SQPR: Stream query planning with reuse

Kalyvianaki, Evangelia; Wiesemann, Wolfram; Vu, Quang Hieu; Kühn, Daniel; Pietzuch, Peter

doi:10.1109/icde.2011.5767851

Cited by 44 publications

(46 citation statements)

References 22 publications

(26 reference statements)

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“…For this reason, various heuristic methods of operator placement have been proposed to reduce network usage and balance the load among nodes [28]. A method that formulates the operator placement problem as mixed-integer linear programming (MILP) has also been proposed [29]. However, these previous methods assume overlay (mainly peer-to-peer) networks.…”

Section: Operator Placement Of Distributed Stream Processingmentioning

confidence: 99%

“…Operator placement This step allocates the expanded HLQ to the nodes and in-vehicle networks of the target. Unlike previous distributed stream processing [28], [29], AEDSMS converts a physical structure into an architectural graph, which is an approach that is widely used to explore the design space of embedded systems [36]. Next, AEDSMS allocates the queries to the architectural graph, as described in Section 5.5.…”

Section: Design Flow Of Aedsmsmentioning

confidence: 99%

“…8 (3). After conversion into an architectural graph and by satisfying the following constraints, the queries can be allocated using previous methods of operator placement, such as those in [29].…”

Section: Operator Placement Using An Architectural Graphmentioning

confidence: 99%

See 2 more Smart Citations

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Yamaguchi

Watanabe

Sato

et al. 2017

Journal of Information Processing

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Data stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.

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Section: Operator Placement Of Distributed Stream Processingmentioning

confidence: 99%

Section: Design Flow Of Aedsmsmentioning

confidence: 99%

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In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Yamaguchi

Watanabe

Sato

et al. 2017

Journal of Information Processing

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“…A plethora of work exists on static query plan generation for DSM systems. Query planners such as SODA [16] and SQPR [13] formulate query planning and placement as an optimization problem and solve it using standard techniques like mixed integer linear programming (MILP) [12]. We can exploit these approaches to generate an initial query deployment plan from a given logical dataflow graph.…”

Section: B Simulation Experimentsmentioning

confidence: 99%

Network-aware stream query processing in mobile ad-hoc networks

O'Keeffe

Salonidis²,

Pietzuch

2015

MILCOM 2015 - 2015 IEEE Military Communications Conference

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Abstract-Many real-time decision support and sensing applications can be expressed as continuous stream queries over time-varying data streams, following a data stream management model. We consider the problem of the efficient and resilient execution of continuous stream queries in tactical edge networks formed from mobile ad-hoc networks (MANETs) with limited backend connectivity. Previous approaches for distributed stream query execution target data center environments in which networks are static, and centralized control is feasible. The distributed, bandwidth-constrained and highly dynamic nature of MANETs render such approaches insufficient-while a stream query executes in a MANET, changes in the network topology mean that any fixed query plan eventually becomes outdated.We introduce an adaptive, network-aware approach for stream query planning in MANETs, which supports both single-and multi-input windowed stream query operators. The basic idea is to increase the path diversity available when executing stream queries by replicating query operators across many nodes in the MANET. During execution, it becomes possible to dynamically switch between different operator replicas based on connectivity and other network path conditions. We evaluate our approach in emulated MANETs, showing that it can increase substantially the robustness of distributed stream query processing under mobility.

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“…In the AdaptiveCQ framework [17], for efficient processing of multiple continuous queries, the intermediate results of queries are shared at a fine level without materializing them on disk. [8] proposes a query planner for distributed stream processing systems which exploits overlaps between queries and sharing partial results with the objective of efficient resource allocation. In our approach, data sharing is implied without using techniques such as query rewriting.…”

Section: Related Workmentioning

confidence: 99%

Utility-driven data acquisition in participatory sensing

Riahi

Papaioannou

Trummer

et al. 2013

Proceedings of the 16th International Conference on Extending Database Technology

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Participatory sensing (PS) is becoming a popular data acquisition means for interesting emerging applications. However, as data queries from these applications increase, the sustainability of this platform for multiple concurrent applications is at stake. In this paper 1 , we consider the problem of efficient data acquisition in PS when queries of different types come from different applications. We effectively deal with the issues related to resource constraints, user privacy, data reliability, and uncontrolled mobility. We formulate the problem as multi-query optimization and propose efficient heuristics for its effective solution for the various query types and mixes that enable sustainable sensing. Based on simulations with real and artificial data traces, we found that our heuristic algorithms outperform baseline approaches in a multitude of settings considered.

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SQPR: Stream query planning with reuse

Cited by 44 publications

References 22 publications

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Network-aware stream query processing in mobile ad-hoc networks

Utility-driven data acquisition in participatory sensing

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