Spark (mathematics)

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

Metatools for knowledge acquisition

Abstract: Four prototype metatools, Protege, Dots, Dash, and Spark, which researchers are using to experiment with the automatic generation of knowledge-acquisition tools, are discussed. Protege and Dots are stand-alone metatools; Dash and Spark are subsystems. Dash is part of Protege II, a design environment for knowledge-based systems. Spark is part of the Spark, Burn, and Firefighter framework for the design of application systems. The two stand-alone tools, their environments, and their subsystems are compared. Protege demonstrates how one can instantiate knowledge-acquisition tools from a description of a problem-solving method. Dots, on the other hand, lets one design knowledge-acquisition tools for many applications. Spark, Burn, and Firefighter are similar to Protege II in that they emphasize developing problem-solving methods from reusable components, although Spark, Burn and Firefighter associate a knowledge-acquisition tool with each method in the library. Protege II uses Dash to generate knowledge-acquisition tools from domain ontologies. >

Effective Prediction of Missing Data on Apache Spark over Multivariable Time Series

Abstract: More massive volume of data are generated in many areas than ever before. However, the missing of some values in collected data always occurs in practice and challenges extracting maximal value from these large scale data sets. Nevertheless, in multivariable time series, most of the existing methods either might be infeasible or could be inefficient to predict the missing data. In this paper, we have taken up the challenge of missing data prediction in multivariable time series by employing improved matrix factorization techniques. Our approaches are optimally designed to largely utilize both the internal patterns of each time series and the information of time series across multiple sources. Based on the idea, we have imposed three different regularization terms to constrain the objective functions of matrix factorization and built five corresponding models. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction in multivariable time series. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large scale multivariable time series.

Observations on Pulsed Spark Chambers

Abstract: The spark formation time in rare‐gas pulsed spark chambers was investigated as a function of the pulse voltage under various conditions of electrodes, gap lengths, gases, delay times, and clearing fields. An explanation of these formation times was found in terms of a simple process of electron multiplication leading to rapid gas breakdown. Notes on the construction of a thin foil spark chamber and on thyratron pulsers for use with spark chambers are also given.