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Wed 21 Jun 2017 11:35 - 12:00 at Auditorium, Vertex Building - Systems and Performance Chair(s): Dan Grossman

Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE that provides natural and high-level constructs for processing streaming data. Our language has a novel integration of linguistic constructs from two distinct programming paradigms: streaming extensions of relational query languages and quantitative extensions of regular expressions. The former allows the programmer to employ relational constructs to partition the input data by keys and to integrate data streams from different sources, while the latter can be used to exploit the logical hierarchy in the input stream for modular specifications.

We first present the core language with a small set of combinators, formal semantics, and a decidable type system. We then show how to express a number of common patterns with illustrative examples. Our compilation algorithm translates the high-level query into a streaming algorithm with precise complexity bounds on per-item processing time and total memory footprint. We also show how to integrate approximation algorithms in our framework. We report on an implementation in Java, and evaluate it with respect to existing high-performance engines for processing streaming data. Our experimental evaluation shows that (1) StreamQRE allows more natural and succinct specification of queries compared to existing frameworks, (2) the throughput of our implementation is higher than comparable systems (for example, two-to-four times greater than RxJava), and (3) the approximation algorithms supported by our implementation can lead to substantial memory savings.

Wed 21 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:20 - 12:00
Systems and PerformancePLDI Research Papers at Auditorium, Vertex Building
Chair(s): Dan Grossman University of Washington
10:20
25m
Talk
Low-Synchronization, Mostly Lock-Free, Elastic Scheduling for Streaming Runtimes
PLDI Research Papers
Scott Schneider IBM Research, Kun-Lung Wu IBM Research
Media Attached
10:45
25m
Talk
Practical Partial Evaluation for High-Performance Dynamic Language Runtimes
PLDI Research Papers
Thomas Wuerthinger Oracle Labs, Christian Wimmer , Christian Humer Oracle Labs, Switzerland, Andreas Woess Oracle Labs, Lukas Stadler Oracle Labs, Austria, Chris Seaton Oracle Labs, Gilles Duboscq Oracle Labs, Doug Simon Oracle Labs, Matthias Grimmer Oracle Labs, Austria
Media Attached
11:10
25m
Talk
Responsive Parallel Computation: Bridging Competitive and Cooperative Threading
PLDI Research Papers
Stefan K. Muller , Umut A. Acar Carnegie Mellon University, Robert Harper CWI
Media Attached
11:35
25m
Talk
StreamQRE: Modular Specification and Efficient Evaluation of Quantitative Queries over Streaming Data
PLDI Research Papers
Konstantinos Mamouras University of Pennsylvania, Mukund Raghotaman University of Pennsylvania, Rajeev Alur University of Pennsylvania, Zachary G. Ives University of Pennsylvania, Sanjeev Khanna University of Pennsylvania
Media Attached