一句话：开启概率字符串上查询的流式空间复杂度研究，用概率分布序列建模流数据不确定性。
IoT 应用的实时决策依赖对流式数据查询进行空间高效评估，而数据处理中的分类常存在不确定性。本文将这种不确定性建模为概率字符串，即有限事件集合上离散概率分布构成的序列，并研究在此类概率字符串上，不同类别查询的流式计算所需的空间复杂度。论文开启了对概率事件流查询评估的空间复杂度研究，为该问题的模型定义与复杂度分析奠定基础；摘要未列出具体查询类或具体空间界限，贡献以理论问题提出与空间复杂度分析框架为主。
Real-time decision making in IoT applications relies upon space-efficient evaluation of queries over streaming data. To model the uncertainty in the classification of data being processed, we consider the model of probabilistic strings --- sequences of discrete probability distributions over a finite set of events, and initiate the study of space complexity of streaming computation for different classes of queries over such probabilistic strings.
DOI 原文 ·
@inproceedings{paperbot5524,
title = {Space-efficient Query Evaluation over Probabilistic Event Streams},
author = {Rajeev Alur and Yu Chen and Kishor Jothimurugan and Sanjeev Khanna},
booktitle = {LICS},
year = {2020},
doi = {10.1145/3373718.3394747}
}