Spark (mathematics)

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

Application of Machine Learning Model on Streaming Health Data Event in Real-Time to Predict Health Status Using Spark

Abstract: In healthcare field, a huge amount of data collected in real-time by IoT systems, remote sensing device and other data collection tools brings new challenges that focus primarily on data size and the fast growth rate of such large data. Applying machine learning model on this voluminous data having varying velocity becomes extremely complex for traditional methods of data mining. To deal with this challenge, Apache Spark, a powerful big data processing tool can be used successfully for streaming data event against machine learning through in-memory and distributed computations. This work aims at developing a real-time health status prediction system with breast cancer use case using spark streaming framework with machine learning especially Decision Tree. The system focus on applying machine learning model on streaming data coming with rapid rate to predict health status based on several input variables. Based on this, the system first preprocesses the dataset and analyzes it to create an offline model for learning system, the model then deployed on system and use it in real-time to predict health status.

Model of Point Cloud Data Management System in Big Data Paradigm

Abstract: Modern geoinformation technologies for collecting and processing data, such as laser scanning or photogrammetry, can generate point clouds with billions of points. They provide abundant information that can be used for different types of analysis. Due to its characteristics, the point cloud is often viewed as a special type of geospatial data. In order to efficiently manage such volumes of data, techniques based on a computer cluster have to be used. The Apache Spark framework has proven to be a solution for efficient processing of large volumes of data. This paper thoroughly examines the representation of point cloud data type using Apache Spark constructs. The common operations over point clouds, range queries and k-nearest neighbors queries (kNN) are implemented using Apache Spark DataFrame Application Programming Interface (API). It enabled the design of point cloud related user defined types (UDT) and user defined functions (UDF). The structure of the point cloud for efficient storing in Big Data key-value stores was analyzed and described. The methods presented in this paper were compared to PostgreSQL RDBMS, and the results were discussed.

Engine spark timing control device

Abstract: AN INTERNAL COMBUSTION ENGINE HAS A VACUUM SERVO CONNECTED TO THE DISTRIBUTOR BREAKER PLATE FOR MOVING IT AS A FUNCTION OF THE CHANGE IN CARBURETOR SPARK PORT VACUUM AS CONTROLLED BY A MECHANICAL DEVICE BETWEEN THE SERVO AND SPARK PORT HAVING SINTERED METAL FLOW RESTRICTION AND A ONE-WAY CHECK VALVE IN PARALLEL FLOW CIRCUITS, THE CHECK VALVE UNSEATING DURING RAPID VEHICLE ACCELERATIONS TO QUICKLY REDUCE THE SPARD ADVANCE SETTING TO A LOWER LEVEL, FOR BETTER PERFORMANCE, THE SINTERED METAL FLOW RESTRICTION PROVIDING A DELAYED RATE OF RETURN TO AN ADVANCED SPARK TIMING SETTING DURING RELATIVELY SLOWER ACCELERATIONS, TO MINIMIZE THE EMISSION OF UNDESIRABLE EXHAUST GAS ELEMENTS.

Spark plug connector and ignition coil module for engine ignition system

Abstract: A module for use in the ignition system of a spark ignited engine combines in one unit a spark plug connector or cap and an ignition coil for converting low voltage current changes into the high voltage pulses required for firing the associated plug, the module, therefore, eliminating the need for a separate high-tension conductor of substantial length between the ignition coil and the spark plug. The module is mounted to an engine shroud directly outboard of the spark plug so as to electrically cooperate with the plug while nevertheless not relying on the plug for its structural support.