Data Analysis Tools Using JAVA/Internet Technology at Arnold Engineering Development Center

1999 ◽  
Author(s):  
Danna Pemberton
Keyword(s):  
Data Analysis ◽  
Internet Technology ◽  
Engineering Development ◽  
Analysis Tools ◽  
Development Center

scholarly journals Implementation of Parallel Algorithm Technology for Time Series Data Mining

2021 ◽  
Vol 2066 (1) ◽  
pp. 012043
Author(s):  
Wei Wang ◽  
Xiaohui Hu ◽  
Mingye Wang ◽  
Yao Du
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Data Analysis ◽  
Parallel Algorithm ◽  
Time Series Data ◽  
Internet Technology ◽  
Series Data ◽  
Data Set ◽  
Time Series Data Mining ◽  
Analysis Tools

Abstract With the rapid development of computer technology, Internet technology and artificial intelligence technology, the amount of global data has exploded. However, the single-machine serial mode of traditional data mining cannot be directly transplanted to the cloud platform. Only by parallelizing and improving many classic data mining algorithms can the cloud computing platform and data mining be effectively combined. Therefore, it is of great significance to the research and implementation of parallel algorithm technology for time series data mining. The purpose of this paper is to study the research and implementation of parallel algorithm technology for time series data mining. This paper adopts the method of literature data, mathematical statistics, logic analysis and other research methods to study the parallel algorithm technology research and realization of time series data mining, mainly to make useful explorations of time series data mining and visualization technology. It embodies the design ideas of big data analysis tools, and finally reflects the power and market value of data analysis tools through the display of the platform. Research shows that running in the same data set and the same experimental environment, the improved parallel collaborative filtering algorithm ACF in this paper has higher time running efficiency than the parallel algorithm MCF based on the cooccurrence matrix, and in the case of larger data sets, the more obvious the time difference.

PENGARUH KOMPENSASI, DISIPLIN KERJA DAN LINGKUNGAN KERJA TERHADAP KINERJA PEGAWAI PADA PUSAT PENGEMBANGAN SUMBER DAYA MANUSIA REGIONAL MAKASSAR

2020 ◽  
Vol 2 (1) ◽  
pp. 21-27
Author(s):  
Auliyah Fajar ◽  
Hasanuddin Remmang ◽  
Firman Menne
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Human Resources ◽  
Work Environment ◽  
Sampling Method ◽  
Employee Performance ◽  
Human Resources Development ◽  
Analysis Technique ◽  
Data Collection Technique ◽  
Development Center

Penelitian ini bertujuan untuk mengetahui dan menganalisis pengaruh kompensasi, disiplin kerja dan lingkungan kerja ter-hadap kinerja pegawai pada Pusat Pengembangan Sumber Daya Manusia Regional Makassar. Metode pengambilan sampel menggunakan sampling jenuh (sensus) terhadap seluruh pegawai yang berjumlah 49 orang. Teknik pengumpulan data yang digunakan adalah kuesioner dan teknik analisis data yang digunakan adalah regresi linear berganda dengan SPSS. Hasil penelitian ini menunjukkan bahwa kompensasi berpengaruh positif dan tidak signifikan terhadap kinerja pegawai, disiplin kerja berpengaruh positif dan signifikan terhadap kinerja pegawai dan Lingkungan kerja berpengaruh positif dan signifikan terhadap kinerja pegawai PPSDM Regional Makassar.   This study aims to determine and analyze the effect of compensation, work discipline and work environment on employee performance at the Makassar Regional Human Resources Development Center. The sampling method uses saturated sampling (census) of all 49 employees. The data collection technique used was a questionnaire and the data analysis technique used was multiple linear regression with SPSS. The results of this study indicate that compensation has a positive and not significant effect on employee performance, work discipline has a positive and significant effect on employee performance and The work environment has a positive and significant effect on employee performance PPSDM Regional Makassar.

scholarly journals Open Plot Project: an open-source toolkit for 3-D structural data analysis

Solid Earth ◽  
2011 ◽  
Vol 2 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. Tavani ◽  
P. Arbues ◽  
M. Snidero ◽  
N. Carrera ◽  
J. A. Muñoz
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Source Code ◽  
Structural Data ◽  
Geological Modelling ◽  
Analysis Tools ◽  
Transect Analysis ◽  
Selection Of

Abstract. In this work we present the Open Plot Project, an open-source software for structural data analysis, including a 3-D environment. The software includes many classical functionalities of structural data analysis tools, like stereoplot, contouring, tensorial regression, scatterplots, histograms and transect analysis. In addition, efficient filtering tools are present allowing the selection of data according to their attributes, including spatial distribution and orientation. This first alpha release represents a stand-alone toolkit for structural data analysis. The presence of a 3-D environment with digitalising tools allows the integration of structural data with information extracted from georeferenced images to produce structurally validated dip domains. This, coupled with many import/export facilities, allows easy incorporation of structural analyses in workflows for 3-D geological modelling. Accordingly, Open Plot Project also candidates as a structural add-on for 3-D geological modelling software. The software (for both Windows and Linux O.S.), the User Manual, a set of example movies (complementary to the User Manual), and the source code are provided as Supplement. We intend the publication of the source code to set the foundation for free, public software that, hopefully, the structural geologists' community will use, modify, and implement. The creation of additional public controls/tools is strongly encouraged.

scholarly journals Spaceborne Differential SAR Interferometry: Data Analysis Tools for Deformation Measurement

2011 ◽  
Vol 3 (2) ◽  
pp. 305-318 ◽  
Author(s):  
Michele Crosetto ◽  
Oriol Monserrat ◽  
María Cuevas ◽  
Bruno Crippa
Keyword(s):  
Data Analysis ◽  
Deformation Measurement ◽  
Sar Interferometry ◽  
Analysis Tools ◽  
Differential Sar Interferometry

Advancements in capturing and mining mass spectrometry data are transforming natural products research

2021 ◽  
Author(s):  
Scott A. Jarmusch ◽  
Justin J. J. van der Hooft ◽  
Pieter C. Dorrestein ◽  
Alan K. Jarmusch
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Natural Products ◽  
Data Analysis ◽  
Community Participation ◽  
Mass Spectrometry Data ◽  
Metabolomics Data ◽  
Analysis Tools ◽  
Public Data ◽  
Potential Use

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

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