Topic
Spark (mathematics)
About: Spark (mathematics) is a research topic. Over the lifetime, 7304 publications have been published within this topic receiving 63322 citations.
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GMC1
TL;DR: In this paper, a rotary combustion engine of the eccentric rotor type includes a housing mounted spark plug which coacts with a plurality of rotor mounted electrodes to provide improved spark ignition for the various combustion chambers of the engine.
Abstract: In a preferred embodiment, a rotary combustion engine of the eccentric rotor type includes a housing mounted spark plug which coacts with a plurality of rotor mounted electrodes to provide improved spark ignition for the various combustion chambers of the engine. The rotor mounted electrodes are suitably profiled to optimize the initial spark gap for best spark forming characteristics under various operating conditions.
18 citations
Patent•
04 May 2016
TL;DR: In this article, a random forest parallelization machine studying method for big data in a Spark cloud service environment is presented, which comprises the steps that dimension reduction processing is performed on the high-dimensional big data through feature vector importance analysis, and prediction is performed by adopting a weighed voting mode; through a distributed memory management mechanism and a cloud computing platform, parallelization of random forest training process model building, single decision-making tree splitting process and prediction voting is improved.
Abstract: The invention discloses a random forest parallelization machine studying method for big data in a Spark cloud service environment. The method comprises the steps that dimension reduction processing is performed on the high-dimensional big data through feature vector importance analysis, and prediction is performed by adopting a weighed voting mode; through a distributed memory management mechanism and a cloud computing platform, parallelization of random forest training process model building, single decision-making tree splitting process and prediction voting is improved. According to the method, dimension reduction processing is performed on the high-dimensional big data through feature vector importance analysis, prediction is performed by adopting the weighed voting mode, therefore, optimization of the random forest method is achieved, and the mining effect of the random forest machine studying method on the complex big data is improved; the random forest parallelization method based on the Spark cloud platform is performed on the basis, so that the operation efficiency of the random forest machine studying method is improved.
18 citations
01 Jan 1988
TL;DR: In this article, the minimum spark ignition energy of monodisperse tetraline aerosols in a laminar gas flow was determined in terms of spark ignition frequency and for droplet sizes between 6.7 μm and 40 μm.
Abstract: An experimental investigation was carried out to determine the minimum spark ignition energy of monodisperse tetraline aerosols in a laminar gas flow. Results, presented in terms of spark ignition frequency, and for droplet sizes between 6.7 μm and 40 μm, showed: a relatively weak dependence on spark duration, an optimum spark gap width between 3 and 4 mm, an increase in minimum spark ignition energy with decreasing oxygen concentration in the gas, a decrease in spark ignition energy as the gas to fuel ratio decreased from lean to stoichiometric, a maximum ignition frequency depending on droplet size, and an optimum droplet size between 22 and 26 μm for minimum spark ignition energy. A model of droplet motion and vaporization allowed an estimation of the non-reactive fuel vapor concentration and droplet motion due to the spark discharge and was used for qualitative discussion of the experimental results.
18 citations
01 Nov 2019
TL;DR: A technical review on big data analytics using Apache Spark and how it uses in-memory computation that makes it remarkably faster as compared to other corresponding frameworks is scrutinized.
Abstract: Big data analysis has influenced the industry market. It has a significant impact on large and varied datasets to exhibit the hidden patterns and other revelations. Apache Hadoop, Apache Flink and Apache Storm are some commonly used frameworks for big data analysis. Apache Spark is a consolidated big data analytics engine and provides absolute data parallelism. This paper scrutinizes a technical review on big data analytics using Apache Spark and how it uses in-memory computation that makes it remarkably faster as compared to other corresponding frameworks. Moreover, Spark also provides exceptional batch processing and stream processing capabilities. Furthermore, it also discuses over the multithreading and concurrency capabilities of Apache Spark. The point of convergence is architecture, hardware requirements, ecosystem, use cases, features of Apache Spark and the use of Spark in emerging technologies.
18 citations
TL;DR: Experiments prove that MSJS outperforms existing parallel approaches of multiway spatial join that have been described in the literature and the formerly inefficient cascaded pairwise spatial join is transformed into a high-performance approach.
Abstract: Multiway spatial join plays an important role in GIS (Geographic Information Systems) and their applications. With the increase in spatial data volumes, the performance of multiway spatial join has encountered a computation bottleneck in the context of big data. Parallel or distributed computing platforms, such as MapReduce and Spark, are promising for resolving the intensive computing issue. Previous approaches have focused on developing single-threaded join algorithms as an optimizing and partition strategy for parallel computing. In this paper, we present an effective high-performance multiway spatial join algorithm with Spark (MSJS) to overcome the multiway spatial join bottleneck. MSJS handles the problem through cascaded pairwise join. Using the power of Spark, the formerly inefficient cascaded pairwise spatial join is transformed into a high-performance approach. Experiments using massive real-world data sets prove that MSJS outperforms existing parallel approaches of multiway spatial join that have been described in the literature.
18 citations