StreamQRE: modular specification and efficient evaluation of quantitative queries over streaming data

Mamouras, Konstantinos; Raghothaman, Mukund; Alur, Rajeev; Khanna, Sanjeev

doi:10.1145/3140587.3062369

Cited by 15 publications

(12 citation statements)

References 35 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The StreamQRE language [18], [6] permits such a declarative way of programming. StreamQRE (pronounced 'stream query') allows the developer to create Quantitative Regular Expressions (QREs), which are a quantitative extension of regular expressions.…”

Section: Detectionmentioning

confidence: 99%

“…The input stream for this stage consists of items of the form shown in (6). In this state, we first calculate the sum of interval lengths over a sliding window that consists of 10 intervals, and we annotate the stream with this information (see Fig.…”

Section: Stage 1: Sudden Onset and Short Intervalsmentioning

confidence: 99%

“…To specify the decision logic based on computing quantitative summaries of data streams we advocate Quantitative Regular Expressions (QREs) [5], [6]. The language allows the computation to be expressed as a streaming composition of stages.…”

Section: Introductionmentioning

confidence: 99%

“…The QRE compiler translates a high-level query into a streaming algorithm with precise complexity bounds on per-item processing time and total memory footprint. The StreamQRE library, an implementation in Java, has been shown experimentally to have superior performance compared to other existing high-performance engines for processing streaming data [6]. This experimental evaluation involved workloads that are representative of clickstream analysis (Yahoo streaming benchmark [7]) and real-time analytics for business event streams (NEXMark benchmark [8]).…”

Section: Introductionmentioning

confidence: 99%

“…The QRE compiler then generates the low-level implementation whose space complexity and per-item processing time complexity are constant -that is, independent of the number of samples processed so far (see Section 4 of [6]). Furthermore, we show how the QRE compiler can statically compute an upper bound on the cost of processing each item, where the cost can be, for example, the energy consumption on a specific platform.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Real-Time Decision Policies With Predictable Performance

et al. 2018

Self Cite

View full text Add to dashboard Cite

As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case performance, on the basis of which hardware can be designed and algorithms can be tuned. Abstract-As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case per...

show abstract

Section: Detectionmentioning

confidence: 99%

Section: Stage 1: Sudden Onset and Short Intervalsmentioning

confidence: 99%