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Hellerstein, Joseph M.; Avnur, Ron; Raman, V.

doi:10.1023/a:1009835310546

Cited by 30 publications

(2 citation statements)

References 41 publications

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“…In [6], the authors propose the SERENA system comprising of a number of services, which collectively provide predictive analytics enabling the application of predictive maintenance policies. Other example analytics models are Prism [9], CONTROL [20] and DBO [23] systems. All these systems aim at saving time in the provision of responses adopting sampling or progressive analytics.…”

Section: For Large-scale Data Managementmentioning

confidence: 99%

“…The first reason is that only EC nodes located in the desired area should respond to the task aligned with the desired spatial requirements. In addition, from the sub-set of EC nodes placed in the specific area, only nodes having in their datasets humidity values in the interval [10,20] can efficiently respond while the remaining will spend resources to return an empty set. The allocation of the desired tasks only to EC nodes that 'match' to their requirements saves resources and time involving only those that can return an efficient response based on the statistics of their datasets.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms

et al. 2021

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Section: For Large-scale Data Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms

et al. 2021

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An Incremental Refining Spatial Join Algorithm for Estimating Query Results in GIS

Bae

Alkobaisi

Leutenegger

2006

Lecture Notes in Computer Science

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Abstract. Geographic information systems (GIS) must support large georeferenced data sets. Due to the size of these data sets finding exact answers to spatial queries can be very time consuming. We present an incremental refining spatial join algorithm that can be used to report query result estimates while simultaneously provide incrementally refined confidence intervals for these estimates. Our approach allows for more interactive data exploration. While similar work has been done in relational databases, to the best of our knowledge this is the first work using this approach in GIS. We investigate different sampling methodologies and evaluate them through extensive experimental performance comparisons. Experiments on real and synthetic data show an order of magnitude response time improvement relative to the exact answer obtained when using the R-tree join.

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Optimized Analytics Query Allocation at the Edge of the Network

Karanika

Soula

Anagnostopoulos

et al. 2019

Internet and Distributed Computing Systems

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The new era of the Internet of Things (IoT) provides the space where novel applications will play a significant role in people's daily lives through the adoption of multiple services that facilitate everyday activities. The huge volumes of data produced by numerous IoT devices make the adoption of analytics imperative to produce knowledge and support efficient decision making. In this setting, one can identify two main problems, i.e., the time required to send the data to Cloud and wait for getting the final response and the distributed nature of data collection. Edge Computing (EC) can offer the necessary basis for storing locally the collected data and provide the required analytics on top of them limiting the response time. In this paper, we envision multiple edge nodes where data are stored being the subject of analytics queries. We propose a methodology for allocating queries, defined by end users or applications, to the appropriate edge nodes in order to save time and resources in the provision of responses. By adopting our scheme, we are able to ask the execution of queries only from a sub-set of the available nodes avoiding to demand processing activities that will lead to an increased response time. Our model envisions the allocation to specific epochs and manages a batch of queries at a time. We present the formulation of our problem and the proposed solution while providing results of an extensive evaluation process that reveals the pros and cons of the proposed model.

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Untitled

Cited by 30 publications

References 41 publications

Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms

Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms

An Incremental Refining Spatial Join Algorithm for Estimating Query Results in GIS

Optimized Analytics Query Allocation at the Edge of the Network

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