Journal of Visual Languages and Computing最新文献

Classical BM25 scoring is designed for unstructured documents. In the past years, people try to adapt the BM25 ranking formula to deal with structured documents. Most works on structured document retrieval treat the combination of field scores, but it is hard to determine the field weights before the formation of document score. We aim to establish a new method to sort the field weights. The motivation comes from two aspects. On the one hand, the construction of interval tree reflects retrieval results with higher-order proximity for a text field. According to writing style, the important sentence or phrase for representing main idea frequently appear in the front or the rear part of a text-field. Therefore, the proximity scoring for different part in a text-field should be different. We thus take higher factor for calculating proximity scoring in the front and the rear parts than in the middle part. On the other hand, the more the interval length includes inquiring terms, the less the proximity scoring is, thereby the higher tf value for term appearing in an interval should affect the computation of proximity scoring. Therefore, we develop a new method for calculating the field weights based on the ranking score. The ranking score for each field can be calculated by interval tree based on terms relevance. Interval tree can be viewed as a tool of higher terms proximity in text visualization. This new field weights reflect the terms proximity and can be used to calculate document scoring for terms retrieval. Experimental results show that the new document scoring model well reflects the terms proximity, and the new document scoring scheme ScoreComp, combined with interval scoring, is more sensitive than scheme FreqComp combined with interval scoring.

Extracting meaningful structures from images with complicated texture patterns is challenging since it is hard to separate structure from texture with similar scale or intensity contrast. In this paper, we propose a structure-preserving bilateral texture filtering algorithm to smooth texture while preserving salient structures. We design a dual-scale patch toggle scheme. That is, two-scale patches are exploited to represent pixels alternatively, the smaller ones for pixels located at structure edges and the bigger ones for pixels in textural regions, and DASM (Directional Anisotropic Structure Measurement) on each pixel is estimated to select which type of patch for it. Our algorithm is based on the framework of joint bilateral filtering, so it is fast, easy to implement, yet effective for adaptive image smoothing. In particular, our approach outperforms previous methods in terms of preserving small structures. The proposed method achieves excellent results that illustrate its effectiveness and efficiency.

In this paper, we present HomeSeeker, an interactive visual analytics system to serve users with different backgrounds of the local real estate market and meet different degrees of user requirements. As a result, HomeSeeker augments existing commercial systems to help users discover hidden patterns, link various location-centered data to the price, as well as explore, filter and compare the properties, in order to easily find their preferred properties. In particular, we make the following contributions: (1) We present a problem abstraction for designing visualizations that help home buyers analyse the real estate data. Specifically, our data abstraction integrates heterogeneous data from different channels into a location-centred integrated real estate dataset. (2) We propose an interactive visual analytic procedure to help less informed users gradually learn about the local real estate market, upon which users exploit this learned knowledge to specify their individual requirements in property seeking. (3) We propose a series of designs to visualize properties/suburbs in different dimensions and in different granularities. We have collected, integrated and cleaned last 10 year’s real estate sold records in Australia as well as their location-related education, facility and transportation profiles, to generate a real multi-dimensional data repository, and implemented a system prototype for public access (http://115.146.89.158). At last, we present case studies based on real-world datasets and real scenario to demonstrate the usefulness and effectiveness of our system.

Visual graphics and image-based content have become the pervasive modes of interaction with the digital information flow. With the immense proliferation of display systems and devices, visual content representation has become increasingly challenging. Classical static image resizing algorithms are not directly suitable for the current dynamic information visualization of streaming data flows and processes because most of the visual content often consists of superimposed, multi-layered, multi-scale structure. In this paper, we propose a new adaptive method for content-aware resizing of visual information flow. Scaling is performed by deforming a hierarchical triangle mesh that matches the visual saliency map (VSM) of the streaming data. The VSM is generated automatically based on a series of predefined rules operating on a triangular mesh representation of visual features. We present a linear energy function to minimize distortions of the triangular deformations to perceptually preserve informative content. Through multiple experiments on real datasets, we show that the method has both high performance as well as high robustness in the presence of large differences in the visual aspect ratios between target displays.

With the widespread application of networked information-based technologies throughout industry manufacturing, modern manufacturing facilities give rise to unprecedented levels of process data generation. Data-rich manufacturing environments provide a broad stage on which advanced data analytics play leading roles in creating manufacturing intelligence to support operational efficiency and process innovation. In this paper, we introduce a process data analysis solution that integrates the technologies of situation awareness and visual analytics for the routine monitoring and troubleshooting of roller hearth kiln (RHK), a complex key manufacturing facility for lithium battery cathode materials. Guided by a set of detailed scenarios and requirement analyses, we first propose a qualitative and quantitative situation assessment model to generate the comprehensive description of RHK's operating situation. An informative visual analysis system then is designed and implemented to enhance the users’ abilities of situation perception and understanding for insightful anomaly root cause reasoning and efficient decision making. We conduct case studies and a user interview together with the managers and operators from manufacturing sites as system evaluation. The result demonstrates its effectiveness and prospects its possible inspiration for other similar scenarios about complex manufacturing facility monitoring in smart factories.

This paper presents a context-aware design pattern for situated analytics called Blended Model View Controller. Our approach is an event-driven design, allowing a seamless transition between the physical space and information space during use. The Blended Model View Controller allows common user interface controls to work in tandem with printed information on a physical object by adapting the operation and presentation based on a semantic matrix. Also presented is an authoring tool that has been developed to assign the parameters of the semantic matrix. We demonstrate the use of the design pattern with a set of augmented reality interactions including; pinch zoom, menus, and details-on-demand. We analyse each control to highlight how the physical and virtual information spaces work in tandem to provide a rich interaction environment in augmented reality.

Graph layout algorithms are used to compute aesthetic and useful visualizations of graphs. In general, for graphs with up to a few hundred nodes, force-directed layout algorithms produce good layouts. Unfortunately, for larger graphs, they often get stuck at local minima and have high computational complexity. In this paper, we introduce a novel message passing technique for graph layout. The key idea of our message passing technique is that an aesthetic layout can be obtained if each node independently suggests aesthetic placements of its neighbors. In other words, every node sends messages to its neighbors, indicating new and better positions for them. As a result, the new technique, which we call Neighborhood Beautification, provides a new perspective that turns out to give a useful trade-off between the excellent layout quality reached by force-directed methods and the fast runtime achieved by algebraic methods. Neighborhood Beautification reduces, in many cases, the computational cost of force-directed algorithms, since only interactions between neighboring nodes are considered. Experimentally, we show that Neighborhood Beautification produces high-quality layouts for grid-like graphs but is outperformed by force-directed algorithms in the case of more complex graphs.

Star coordinates is an important visualization tool for exploring high-dimensional data. By carefully manipulating the star-coordinate axes, users can obtain a good projection matrix to reveal the cluster structures in the high-dimensional data. However, finding a good projection matrix through axes manipulation is often a very tedious and trial-and-error process. This paper presents cluster aware star coordinates plot, which not only improves the efficiency of axes manipulation with higher cluster quality, but also enables users to learn the relations between cluster and data attributes. Based on the proposed approximated visual silhouette index, we introduce the silhouette index view, which interactively informs the user of the cluster quality of the projection. However, the user may still have no clue on how to manipulate the axes to improve the cluster quality. To resolve this issue, we propose a dimensionality reduction technique for visualization to progressively modify the projection matrix and improve the cluster results. Through this technique including a family of cluster-aware interactions, users can highlight important features of interest, such as points, clusters and dimensions, effectively investigate the change of cluster structures, and steer their relationship with the dimensions. In the end, we employ twelve high-dimensional data sets and demonstrate the effectiveness of our method through a series of experiments: comparison with state-of-the-art methods, interactive outlier detection, and exploration of cluster-dimension relationship.

Information visualization has been widely used to convey information from data and assist communication. There are enormous needs of efficient visualization design for users from diverse fields to leverage the power of data. As a result, emerging construction tools for information visualization focus on providing solutions with different aspects including expressiveness, accessibility, and efficiency. In this paper, we review existing works on declarative specifications and user interfaces for visualization construction. By summarizing their methods for producing information visualizations and efforts on improving usability, we express the design patterns in terms of a design space which describes the tools in several different aspects. We discuss how the design space can be applied to support further exploration of potential research topics in the future.

Visualization improves the user experience by presenting a pictorial view of the web information. Users are benefited in navigating and searching using the web graph visualization because the web graph presents the relationships among the web documents. To achieve this goal, the web information is processed through several steps from the web data collection to the final visualization. Researchers propose and develop various frameworks to systematically accomplish the required steps. However, to make the visualization more effective to the users, their interests or preferences need to be incorporated to the web graph generation procedures. Towards this goal, this paper presents an architecture to produce and visualize the user interest-based web graph (UIWG). The presented architecture establishes the procedures to model the user interests. To construct and manage the user interest profile, the architecture analyses both implicit and explicit user feedbacks. The architecture also presents the steps to collect and analyse the web data and finds out the appropriate positions to incorporate the user interests to the UIWG generation procedures. According to the architecture, the user interests are integrated to the filtering to remove irrelevant web documents, to the graph generation to connect the web documents according to the user, and to the clustering to group web documents that are similar to a specific user. The UIWG is visualized through an interface and the architecture describes the analysis of the user interactions to capture the implicit user feedback. The transformation of the web data throughout various steps of the architecture are also discussed in this paper.