Details

Autor(en) / Beteiligte

• Ji, Yuanzhen
• Zhou, Hongjin
• Jerzak, Zbigniew
• Nica, Anisoara
• Hackenbroich, Gregor
• Fetzer, Christof

Titel

Quality-Driven Continuous Query Execution over Out-of-Order Data Streams

Ist Teil von

• Proceedings of the 2015 ACM SIGMOD International Conference on management of data, 2015, p.889-894

Ort / Verlag

ACM

Erscheinungsjahr

2015

Link zum Volltext

Quelle

ACM Digital Library Complete

Beschreibungen/Notizen

• Executing continuous queries over out-of-order data streams, where tuples are not ordered according to timestamps, is challenging; because high result accuracy and low result latency are two conflicting performance metrics. Although many applications allow trading exact query results for lower latency, they still expect the produced results to meet a certain quality requirement. However, none of existing disorder handling approaches have considered minimizing the result latency while meeting user-specified requirements on the quality of query results. In this demonstration, we showcase AQ-K-slack , an adaptive, buffer-based disorder handling approach, which supports executing sliding window aggregate queries over out-of-order data streams in a quality-driven manner. By adapting techniques from the field of sampling-based approximate query processing and control theory, AQ-K-slack dynamically adjusts the input buffer size at query runtime to minimize the result latency, while respecting a user-specified threshold on relative errors in produced query results. We demonstrate a prototype stream processing system, which extends SAP Event Stream Processor with the implementation of AQ-K-slack . Through an interactive interface, the audience will learn the effect of different factors, such as the aggregate function, the window specification, the result error threshold, and stream properties, on the latency and the accuracy of query results. Moreover, they can experience the effectiveness of AQ-K-slack in obtaining user-desired latency vs. result accuracy trade-offs, compared to naive disorder handling approaches that make extreme trade-offs. For instance, by scarifying 1% result accuracy, our system can reduce the result latency by 80% when compared to the state of the art.