STORM: Spatio-Temporal Online Reasoning and Management of Large Spatio-Temporal Data

被引：10

作者：

Christensen, Robert ^{[1
]}

Wang, Lu ^{[2
]}

Li, Feifei ^{[1
]}

Yi, Ke ^{[2
]}

Tang, Jun ^{[1
]}

Villa, Natalee ^{[1
]}

机构：

[1] Univ Utah, Salt Lake City, UT 84112 USA

[2] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China

来源：

SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA | 2015年

基金：

美国国家科学基金会;

关键词：

Spatial online sampling; spatial online analytics; STORM;

D O I：

10.1145/2723372.2735373

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present the STORM system to enable spatio-temporal online reasoning and management of large spatio-temporal data. STORM supports interactive spatio-temporal analytics through novel spatial online sampling techniques. Online spatio-temporal aggregation and analytics are then derived based on the online samples, where approximate answers with approximation quality guarantees can be provided immediately from the start of query execution. The quality of these online approximations improve over time. This demonstration proposal describes key ideas in the design of the STORM system, and presents the demonstration plan.

引用

页码：1111 / 1116

页数：6