Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks. Beyond predicting a correct class for each data instance, data scientists also want to understand what differentiating factors in the data have contributed to the classification during the learning process. We present a visual analytics approach to facilitate this task by revealing the RNN attention for all data instances, their temporal positions in the sequences, and the attribution of variables at each value level. We demonstrate with real-world datasets that our approach can help data scientists to understand such dynamics in deep RNNs from the training results, hence guiding their modeling process.
We summarize temporal sequence visualizations into the following categories: features in the visualization of events transfer. Alluvial diagrams @cite_10 reveal how network structures change over time. Outflow @cite_25 visualizes temporal event sequences in pathways that are similar to parallel coordinates. emphasizes links between events. For example, MatrixWave @cite_11 aligns and compares the differences in the occurrence of clickstreams augmented by event states. Liu et. al. @cite_20 presented an analytic pipeline for pattern mining, pattern pruning, and coordinated exploration between patterns and sequences. ViDX @cite_24 extends Marey's graph with a time-aware outlier-preserving design to support faults detection and troubleshooting on assembly lines. The temporal sequence design in our work attempts to visualization and compare the temporal patterns of all data instances from multiple predicted classes. We also adopt a juxtaposed design to depict the temporal changes within the user-specified range.
@cite_24 @cite_10 @cite_25 @cite_20 @cite_11
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Event sequence data analysis is common in many domains, including web and software development, transportation, and medical care. Few have investigated visualization techniques for comparative analysis of multiple event sequence datasets. Grounded in the real-world characteristics of web clickstream data, we explore visualization techniques for comparison of two clickstream datasets collected on different days or from users with different demographics. Through iterative design with web analysts, we designed MatrixWave, a matrix-based representation that allows analysts to get an overview of differences in traffic patterns and interactively explore paths through the website. We use color to encode differences and size to offer context over traffic volume. User feedback on MatrixWave is positive. Our study participants made fewer errors with MatrixWave and preferred it over the more familiar Sankey diagram. In this paper, we address the visual video summarization problem in a Bayesian framework in order to detect and describe the underlying temporal transformation symmetries in a video sequence. Given a set of time correlated frames, we attempt to extract a reduced number of image-like data structures which are semantically meaningful and that have the ability of representing the sequence evolution. To this end, we present a generative model which involves jointly the representation and the evolution of appearance. Applying Linear Dynamical System theory to this problem, we discuss how the temporal information is encoded yielding a manner of grouping the iconic representations of the video sequence in terms of invariance. The formulation of this problem is driven in terms of a probabilistic approach, which affords a measure of perceptual similarity taking both learned appearance and time evolution models into account. In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification. Given the video sequence of a person, features from each frame that are extracted from all levels of a deep convolutional network can preserve a higher spatial resolution from which we can model finer motion patterns. These low-level visual percepts are leveraged into a variant of recurrent model to characterize the temporal variation between time-steps. Features from all time-steps are then summarized using temporal pooling to produce an overall feature representation for the complete sequence. The deep convolutional network, recurrent layer, and the temporal pooling are jointly trained to extract comparable hidden-unit representations from input pair of time series to compute their corresponding similarity value. The proposed framework combines time series modeling and metric learning to jointly learn relevant features and a good similarity measure between time sequences of person. Experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose. Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.