Boduo Li

TL;DR: This paper presents the PODS system that supports stream processing for uncertain data naturally captured using continuous random variables, and develops evaluation techniques for complex relational operators, i.e., aggregates and joins, by exploring advanced statistical theory and approximation.

TL;DR: In this paper, the authors present a data stream system that captures data uncertainty from data collection to query processing to final result generation using probabilistic modeling and inference to generate uncertainty description for raw data, and then a suite of statistical techniques to capture changes of uncertainty as data propagates through query operators.

TL;DR: Sparkler provides a convenient and efficient extension to Spark for solving matrix factorization problems on very large datasets, and is faster than Spark by 4x to 21x, with bigger advantages for larger problems.

TL;DR: Results from real-world workloads show that the techniques implemented in Incremental Hadoop, reduce its latency from tens of seconds to sub-second, with 2x-5x increase in throughput, and outperforms state-of-the-art distributed stream systems, Storm and Spark Streaming, when combining latency and throughput.