Making Sense of Temporal Queries with Interactive Visualization

被引:9
|
作者
Battle, Leilani [1 ]
Fisher, Danyel [2 ]
DeLine, Robert [2 ]
Barnett, Mike [2 ]
Chandramouli, Badrish [2 ]
Goldstein, Jonathan [2 ]
机构
[1] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[2] Microsoft Res, Redmond, WA 98052 USA
关键词
Streaming Data; Data Visualization; Data Analysts; H.5.m. Information interfaces and presentation (e.g; HCI):; Miscellaneous;
D O I
10.1145/2858036.2858408
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As real-time monitoring and analysis become increasingly important, researchers and developers turn to data stream management systems (DSMS's) for fast, efficient ways to pose temporal queries over their datasets. However, these systems are inherently complex, and even database experts find it difficult to understand the behavior of DSMS queries. To help analysts better understand these temporal queries, we developed StreamTrace, an interactive visualization tool that breaks down how a temporal query processes a given dataset, step-by-step. The design of StreamTrace is based on input from expert DSMS users; we evaluated the system with a lab study of programmers who were new to streaming queries. Results from the study demonstrate that StreamTrace can help users to verify that queries behave as expected and to isolate the regions of a query that may be causing unexpected results.
引用
收藏
页码:5433 / 5443
页数:11
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