GreenVis: Energy-Saving Color Schemes for Sequential Data Visualization on OLED Displays

The organic light emitting diode (OLED) display has recently become popular in the consumer electronics market. Compared with current LCD display technology, OLED is an emerging display technology that emits light by the pixels themselves and doesn’t need an external back light as the illumination source. In this paper, we offer an approach to reduce power consumption on OLED displays for sequential data visualization. First, we create a multi-objective optimization approach to find the most energy-saving color scheme for given visual perception difference levels. Second, we apply the model in two situations: pre-designed color schemes and auto generated color schemes. Third, our experiment results show that the energy-saving sequential color scheme can reduce power consumption by 17.2% for pre-designed color schemes. For auto-generated color schemes, it can save 21.9% of energy in comparison to the reference color scheme for sequential data

Supporting Evolution and Maintenance of android Apps

Mobile developers and testers face a number of emerging challenges. These include rapid platform evolution and API instability; issues in bug reporting and reproduction involving complex multitouch gestures; platform fragmentation; the impact of reviews and ratings on the success of their apps; management of crowd-sourced requirements; continuous pressure from the market for frequent releases; lack of effective and usable testing tools; and limited computational resources for handheld devices. Traditional and contemporary methods in software evolution and maintenance were not designed for these types of challenges; therefore, a set of studies and a new toolbox of techniques for mobile development are required to analyze current challenges and propose new solutions. This dissertation presents a set of empirical studies, as well as solutions for some of the key challenges when evolving and maintaining android apps. In particular, we analyzed key challenges experienced by practitioners and open issues in the mobile development community such as (i) android API instability, (ii) performance optimizations, (iii) automatic GUI testing, and (iv) energy consumption. When carrying out the studies, we relied on qualitative and quantitative analyses to understand the phenomena on a large scale by considering evidence extracted from software repositories and the opinions of open-source mobile developers. From the empirical studies, we identified that dynamic analysis is a relevant method for several evolution and maintenance tasks, in particular, because of the need of practitioners to execute/validate the apps on a diverse set of platforms (i.e., device and OS) and under pressure for continuous delivery. Therefore, we designed and implemented an extensible infrastructure that enables large-scale automatic execution of android apps to support different evolution and maintenance tasks (e.g., testing and energy optimization). In addition to the infrastructure we present a taxonomy of issues, single solutions to the issues, and guidelines to enable large execution of android apps. Finally, we devised novel approaches aimed at supporting testing and energy optimization of mobile apps (two key challenges in evolution and maintenance of android apps). First, we propose a novel hybrid approach for automatic GUI-based testing of apps that is able to generate (un)natural test sequences by mining real applications usages and learning statistical models that represent the GUI interactions. In addition, we propose a multi-objective approach for optimizing the energy consumption of GUIs in android apps that is able to generate visually appealing color compositions, while reducing the energy consumption and keeping a design concept close to the original

Visual Query System to Help Users Refine Queries from High-Dimensional Data: A Case Study

Temporal queries are normally issued for cohort selection from the high-dimensional dataset in many contexts, such as medical related research areas. The idea was inspired by the difficulties when interacting with the i2b2 system, an NIH-funded National Center for Biomedical Computing based at Partners HealthCare System, which seldom provides informative feedbacks and interactive exploration about the clinical events of each query or the expecting follow-up cohort. Considering the complexity and time-consuming nature of complicated temporal queries, it would be frustrating when iterative query refining is needed. The paper presents a newly designed web-based visual query system to facilitate refining the initial temporal query to select a satisfactory cohort for a given research. A detailed interface design associated with the query time frame and the implementation of the visual query algorithm that enables advanced arbitrary temporal query logic is included. In addition, a case study with 3 participants in medical related research areas was conducted that shows the system was overall useful to help the users to gain an idea about their follow-up queries.Master of Science in Information Scienc