Do UML object diagrams affect design comprehensibility? Results from a family of four controlled experiments

Objective: The main objective of our study is to assess whether the use of UML (Unified Modeling Language) object diagrams improves comprehensibility of software design when this kind of diagrams is added to UML class diagrams.Method: We have conducted a family of four controlled experiments. We involved groups of bachelor and master students.Results: Results suggest that the use of object diagrams does not always introduce significant benefits in terms of design comprehensibility. We found that benefits strongly depend on the experience of participants and their familiarity with UML. More experienced participants achieved better design comprehensibility when provided with both class and object diagrams, while less experienced seemed to be damaged when using class and object diagrams together. Results also showed the absence of substantial variations in the time needed to comprehend UML models, with or without object diagrams.Implications: Our results suggest that it is important to be aware and take into account experience and UML familiarity before using object diagrams in software modeling.     

A personal visual analytics on smartphone usage data

The percentage of individuals frequently using their smartphones in work and life is increasing steadily. The interactions between individuals and their smartphones can produce large amounts of usage data, which contain rich information about smartphone owners’ usage habits and their daily life. In this paper, a personal visual analytic tool is proposed to develop insights and discover knowledge of personal life in smartphone usage data. Four cooperated visualization views and many interactions are provided in this tool to visually explore the temporal features of various interactive events between smartphones and their users, the hierarchical associations among event types, and the detailed distributions of massive event sequences. In the case study, plenty of interesting patterns are discovered by analyzing the data of two smartphone users with different usage styles. We also conduct a one-month user study on several invited volunteers from our laboratory and acquaintance circle to improve our prototype system based on their feedback.     

UNMAT: Visual comparison and exploration of uncertainty in large graph sampling

Graph sampling, simplying the networks while preserving primary graph characteristics, provides a convenient means for exploring large network. During the last few years a variety of graph sampling algorithms have been proposed, and the evaluation and comparison of the algorithms has witnessed a growing interest. Although different tests have been conducted, an important aspect of graph sampling, namely, uncertainty in graph sampling, has been ignored so far. Additionally, existing studies mainly rely on simple statistical analysis and a few relatively small datasets. They may not be applicable to other more complicated graphs with much larger numbers of nodes and edges. Furthermore, while graph clustering is becoming increasingly important, it is still unknown how different sampling algorithms and their associated uncertainty can impact the subsequent graph analysis, such as graph clustering. In this work, we propose an efficient visual analytics framework for measuring the uncertainty from different graph sampling methods and quantifying the influence of the uncertainty in general graph analysis procedures. A spreadsheet-style visualization with rich user interactions is presented to facilitate visual comparison and analysis of multiple graph sampling algorithms. Our framework helps users gain a better understanding of the graph sampling methods in producing uncertainty information. The framework also makes it possible for users to quickly evaluate graph sampling algorithms and select the most appropriate one for their applications.     

Animated chorem-based summaries of geographic data streams from sensors in real time

This paper describes a new visualization approach for the automatic generation of visual summaries dealing with cartographic visualization methods and modeling of real time data coming from sensors. Indeed the concept of chorems seems an interesting candidate to visualize real time geographic database summaries. Chorems have been defined by Roger Brunet as schematized visual representations of territories. However, the time information is not yet handled in existing chorematic map approaches, that is the issue been discussed in this paper in which geodata are coming regularly from sensors distributed along some territory. Our approach is based on spatial analysis by interpolating the values recorded at the same time, so we have a number of distributed observations on areas of study. To get a better visual overview of the entire sensor geodata at a given time, we use spatial statistics formulas on the fly, and so it is possible to extract important spatiotemporal patterns and detect trends over time as geographic rules. Then, those spatiotemporal patterns are visualized as animated chorems. An example is taken from meteorology.