Journal of Visual Languages and Computing最新文献

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.

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.

Existing network visualizations support hierarchical exploration, which rely on user interactions to create and modify graph hierarchies based on the patterns in the data attributes. It will take a relatively long time for users to identify the impact of different attributes on the cluster structure. To address this problem, this paper proposes a visual analytical approach, called HybridVis, creating an interactive layout to reveal clusters of obvious characteristics on one or more attributes at different scales. HybridVis can help people gain social insight and better understand the roles of attributes within a cluster. First, an approximate optimal graph hierarchy based on an energy model is created, considering both data attributes and relationships among data items. Then a layout algorithm and a level-dependent perceptual view for multi-scale graphs are proposed to show the attribute-driven graph hierarchy. Several views, which interact with each other, are designed in HybridVis, including a graphical view of the relationships among clusters; a cluster tree revealing the cluster scales and the details of attributes on parallel coordinates augmented with histograms and interactions. From the meaningful and globally approximate optimal abstraction, users can navigate a large multivariate graph with an overview+detail to explore and rapidly find the potential correlations between the graph structure and the attributes of data items. Finally, experiments using two real world data sets are performed to demonstrate the effectiveness of our methods.

The proposed work aims at visual subspace clustering and addresses two challenges: an efficient visual subspace clustering workflow and an intuitive visual description of subspace structure. Handling the first challenge is to escape the circular dependency between detecting meaningful subspaces and discovering clusters. We propose a dimension relevance measure to indicate the cluster significance in the corresponding subspace. The dynamic dimension relevance guides the subspace exploring in our visual analysis system. To address the second challenge, we propose hyper-graph and the visualization of it to describe the structure of subspaces. Dimension overlapping between subspaces and data overlapping between clusters are clearly shown with our visual design. Experimental results demonstrate that our approach is intuitive, efficient, and robust in visual subspace clustering.

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.

E-commerce websites, besides selling products and services, pay ample emphasis on providing a platform for consumers to share their opinions about past and potential purchases. They share such opinions as product reviews (star ratings, plain text, etc.) and answering product related questions (Q&A data). There are several machine learning and classification approaches available to scrutinize this review data, e.g., algorithms based on Entropy measures, Bilinear Similarity, stochastic methods, etc. In this paper, we review some of the prevalent review classification techniques and present a hybrid approach, involving Singular Value Decomposition (SVD), Entropy and Bilinear Similarity measures, that uses heterogeneous product data and simultaneously analyze and rank products for customers. With experimental results, we show that our approach effectively ranks products using (1) text reviews (2) Q&A data (3) five-star rating of products and has 10% improved prediction accuracy as compared to the individual approaches. Also, using SVD, we achieve a 35% runtime efficiency for our algorithm while only sacrificing 1% of the prediction accuracy.

Annotation has become a common practice when interacting with digital documents, allowing the addition of information to existing data without corrupting them. Restricting visibility of annotations to groups of users, as usually enforced in enterprise collaboration systems, turns annotations into a tool for focused collaboration among users with common interests. However, providing different levels of visibility in an open Web-based environment is challenging, due to conflicting needs: to keep the annotation process simple, to ensure adequate levels of privacy and confidence to users, and to allow users to filter out irrelevant information. We describe a suite of services for group formation and management, integrated into the existing MADCOW annotation system. We also propose a formal characterisation of groups, introducing atomic operations on which complex functionalities are based. The paper discusses different scenarios and applications and argues for the usability of the proposed integration by presenting the results of experimental tests.

World cultural heritage is the accumulation and essence of the development of human civilization, as well as the rare and irreplaceable treasures bestowed by history. However, cultural heritage is increasingly exposed to various risks caused by natural and man-made factors. Flood risk is the most common and the most devastating risk for cultural heritage. This study proposes a visual analytics method that supports the visual analysis of flood risk from multiple aspects, including predicted flood peak flow, flood propagation, flood impact, and vulnerability. The proposed method can also provide the required information from multiple scales, including the basin-, site-, multi-cave-, and single-cave-scale levels. The combination of the visualization techniques of flood risk analysis will enable the proposed method to support users to make decisions with respect to mitigation measures. Lastly, the proposed method is evaluated by water experts and cultural heritage site managers.

Mobile data has various properties which contained social and spatio-temporal information of human activities. To support security department for crime fighting we encountered several challenges in the way of exploring complex mobile data, in order to solve those challenges this work has built a vivid method for mobile data analysis from social and spatio-temporal aspects, moreover, a prototype system was built to support person and community’s pattern analysis for security department recognize abnormal person, for the sake of support quickly targets searching, community detection was used to obtain the high-level information and the detail-information is presented by a series visualization models, users are able to accomplish their investigation tasks with interact with visual analysis system. Finally, we demonstrated the superiority of our method by the result that came from the analyzing of the call records which provided by an anonymous communications operator company.