Geo-distributed consensus protocols provide high availability and resilience for distributed database services. These protocols group nodes by their data centers to leverage the network topology that spans across multiple data centers, thereby reducing costly cross-datacenter communication. However, they still face performance and scalability challenges due to inefficient log replication mechanisms. 1) These protocols rely on the leader node in each group to perform cross-datacenter log replication, creating a single-node performance bottleneck. 2) Byzantine receivers can behave arbitrarily, forcing the group leader to send multiple log copies during replication to prevent loss, thus causing redundant transmissions. 3) Since all groups must execute these logs in the same order, synchronizations across groups are necessary to maintain consistency when multiple groups are proposing concurrently, which also slow down log replication. This paper presents MassBFT, a Byzantine fault-tolerant geo-consensus protocol that achieves high performance and scalability. We design an encoded bijective log replication to eliminate the leader bottleneck and reduce the cross-datacenter network consumption. We also propose asynchronous log ordering to eliminate synchronization across groups. Experimental results show that MassBFT is scalable, fault-tolerant, and outperforms state-of-the-art protocols with 5.49-29.96 times higher throughput under YCSB, SmallBank, and TPC-C workloads.

Earth scientists are increasingly employing time series data with multiple dimensions and high temporal resolution to study the impacts of climate and environmental changes on Earth's atmosphere, biosphere, hydrosphere, and lithosphere. However, the large number of variables and varying time scales of antecedent conditions contributing to natural phenomena hinder scientists from completing more than the most basic analyses. In this paper, we present EVis (Environmental Visualization), a new visual analytics prototype to help scientists analyze and explore recurring environmental events (e.g. rock fracture, landslides, heat waves, floods) and their relationships with high dimensional time series of continuous numeric environmental variables, such as ambient temperature and precipitation. EVis provides coordinated scatterplots, heatmaps, histograms, and RadViz for foundational analyses. These features allow users to interactively examine relationships between events and one, two, three, or more environmental variables. EVis also provides a novel visual analytics approach to allowing users to discover temporally lagging relationships related to antecedent conditions between events and multiple variables, a critical task in Earth sciences. In particular, this latter approach projects multivariate time series onto trajectories in a 2D space using RadViz, and clusters the trajectories for temporal pattern discovery. Our case studies with rock cracking data and interviews with domain experts from a range of sub-disciplines within Earth sciences illustrate the extensive applicability and usefulness of EVis.