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Spark (mathematics)

About: Spark (mathematics) is a research topic. Over the lifetime, 7304 publications have been published within this topic receiving 63322 citations.


Papers
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Proceedings ArticleDOI
01 Mar 1999

48 citations

BookDOI
01 Jan 2015
TL;DR: Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, such as HDFS, Avro, Parquet, Ka a, Cassandra, HBase, Mesos, and so on.
Abstract: What’s more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, such as HDFS, Avro, Parquet, Ka a, Cassandra, HBase, Mesos, and so on. It also provides an introduction to machine learning and graph concepts. So the book is self-suffi cient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to have is some programming knowledge in any language.

48 citations

Patent
John R. Frus1, Michael J. Cochran1
12 Jul 1996
TL;DR: In this paper, an apparatus for controllably generating sparks is provided, which includes a spark generating device, at least two output stages connected to the spark generator, a means for charging energy storage devices in the output stages and at least partially isolating each of the devices from the other output stages, and a logic circuit for selectively triggering the outputs to generate a spark.
Abstract: An apparatus for controllably generating sparks is provided. The apparatus includes a spark generating device; at least two output stages connected to the spark generating device; means for charging energy storage devices in the output stages and at least partially isolating each of the energy storage devices from the energy storage devices of the other output stages; and, a logic circuit for selectively triggering the output stages to generate a spark. Each of the output stages preferably includes: (1) an energy storage device to store the energy; (2) a controlled switch for selectively discharging the energy storage device; and (3) a network for transferring the energy discharged by the energy storage device to the spark generating device. In accordance with one aspect of the invention, the logic circuit, which is connected to the controlled switches of the output stages, can be configured to fire the stages at different times, in different orders, and/or in different combinations to provide the spark generating device with output pulses having substantially any desired waveshape and energy level to thereby produce a spark having substantially any desired energy level and plume shape at the spark generating device to suit any application.

47 citations

Proceedings ArticleDOI
01 Jun 2017
TL;DR: An approach to detect abnormality and analyzes root causes using Spark log files is proposed and results show that the proposed approach is accurate on detecting abnormal tasks as well as finding the root causes.
Abstract: -Application delays caused by abnormal tasks arecommon problems in big data computing frameworks. Anabnormal task in Spark, which may run slowly withouterror or warning logs, not only reduces its resident node’sperformance, but also affects other nodes’ efficiency.Spark log files report neither root causes of abnormal tasks,nor where and when abnormal scenarios happen. AlthoughSpark provides a “speculation” mechanism to detect stragglertasks, it can only detect tailed stragglers in each stage. Sincethe root causes of abnormal happening are complicated, thereare no effective ways to detect root causes.This paper proposes an approach to detect abnormality andanalyzes root causes using Spark log files. Unlike commononline monitoring or analysis tools, our approach is a pureoff-line method that can analyze abnormality accurately. Ourapproach consists of four steps. First, a parser preprocessesraw log files to generate structured log data. Second, ineach stage of Spark application, we choose features relatedto execution time and data locality of each task, as well asmemory usage and garbage collection of each node. Third,based on the selected features, we detect where and whenabnormalities happen. Finally, we analyze the problems usingweighted factors to decide the probability of root causes. In thispaper, we consider four potential root causes of abnormalities,which include CPU, memory, network, and disk. The proposedmethod has been tested on real-world Spark benchmarks.To simulate various scenario of root causes, we conductedinterference injections related to CPU, memory, network,and Disk. Our experimental results show that the proposedapproach is accurate on detecting abnormal tasks as well asfinding the root causes

47 citations

Patent
27 Feb 1995
TL;DR: An improved spark ignition engine system produces a large continuous, centrally directed, flow coupled ignition spark discharge through combustion chamber (1), piston (4), inlet system (28/29), spark plug (5), and ignition discharge (26) design, and through the location and orientation, with respect to the mixture flow field, of a special design firing end and gap (7/9) of a spark plug fired with a spark discharge of hundreds of watts of power for hundreds of microseconds without spark segmentation or spark break-up by the flow field of up to about 20 m
Abstract: An improved spark ignition engine system producing a large continuous, centrally directed, flow coupled ignition spark discharge through combustion chamber (1), piston (4), inlet system (28/29), spark plug (5), and ignition spark discharge (26) design, and through the location and orientation, with respect to the mixture flow field, of a special design firing end and gap (7/9) of a spark plug fired with a spark discharge of hundreds of watts of power for hundreds of microseconds without spark segmentation or spark break-up by the flow field of up to about 20 m/sec flow velocity, with bulk flow occurring at the spark plug site at most engine speeds including low speeds to produce a very large centrally directed spark-initial flame front kernel which allows for substantial dilution of the mixture and significant reduction in engine cycle-to-cycle variation under most operating conditions of the engine including low speed light load.

47 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202210
2021429
2020525
2019661
2018758
2017683