Nostradamus: A novel event propagation prediction approach with spatio-temporal characteristics in non-Euclidean space.

The prediction of event propagation has received extensive attention from the knowledge discovery community for applications such as virus spread analytics, social network analysis, earthquake location prediction, and typhoon tracking. The data describing these phenomena are multidimensional asynchronous event data that affect each other and show complex dynamic patterns in the continuous-time domain. Unlike the discrete characteristics formed by sampling at equal intervals of asynchronous time series, the timestamps of asynchronous events are in the continuous-time field.

[Microbial Community Structure for Sewage Wastewater Treatment Plants in Winter].

Constructing an effective control strategy for the daily operation of sewage treatment plants daily operation is an important criterion for the removal performances of nutrient stable in winter. To investigate microbial community structure, functional groups, and relationships between population dynamics and effluent variation, activated sludge obtained from 4 plants, which were running stably in a low temperature period (8-15℃) in northern China, were sampled. The high-throughput sequencing results indicated that the microbial community had good richness in the low temperature period during which the abundance of Actinobacteria increased.

MonkeyKing: Adaptive Parameter Tuning on Big Data Platforms with Deep Reinforcement Learning.

Choosing the right parameter configurations for recurring jobs running on big data analytics platforms is difficult because there can be hundreds of possible parameter configurations to pick from. Even the selection of parameter configurations is based on different types of applications and user requirements. The difference between the best configuration and the worst configuration can have a performance impact of more than 10 times.