No abstract

The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi-and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

Abstract-The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user-and context-dependent visual exploration systems.

This paper describes a synthesis method and adsorption properties of geopolymer microspheres toward strontium ions; first, it was prepared by a dispersion–pelletizing–solidification (DPS) method followed by transformation into NaA zeolite microspheres (about 100 μm) through an in situ heat curing process. The adsorption experiments were investigated and the experimental data were fitted well by the pseudo-second-order kinetic model and Freundlich isotherm model. Loading experiments were performed by batch and column process techniques. The maximum adsorption capacity in the batch process was assigned to be 106.28 mg/g, the zeolite microspheres’ adsorption of strontium ions reached adsorption equilibrium in approximately 15 min. In the dynamic column, the most suitable flow rate was found to be 4 mL/min; this was higher compared with other sorbents with the same particle size. Moreover, the zeolite microspheres have a good dynamic separation effect, the concentration of the outlet Sr­(II) ions from the column began to rise after 18 h with a bed height of 1.5 cm. The competitive adsorption capabilities are investigated and have the following order Na+ < Mg2+ < K+ < Ca2+, indicating that this adsorbent has a good adsorption effect in real seawater. Through the analysis of the solution after adsorption, the process is not only chemical adsorption but also ion exchange. The used adsorbents could be easily regenerated using 0.05 mol/L EDTA-2Na solution. This result showed that NaA zeolite microspheres are convenient and low-cost adsorbents for the removal of Sr­(II) from liquid wastes.

Data analysis often involves finding models that can explain patterns in data, and reduce possibly large data sets to more compact model‐based representations. In Statistics, many methods are available to compute model information. Among others, regression models are widely used to explain data. However, regression analysis typically searches for the best model based on the global distribution of data. On the other hand, a data set may be partitioned into subsets, each requiring individual models. While automatic data subsetting methods exist, these often require parameters or domain knowledge to work with. We propose a system for visual‐interactive regression analysis for scatter plot data, supporting both global and local regression modeling. We introduce a novel regression lens concept, allowing a user to interactively select a portion of data, on which regression analysis is run in interactive time. The lens gives encompassing visual feedback on the quality of candidate models as it is interactively navigated across the input data. While our regression lens can be used for fully interactive modeling, we also provide user guidance suggesting appropriate models and data subsets, by means of regression quality scores. We show, by means of use cases, that our regression lens is an effective tool for user‐driven regression modeling and supports model understanding.