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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TL;DR: In this article, a very low-energy capacitive spark ignition system was developed to produce short sparks with fixed lengths of 1-2 mm, and the system was used to perform spark ignition tests using a range of spark energies in lean hydrogen-oxygen-argon test mixtures used in aviation safety testing.
59 citations
01 Oct 2014
TL;DR: DBStream is described, which is an SQL-based system that explicitly supports incremental queries for rolling data analysis and is presented a performance comparison of DBStream with a parallel data processing engine (Spark), showing that, in some scenarios, a single DBStream node can outperform a cluster of ten Spark nodes on rolling network monitoring workloads.
Abstract: The complexity of the Internet has rapidly increased, making it more important and challenging to design scalable network monitoring tools. Network monitoring typically requires rolling data analysis, i.e., continuously and incrementally updating (rolling-over) various reports and statistics over highvolume data streams. In this paper, we describe DBStream, which is an SQL-based system that explicitly supports incremental queries for rolling data analysis. We also present a performance comparison of DBStream with a parallel data processing engine (Spark), showing that, in some scenarios, a single DBStream node can outperform a cluster of ten Spark nodes on rolling network monitoring workloads. Although our performance evaluation is based on network monitoring data, our results can be generalized to other big data problems with high volume and velocity.
59 citations
59 citations
29 Oct 2015
TL;DR: This work studies the state-of-the-art in distributed and parallel computing, storage, query and ingestion methods, and evaluates tools for periodical and real-time analysis of heterogeneous data, and introduces a Big Data cloud platform with ingestion, analysis, storage and data query APIs.
Abstract: Demand for new efficient methods for processing large-scale heterogeneous data in real-time is growing. Currently, one key challenge in Big Data is performing low-latency analysis with real-time data. In vehicle traffic, continuous high speed data streams generate large data volumes. Harnessing new technologies is required to benefit from all the potential this data withholds. This work studies the state-of-the-art in distributed and parallel computing, storage, query and ingestion methods, and evaluates tools for periodical and real-time analysis of heterogeneous data. We also introduce a Big Data cloud platform with ingestion, analysis, storage and data query APIs to provide programmable environment for analytics system development and evaluation.
59 citations