Joining Punctuated Streams

Ding, Luping; Mehta, Nishant; Rundensteiner, Elke A.; Heineman, George T.

doi:10.1007/978-3-540-24741-8_34

Cited by 42 publications

(34 citation statements)

References 15 publications

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“…Sensor network data can be modeled as data streams of facts corresponding to sensing readings [20,42]. Due to limited memory resources, we store the data streams as slidingwindow [3,8] consisting of typically the most recent tuples. In our framework, we can use temporal predicates to specify time-based windows.…”

Section: B Deductive Programming Frameworkmentioning

confidence: 99%

Deductive Framework for Programming Sensor Networks

Gupta

Zhu

2009

2009 IEEE 25th International Conference on Data Engineering

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Abstract-Developing powerful paradigms for programming sensor networks is critical to realize the full potential of sensor networks as collaborative data processing engines. In this article, we motivate and develop a deductive framework for programming sensor networks, extending the prior vision of viewing sensor network as a distributed database. The deductive programming approach is declarative, very expressive, and amenable to automatic optimizations. Such a framework allows users to program sensor network applications at a high-level without worrying about the low-level tedious details. Our system translates a given deductive program to efficient distributed code that runs on individual nodes. To facilitate the above translation, we develop techniques for distributed and asynchronous evaluation of deductive programs in sensor networks. Our techniques generalize to recursive programs without negations, arbitrary nonrecursive programs with negations, and in general to arbitrary "locally non-recursive" programs with function symbols. We present performance results on TOSSIM, a network simulator, and a small network testbed.

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Section: B Deductive Programming Frameworkmentioning

confidence: 99%

Deductive Framework for Programming Sensor Networks

Gupta

Zhu

2009

2009 IEEE 25th International Conference on Data Engineering

View full text Add to dashboard Cite

show abstract

“…This will trigger the group by operator to emit a partial result for this person. Our experimental results [4,5] show that by exploiting appropriate metadata, the join only requires near-constant memory overhead. The groupby operator is able to produce partial results at the earliest time possibly by exploiting the punctuations propagated by the upstream join.…”

Section: Wuhdpvmentioning

confidence: 99%

“…We take our adaptive punctuation-exploiting join operator PJoin [4,5] as an example. PJoin is able to utilize punctuations to remove no-longer-needed data from its join state in a timely manner, thereby reducing the memory overhead and improving the probe efficiency.…”

Section: Highly Reactive Query Operatorsmentioning

confidence: 99%

“…The STREAM system [7] cusing on formal continuous query semantics, also applies some adaptation strategies such as runtime modification of the resource allocation and scheduling policy [2]. In addition to this, our system enables finergrained intra-operator adaptivity [4,5] and distributes a query plan to run on multiple machines for scaling purposes [9].…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the Aurora system, we focus on the design of the fine-tuned individual query operators [4,5], such as the join operator with intra-operator scheduling of various related tasks including state purge and metadata propagation. Moreover, we support online migration of sub-plans not only composed of stateless operators, but also containing stateful operators, to allow for maximal online adjustments in plan execution performance [11].…”

Section: Introductionmentioning

confidence: 99%

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