Fuzzy clusters, where ambiguous samples belong to multiple clusters, are common in real-world applications. Analyzing such ambiguous samples in large-scale datasets is crucial for practical applications, such as diagnosing machine learning models. A promising method to support such analysis is through hierarchical cluster-aware grid visualizations, which offer high space efficiency and clear cluster perception. However, existing cluster-aware grid layout methods cannot clarify ambiguity among fuzzy clusters, which limits their effectiveness in fuzzy cluster analysis. To tackle this issue, we introduce a hierarchical fuzzy-cluster-aware grid layout method that supports hierarchical exploration of large-scale datasets. Throughout the hierarchical exploration, it is crucial to facilitate fuzzy cluster analysis while maintaining visual continuity for users. To achieve this, we propose a two-step optimization strategy for enhancing cluster perception, clarifying ambiguity, and preserving stability during the exploration. The first step is to create cluster-aware partitions, where each partition corresponds to a cluster. This step focuses on enhancing cluster perception and maintaining the previous shapes and positions of clusters to preserve stability at the cluster level. The second step is to generate a grid layout for each partition. In addition to placing similar samples together, this step also places ambiguous samples near the boundaries to clarify ambiguity and reveal the root causes of their occurrences and maintains the relative positions of the samples in the same cluster to preserve stability at the sample level. Several quantitative experiments and a use case are conducted to demonstrate the effectiveness and usefulness of our method in analyzing large-scale datasets, especially in fuzzy cluster analysis.

Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face difficulties when dealing with the inherent cluster structures within the data. To address this issue, we propose a cluster-aware grid layout method that aims to better preserve cluster structures by simultaneously considering proximity, compactness, and convexity in the optimization process. Our method utilizes a hybrid optimization strategy that consists of two phases. The global phase aims to balance proximity and compactness within each cluster, while the local phase ensures the convexity of cluster shapes. We evaluate the proposed grid layout method through a series of quantitative experiments and two use cases, demonstrating its effectiveness in preserving cluster structures and facilitating analysis tasks.

Based on the SEIR model, which takes into account prevention and control measures, prevention and control awareness, and economic level and medical level indicators, this paper proposes an infectious disease model of “susceptible‐exposed‐infected‐removed‐asymptomatic‐isolated” (short for SEIR‐AQ) to assess and predict the development of the COVID‐19 pandemic with different prevention and control strategies. The kinetic parameters of the SEIR‐AQ model were obtained by fitting, and the parameters of the SEIR‐AQ model were solved through the Euler method. Furthermore, the effects of different countries’ prevention and control strategies on the number of infections, the proportion of isolation, the number of deaths, and the number of recoveries were also simulated. The theoretical analysis showed that measures such as isolation for prevention and control and medical tracking isolation had a significant inhibitory effect on the development of the COVID‐19 pandemic, among which stratified treatment and enhanced awareness played a key role in the rapid regression of the peak of COVID‐19‐infected patients. Conclusion of the Simulation. The SEIR‐AQ model can be used to evaluate the development status of the COVID‐19 epidemic and has some theoretical value for the prediction of COVID‐19.

Hybrid Transactional and Analytical Processing (HTAP) databases require processing transactional and analytical queries in isolation to remove the interference between them. To achieve this, it is necessary to maintain different replicas of data specified for the two types of queries. However, it is challenging to provide a consistent view for distributed replicas within a storage system, where analytical requests can efficiently read consistent and fresh data from transactional workloads at scale and with high availability. To meet this challenge, we propose extending replicated state machine-based consensus algorithms to provide consistent replicas for HTAP workloads. Based on this novel idea, we present a Raft-based HTAP database: TiDB. In the database, we design a multi-Raft storage system which consists of a row store and a column store. The row store is built based on the Raft algorithm. It is scalable to materialize updates from transactional requests with high availability. In particular, it asynchronously replicates Raft logs to learners which transform row format to column format for tuples, forming a real-time updatable column store. This column store allows analytical queries to efficiently read fresh and consistent data with strong isolation from transactions on the row store. Based on this storage system, we build an SQL engine to process large-scale distributed transactions and expensive analytical queries. The SQL engine optimally accesses row-format and column-format replicas of data. We also include a powerful analysis engine, TiSpark, to help TiDB connect to the Hadoop ecosystem. Comprehensive experiments show that TiDB achieves isolated high performance under CH-benCHmark, a benchmark focusing on HTAP workloads.

During the period of processing and analysis of on-orbit radiometric calibration data received from a space-based infrared camera launched recently, three practical issues were extracted and resolved at the data level, specifically how to exclude the invalid calibration data; how to determine the appropriate on-orbit decontamination time in the presence of increasing contaminants inside the camera system; and how to calibrate images without suitable calibration coefficients. Three major types of invalid data were summarized according to their appearances and possible causes after analyzing data from many on-orbit calibration tests, and the targeted filtering strategies were proposed with proven excellent performance in practice. A two-term exponential model was established to characterize the observed camera degradation by dividing the corresponding digital number into the blackbody and non-blackbody terms. Based on the model, degradation trends of the camera response, radiance resolution, and signal-to-noise ratio were estimated, respectively, to help determine the contamination tolerance from different aspects. Attempts were made to predict calibration coefficients by the pixelwise degradation models, and then they were applied to image calibration. Results show that the predicted coefficients can effectively compensate the calibration errors due to degradation and can be treated as an alternative if time-matched coefficients are unavailable.