Data mining and visualization have attracted considerable attention in recent years for exploring and understanding big data. Machine learning focuses on developing automatic algorithms to discover patterns in large data sets. Although huge successes have been achieved, existing approaches usually assume that a ground truth is readily available. In practice, this is often not the case. In some cases, manual data annotation is required, which is tedious, onerous at scale, and highly dependent on the judgment of the human annotators. A ground truth understanding of a dataset may simply not exist, such that it can be difficult if not impossible to mathematically model the unknown types of patterns we hope to find. Even when patterns can be modeled, the intuitive explanation and validation of the models can pose a major challenge. Moreover, the underspecified complex tasks with a very high dimensional space of input variables and parameters cannot be simply handled without the inclusion of human expertise and knowledge. Compared with data mining, visualization aims to produce intuitive visual representations of data. It allows people to quickly see and interact with the patterns in the data by making effective use of their high-bandwidth visual system. As an old saying goes a picture worth a thousand words, a good visualization will significantly improve the abilities of people to understand and interpret the data and analysis results. However, the enormous amount of complex data leads to the difficulty of creating concise, discernable, and intuitive visual representations. Visual summaries of big data can still easily overwhelm users.
To make best use of the advantages and bypass the disadvantages of data mining and visualization, visual analytics has recently been introduced to facilitate analytical reasoning by interactive visual interfaces. It presents data and analysis results in context, and thus, it can provide rich evidence that supports or contradicts the analysis results, and consequently, help with data interpretation and result validation. Analysts can annotate on (e.g., place labels on) or adjust results via interactive visualizations to supervise the underlying analysis procedure, for example, and thus, gradually produce increasingly precise analysis and correct results. Visual analytics have been used in many applications to tackle various important problems, such as tackling urban issues like traffic jam and air pollution, making better diagnostic and treatment decisions, preventing threats and fraud in business, optimizing rescue efforts, forecasting severe weather conditions, and achieving situational awareness during crisis. We believe that visual analytics can enable human-centric computational intelligence by effectively integrating human knowledge and expertise into powerful computational algorithms through a high-bandwidth visual processing channel and user interactions. Despite recent impressive advances, designing developing effective visual analytics for big data still poses significant challenges for researchers and practitioners. There are still many research opportunities and open questions that we should address for creating visual analytics to enable effective human-machine intelligence.