Journal of Visual Languages and Computing最新文献

As a useful formalism tool, graph grammars provide a rigorous but intuitive way to specify visual languages. This paper, based on the existing Edge-based Graph Grammar (EGG), proposes a new context-sensitive graph grammar formalism called the Temporal Edge-based Graph Grammar, or TEGG. TEGG introduces some temporal mechanisms to grammatical specifications, productions, operations and so on in order to tackle time-related issues. In the paper, formal definitions of TEGG are provided first. Then, a new parsing algorithm with a decidability proof is proposed to check the correctness of a given graph's structure, to analyze operations’ timing when needed, and to make the computer simulation of the temporal sequence in the graph available. Next, the complexity of the parsing algorithm is analyzed. Finally, a case study on an application with temporal requirements is provided to show how the parsing algorithm of TEGG works.

MapReduce is an effective and widely-used framework for processing large datasets in parallel over a cluster of computers. Data skew, cluster heterogeneity, and network traffic are three issues that significantly affect the performance of MapReduce applications. However, the hash-based partitioner in the native Hadoop does not consider these factors. This paper proposes a new partitioner for Yarn (Hadoop 2.6.0), namely, NPIY, which adopts an innovative parallel sampling method to distribute intermediate data. The paper makes the following major contributions: (1) NPIY mitigates data skew in MapReduce applications; (2) NPIY considers the heterogeneity of computing resources to balance the loads among Reducers; (3) NPIY reduces the network traffic in the shuffle phase by trying to retain intermediate data on those nodes running both map and reduce tasks. Compared with the native Hadoop and other popular strategies, NPIY can reduce execution time by up to 41.66% and 58.68% in homogeneous and heterogeneous clusters, respectively. We further customize NPIY for parallel image processing, and the execution time has been improved by 28.8% compared with the native Hadoop.

The dimension ordering of parallel coordinate plots has been widely studied, aiming at the insightful exploration of multi-dimensional data. However, few works focus on the category distributions across dimensions and construct an effective dimension ordering to enable the visual exploration of clusters. Therefore, we propose a cluster-aware arrangement method of the parallel coordinate plots and design a visualization framework for the multi-dimensional data exploration. Firstly, a hierarchical clustering scheme is employed to identify the categories of interest across different dimensions. Then we design a group of icicle views to present the hierarchies of dimensions, the colors of which also indicate the relationships between different categories. A cluster-aware correlation is defined to measure the relationships between different attribute axes, based on the distributions of categories. Furthermore, a matrix map is designed to present the relationships between dimensions, and the MDS method is employed to transform the dimensions into 2D coordinates, in which the correlations among the dimensions are conserved. At last, we solve the Traveling Salesman Problem (TSP) and achieve an automated dimension ordering of the parallel coordinate plots, which largely highlights the relations of categories across dimensions. A set of convenient interactions are also integrated in the visualization system, allowing users to get insights into the multi-dimensional data from various perspectives. A large number of experimental results and the credible user studies further demonstrate the usefulness of the cluster-aware arrangement of the parallel coordinate plots.

Parameterization of triangulated surface meshes is a crucial problem in computer graphics, computer aided geometric design and digital geometric processing. This paper addresses the problem of planar parameterization, i.e., mapping a given triangulated surface onto a planar domain. We construct an optimized algorithm for parameterization of genus-zero meshes and aim to minimize the distortion of the parameterization. An energy functional is proposed in the paper, that quantities angle and area distortions simultaneously, while the relative importance between angle and area preservation can be controlled by the user through a parameter. The method is based on an iterative procedure that incrementally flattens mesh by growing region to obtain a parameterization result with free boundary. The result is then converted to a parameterization with regular boundary by conformal mapping. Application of the method to texture mapping is presented. Experiments show that the proposed method can obtain better results than some common parameterization methods.

Information search is a common yet important task in everyday work and life. It remains challenging how to help users search for information or things they don’t know how to express with words. Also, even when people know how to express, the cognitive cost required to retrieve the concepts and formulate the queries can be excessive. In this paper, we present Click-Search, a search user interface that allows people to indicate their search intents by merely selecting and cropping segments of pictures. The system automatically converts cropped image segments to keywords based on known semantic labels at the level of image pixels that are generated by crowdsourced image tagging. Through a user study, we showed that Click-Search could support object-finding activities effectively with a satisfactory user experience.

The Web and digital media require documents whose appearance and content adapt to the viewing context and to user interaction. While most previous research has focussed on adaptation for textual and multimedia content, this is also true for diagrammatic content. We (a) identify the reasons for adaptation and the different kinds of adaptation that make sense for diagrams; (b) present an aspect-oriented model for diagram adaptation that separates adaptation into different orthogonal components; (c) describe a diagram authoring tool based on this model; and (d) present the results of a user evaluation of the tool. Our model uses layout “configurations” to model significantly different layout alternatives and geometric constraints to perform minor layout adjustment. The author can also specify alternate representations for an object, alternate styles and alternate textual content. The resulting space of different versions of the diagram is the cross product of these different alternatives. At display time the version is selected from this cross product and constructed automatically, taking into account the author specified preference order on the alternatives, current viewing environment, and user interaction.

The emerging field of Immersive Analytics investigates how novel display and interaction technologies can enable people to better explore and analyse data and complex information. Collaboration is a crucial aspect of Immersive Analytics. In this paper we present ContextuWall, a system for interactive local and remote collaboration using touch and mobile devices as well as displays of various sizes. The system enables groups of users located on different sites to share content to a jointly used virtual desktop which is accessible over a secured network. This virtual desktop can be shown on different large displays simultaneously, taking advantage of their high resolution. To enable users to intuitively share, arrange as well as annotate image content, a purpose-built client software has been developed and can easily be adapted with plug-ins for existing data analytics software. We show exemplary use cases and describe the system architecture and its implementation.

Smart cities are providing advanced services aggregating and exploiting data from different sources. Cities collect static data such as road graphs, service description, as well as dynamic/real time data like weather forecast, traffic sensors, bus positions, city sensors, events, emergency data, flows, etc. RDF stores may be used to set up knowledge bases integrating heterogeneous information for web and mobile applications to use the data for new advanced services to citizens and city administrators, thus exploiting inferential capabilities, temporal and spatial reasoning, and text indexing. In this paper, the needs and constraints for RDF stores to be used for smart cities services, together with the currently available RDF stores are evaluated. The assessment model allows a full understanding of whether an RDF store is suitable to be used as a basis for Smart City modeling and applications. The RDF assessment model is also supported by a benchmark which extends available RDF store benchmarks at the state the art. The comparison of the RDF stores has been applied on a number of well-known RDF stores as Virtuoso, GraphDB (former OWLIM), Oracle, StarDog, and many others. The paper also reports the adoption of the proposed Smart City RDF Benchmark on the basis of Florence Smart City model, data sets and tools accessible as Km4City Http://www.Km4City.org, and adopted in the European Commission international smart city projects named RESOLUTE H2020, REPLICATE H2020, and in Sii-Mobility National Smart City project in Italy.

Public Bicycle System(PBS) is an increasingly popular mode of public transit, with the advantage of pollution-free and flexibility. In this paper, we present an interactive visual analytic system for exploring complex flows generated by PBS. Four inter-linked visualization views are designed to illustrate multiple perspectives of data, such as the spatial-temporal changes, the relationships and differences between flow OD pairs and the multi-dimensional factors(weather condition, calendar events) influencing on the rental numbers. A new presentation “Parallel coordinates with line and set” combined with flexible interaction schemes is proposed to support the exploration of multivariate association. We exemplify our approach with a real citywide PBS dataset. The results of case study demonstrate that our system is helpful for visually classifying stations with different flow patterns, speculating in-depth reasons, as well as investigating abnormal behaviors, helping decision makers to gain a better understanding of the large dataset.

Depth compression plays an important role in 3D video coding with the typical texture-plus-depth representation. In this paper, to reduce the encoding complexity of depth map, we propose a low complexity adaptive View Synthesis Optimization scheme for the 3D extension of high efficiency video coding (3D-HEVC) standard. More specifically, we distinguish the coding tree units (CTUs) based on the influence of depth map compression on the quality of rendered synthesized view, and classify them into two categories, including synthesized view distortion change (SVDC) based and view synthesis distortion estimation (VSDE) based CTUs. In this manner, we can dynamically distinguish the CTUs and apply different rate-distortion optimization strategies. Moreover, for VSDE based CTUs, a new distortion model is proposed to infer the distortion of the synthesized view based on the depth distortion and texture characteristics. As such, we can achieve a good trade-off between the rate-distortion performance and computational complexity for depth map coding. Experimental results also confirm that the proposed scheme is effective in reducing the encoding complexity with ignorable rate-distortion performance loss compared with the state-of-the-art scheme in the 3D-HEVC platform.