Load Forecasting Research Based on High Performance Intelligent Data Processing of Power Big Data

2018 ◽  
Author(s):  
Menghan Xu ◽  
Gaopan Huang ◽  
Mingming Zhang ◽  
Peng Cui ◽  
Chong Wang
Keyword(s):  
Big Data ◽  
Data Processing ◽  
High Performance ◽  
Load Forecasting ◽  
Intelligent Data Processing

High-Performance Computing for Big Data Processing

2018 ◽  
Vol 88 ◽  
pp. 693-695 ◽  
Author(s):  
Yulei Wu ◽  
Yang Xiang ◽  
Jingguo Ge ◽  
Peter Muller
Keyword(s):  
Big Data ◽  
Data Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Big Data Processing ◽  
Performance Computing

scholarly journals High-Performance Geospatial Big Data Processing System Based on MapReduce

2018 ◽  
Vol 7 (10) ◽  
pp. 399 ◽  
Author(s):  
Junghee Jo ◽  
Kang-Woo Lee
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Data Processing ◽  
Spatial Data ◽  
High Performance ◽  
Rapid Development ◽  
Processing System ◽  
Data Processing System ◽  
Seamless Integration ◽  
Big Data Processing

With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index.

scholarly journals Upgrading a high performance computing environment for massive data processing

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Lucas M. Ponce ◽  
Walter dos Santos ◽  
Wagner Meira ◽  
Dorgival Guedes ◽  
Daniele Lezzi ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Data Access ◽  
Massive Data ◽  
Analysis Tool ◽  
Data Framework ◽  
Performance Computing ◽  
Massive Data Processing

Abstract High-performance computing (HPC) and massive data processing (Big Data) are two trends that are beginning to converge. In that process, aspects of hardware architectures, systems support and programming paradigms are being revisited from both perspectives. This paper presents our experience on this path of convergence with the proposal of a framework that addresses some of the programming issues derived from such integration. Our contribution is the development of an integrated environment that integretes (i) COMPSs, a programming framework for the development and execution of parallel applications for distributed infrastructures; (ii) Lemonade, a data mining and analysis tool; and (iii) HDFS, the most widely used distributed file system for Big Data systems. To validate our framework, we used Lemonade to create COMPSs applications that access data through HDFS, and compared them with equivalent applications built with Spark, a popular Big Data framework. The results show that the HDFS integration benefits COMPSs by simplifying data access and by rearranging data transfer, reducing execution time. The integration with Lemonade facilitates COMPSs’s use and may help its popularization in the Data Science community, by providing efficient algorithm implementations for experts from the data domain that want to develop applications with a higher level abstraction.

scholarly journals A novel approach for big data processing using message passing interface based on memory mapping

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Saad Ahmed Dheyab ◽  
Mohammed Najm Abdullah ◽  
Buthainah Fahran Abed
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Memory Storage ◽  
Big Data Processing ◽  
Memory Space ◽  
Novel Approach ◽  
Memory Mapping

AbstractThe analysis and processing of big data are one of the most important challenges that researchers are working on to find the best approaches to handle it with high performance, low cost and high accuracy. In this paper, a novel approach for big data processing and management was proposed that differed from the existing ones; the proposed method employs not only the memory space to reads and handle big data, it also uses space of memory-mapped extended from memory storage. From a methodological viewpoint, the novelty of this paper is the segmentation stage of big data using memory mapping and broadcasting all segments to a number of processors using a parallel message passing interface. From an application viewpoint, the paper presents a high-performance approach based on a homogenous network which works parallelly to encrypt-decrypt big data using AES algorithm. This approach can be done on Windows Operating System using .NET libraries.

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