focus on emergent, bottom up organizational practice arising within the Wikipedia community, namely the use of template messages, which seems to act as an effective and parsimonious coordination device in emphasizing quality concerns (in terms of accuracy, consistency, completeness, fragmentation, and so on) or in highlighting the existence of other particular issues which are to be addressed. We focus on the template "NPOV" which signals breaches on the fundamental policy of neutrality of Wikipedia articles and we show how and to what extent imposing such template on a page affects the production process and changes the nature and division of labor among participants. We find that intensity of editing increases immediately after the "NPOV" template appears. Moreover, articles that are treated most successfully, in the sense that "NPOV" disappears again relatively soon, are those articles which receive the attention of a limited group of editors. In this dimension at least the distribution of tasks in Wikipedia looks quite similar to what is know about the distribution in the FLOSS development process

Coordination, Division of Labor, and Open Content Communities: Template Messages in Wiki-Based Collections.

In this paper we investigate how in commons based peer production a large community of contributors coordinates its efforts towards the production of high quality open content. We carry out our empirical analysis at the level of articles and focus on the dynamics surrounding their production. That is, we focus on the continuous process of revision and update due to the spontaneous and largely uncoordinated sequence of contributions by a multiplicity of individuals. We argue that this loosely regulated process, according to which any user can make changes to any entry, while allowing highly creative contributions, has to come into terms with potential issues with respect to the quality and consistency of the output. In this respect, we focus on emergent, bottom up organizational practice arising within the Wikipedia community, namely the use of template messages, which seems to act as an effective and parsimonious coordination device in emphasizing quality concerns (in terms of accuracy, consistency, completeness, fragmentation, and so on) or in highlighting the existence of other particular issues which are to be addressed. We focus on the template "NPOV" which signals breaches on the fundamental policy of neutrality of Wikipedia articles and we show how and to what extent imposing such template on a page affects the production process and changes the nature and division of labor among participants. We find that intensity of editing increases immediately after the "NPOV" template appears. Moreover, articles that are treated most successfully, in the sense that "NPOV" disappears again relatively soon, are those articles which receive the attention of a limited group of editors. In this dimension at least the distribution of tasks in Wikipedia looks quite similar to what is know about the distribution in the FLOSS development process.commons based peer production; wikipedia; wiki; survival analysis; quality; bug fixing; template messages; coordination

Social Media for Cities, Counties and Communities

Social media (i.e., Twitter, Facebook, Flickr, YouTube) and other tools and services with user- generated content have made a staggering amount of information (and misinformation) available. Some government officials seek to leverage these resources to improve services and communication with citizens, especially during crises and emergencies. Yet, the sheer volume of social data streams generates substantial noise that must be filtered. Potential exists to rapidly identify issues of concern for emergency management by detecting meaningful patterns or trends in the stream of messages and information flow. Similarly, monitoring these patterns and themes over time could provide officials with insights into the perceptions and mood of the community that cannot be collected through traditional methods (e.g., phone or mail surveys) due to their substantive costs, especially in light of reduced and shrinking budgets of governments at all levels. We conducted a pilot study in 2010 with government officials in Arlington, Virginia (and to a lesser extent representatives of groups from Alexandria and Fairfax, Virginia) with a view to contributing to a general understanding of the use of social media by government officials as well as community organizations, businesses and the public. We were especially interested in gaining greater insight into social media use in crisis situations (whether severe or fairly routine crises, such as traffic or weather disruptions)

Rethinking Map Legends with Visualization

This design paper presents new guidance for creating map legends in a dynamic environment. Our contribution is a set of guidelines for legend design in a visualization context and a series of illustrative themes through which they may be expressed. These are demonstrated in an applications context through interactive software prototypes. The guidelines are derived from cartographic literature and in liaison with EDINA who provide digital mapping services for UK tertiary education. They enhance approaches to legend design that have evolved for static media with visualization by considering: selection, layout, symbols, position, dynamism and design and process. Broad visualization legend themes include: The Ground Truth Legend, The Legend as Statistical Graphic and The Map is the Legend. Together, these concepts enable us to augment legends with dynamic properties that address specific needs, rethink their nature and role and contribute to a wider re-evaluation of maps as artifacts of usage rather than statements of fact. EDINA has acquired funding to enhance their clients with visualization legends that use these concepts as a consequence of this work. The guidance applies to the design of a wide range of legends and keys used in cartography and information visualization

Prioritization of Software and System Requirements through Natural Language Processing for Testing Software

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2021Safety¬critical systems have been a constant and increased presence in industrial production, such as railways and vehicles. These systems are highly configurable and must be intensively tested by system engineers before being deliverable to customers. This process is highly time¬consuming and might require associations between the product features and requirements demanded by customers. Requirement prioritization looks to recognize the most relevant requirements of a system, aiming to reduce the costs and time of the testing process. Machine Learning has been shown useful in helping engineers in this task, automating associations between features and requirements. However, its application can be more difficult when requirements are written in natural language and if a ground truth dataset does not exist with them. In our work, we present ARRINA, a Natural Language Processing¬based recommendation system able to extract and associate components from safety¬critical systems with their specifications written in natural language and process customer requirements and map them to components. The system integrates a Weight Association Rule Mining framework to extract the components and their associations and generates visualizations that can help engineers understand which components are generally introduced in project requirements. The system also includes a recommendation framework that can associate in put requirements to existing subsystems, reducing engineers’ effort in terms of requirement analysis and prioritization. We performed several experiments to evaluate the different components of ARRINA over four railway’s subsystems and input requirements. As a result, the system achieved 90% of accuracy, which denotes its importance in reducing the time¬consuming of engineers in discovering the correct subsystem links and prioritizing requirements for the testing process