Scalable approximate query tracking over highly distributed data streams with tunable accuracy guarantees

作者：Giatrakos Nikos^*; Deligiannakis Antonios; Garofalakis Minos; Keren Daniel; Samoladas Vasilis

来源：Information Systems, 2018, 76: 59-87.

DOI：10.1016/j.is.2018.05.001

• Distributed function tracking
• Data streams
• Sampling

The recently proposed Geometric Monitoring (GM) method has provided a general tool for the distributed monitoring of arbitrary non-linear queries over streaming data observed by a collection of remote sites, with numerous practical applications. Unfortunately, GM-based techniques can suffer from serious scalability issues with increasing numbers of remote sites. In this paper, we propose novel techniques that effectively tackle the aforementioned scalability problems by exploiting a carefully designed sample of the remote sites for efficient approximate query tracking. Our novel sampling-based scheme utilizes a sample of cardinality proportional to root N (compared to N for the original GM and its variants), where N is the number of sites in the network, to perform the monitoring process. Our extensive experimental evaluation and comparative analysis over a variety of real-life data streams demonstrates that our sampling-based techniques can significantly reduce the communication cost during distributed monitoring with controllable, predefined accuracy guarantees. In that, we manage to scale the monitoring of any given non-linear function on much higher network scales which had not been reached by any GM related method or variant so far.

• 出版日期2018-7