Total plant performance evaluation based on big data: Visualization analysis of TE process

The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classificationvisualization method based on kernel principal component analysis(KPCA) and self-organizing map(SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.

Multidimensional Projections for Visual Analysis of Social Networks

Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in atgribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.

Evaluation on interactive visualization data with scatterplots

Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.

ManyInsights: A Visual Analytics Approach to Supporting Effective Insight Management

Although significant progress has been made towards effective insight discovery in visual analytics systems, there are few effective approaches for managing the large number of insights generated in visual analytics processes. This paper presents Manylnsights, a multidimensional visual analytics prototype that integrates several novel insight management approaches proposed by the authors in their previous work. These approaches include insight annotation, browsing, retrieval, organization, and association. This paper also reports a long-term case study that evaluated Manylnsights with a domain expert, realistic analytic tasks, and real datasets.