Facies classification using machine learning

2016 ◽  
Vol 35 (10) ◽  
pp. 906-909 ◽  
Author(s):  
Brendon Hall
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Data Sets ◽  
Complex Data ◽  
Facies Classification ◽  
Large Complex ◽  
Complex Data Sets ◽  
Software Platforms ◽  
Tools And Techniques

There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist's toolbox, much of which used to be only available in proprietary (and expensive) software platforms.

scholarly journals Using Hadoop Technology to Overcome Big Data Problems by Choosing Proposed Cost-efficient Scheduler Algorithm for Heterogeneous Hadoop System (BD3)

2020 ◽  
pp. 58-84
Author(s):  
Abou_el_ela Abdou Hussein
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
Database Management System ◽  
Data Sets ◽  
Complex Data ◽  
Daily Data ◽  
Complex Data Sets ◽  
Cost Efficient ◽  
Hadoop System

Day by day advanced web technologies have led to tremendous growth amount of daily data generated volumes. This mountain of huge and spread data sets leads to phenomenon that called big data which is a collection of massive, heterogeneous, unstructured, enormous and complex data sets. Big Data life cycle could be represented as, Collecting (capture), storing, distribute, manipulating, interpreting, analyzing, investigate and visualizing big data. Traditional techniques as Relational Database Management System (RDBMS) couldn’t handle big data because it has its own limitations, so Advancement in computing architecture is required to handle both the data storage requisites and the weighty processing needed to analyze huge volumes and variety of data economically. There are many technologies manipulating a big data, one of them is hadoop. Hadoop could be understand as an open source spread data processing that is one of the prominent and well known solutions to overcome handling big data problem. Apache Hadoop was based on Google File System and Map Reduce programming paradigm. Through this paper we dived to search for all big data characteristics starting from first three V's that have been extended during time through researches to be more than fifty six V's and making comparisons between researchers to reach to best representation and the precise clarification of all big data V’s characteristics. We highlight the challenges that face big data processing and how to overcome these challenges using Hadoop and its use in processing big data sets as a solution for resolving various problems in a distributed cloud based environment. This paper mainly focuses on different components of hadoop like Hive, Pig, and Hbase, etc. Also we institutes absolute description of Hadoop Pros and cons and improvements to face hadoop problems by choosing proposed Cost-efficient Scheduler Algorithm for heterogeneous Hadoop system.

Introduction to Big Data and Business Analytics

2022 ◽  
pp. 67-76
Author(s):  
Dineshkumar Bhagwandas Vaghela
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sets ◽  
Complex Data ◽  
Business Analytics ◽  
Database Applications ◽  
Complex Data Sets ◽  
History Of ◽  
Rapid Generation

The term big data has come due to rapid generation of data in various organizations. In big data, the big is the buzzword. Here the data are so large and complex that the traditional database applications are not able to process (i.e., they are inadequate to deal with such volume of data). Usually the big data are described by 5Vs (volume, velocity, variety, variability, veracity). The big data can be structured, semi-structured, or unstructured. Big data analytics is the process to uncover hidden patterns, unknown correlations, predict the future values from large and complex data sets. In this chapter, the following topics will be covered more in detail. History of big data and business analytics, big data analytics technologies and tools, and big data analytics uses and challenges.

Bayesian Modelling for Machine Learning

2005 ◽  
pp. 236-242
Author(s):  
Paul Rippon ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Bayesian Learning ◽  
Data Sets ◽  
Complex Data ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Complex Data Sets ◽  
Complex Phenomena ◽  
Learning Data

Learning algorithms are central to pattern recognition, artificial intelligence, machine learning, data mining, and statistical learning. The term often implies analysis of large and complex data sets with minimal human intervention. Bayesian learning has been variously described as a method of updating opinion based on new experience, updating parameters of a process model based on data, modelling and analysis of complex phenomena using multiple sources of information, posterior probabilistic expectation, and so on. In all of these guises, it has exploded in popularity over recent years.

scholarly journals Application of Multivariate-Rank-Based Techniques in Clustering of Big Data

2018 ◽  
Vol 43 (4) ◽  
pp. 179-190
Author(s):  
Pritha Guha
Keyword(s):  
Big Data ◽  
Dna Analysis ◽  
Retail Banking ◽  
Data Sets ◽  
Complex Data ◽  
Data Handling ◽  
Credit Risk Management ◽  
Statistical Tools ◽  
Executive Summary ◽  
Complex Data Sets

Executive Summary Very large or complex data sets, which are difficult to process or analyse using traditional data handling techniques, are usually referred to as big data. The idea of big data is characterized by the three ‘v’s which are volume, velocity, and variety ( Liu, McGree, Ge, & Xie, 2015 ) referring respectively to the volume of data, the velocity at which the data are processed and the wide varieties in which big data are available. Every single day, different sectors such as credit risk management, healthcare, media, retail, retail banking, climate prediction, DNA analysis and, sports generate petabytes of data (1 petabyte = 250 bytes). Even basic handling of big data, therefore, poses significant challenges, one of them being organizing the data in such a way that it can give better insights into analysing and decision-making. With the explosion of data in our life, it has become very important to use statistical tools to analyse them.

scholarly journals Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning

2021 ◽  
Vol 921 (2) ◽  
pp. 177
Author(s):  
Regina Sarmiento ◽  
Marc Huertas-Company ◽  
Johan H. Knapen ◽  
Sebastián F. Sánchez ◽  
Helena Domínguez Sánchez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Physical Properties ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Complex Data ◽  
Galaxy Surveys ◽  
Complex Data Sets ◽  
Low Mass ◽  
Field Unit ◽  
Integral Field

Abstract As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing high dimensionality and complex data sets, which challenges the traditional methods used to extract the physical information they contain. We present the use of a novel self-supervised machine-learning method to visualize the multidimensional information on stellar population and kinematics in the MaNGA survey in a 2D plane. Our framework is insensitive to nonphysical properties such as the size of the integral field unit and is therefore able to order galaxies according to their resolved physical properties. Using the extracted representations, we study how galaxies distribute based on their resolved and global physical properties. We show that even when exclusively using information about the internal structure, galaxies naturally cluster into two well-known categories, rotating main-sequence disks and massive slow rotators, from a purely data-driven perspective, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear as a third cluster only if information about the integrated physical properties is preserved, suggesting a mixture of assembly processes for these galaxies without any particular signature in their internal kinematics that distinguishes them from the two main groups. The framework for data exploration is publicly released with this publication, ready to be used with the MaNGA or other integral field data sets.

scholarly journals Using visual analytics to make sense of railway Close Calls

2016 ◽  
Vol 231 (10) ◽  
pp. 1107-1114
Author(s):  
Miguel Figueres-Esteban ◽  
Peter Hughes ◽  
Coen van Gulijk
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Visual Analytics ◽  
Near Miss ◽  
Test Case ◽  
Data Sets ◽  
Complex Data ◽  
Network Text Analysis ◽  
Complex Data Sets ◽  
Close Call

In the big data era, large and complex data sets will exceed scientists’ capacity to make sense of them in the traditional way. New approaches in data analysis, supported by computer science, will be necessary to address the problems that emerge with the rise of big data. The analysis of the Close Call database, which is a text-based database for near-miss reporting on the GB railways, provides a test case. The traditional analysis of Close Calls is time consuming and prone to differences in interpretation. This paper investigates the use of visual analytics techniques, based on network text analysis, to conduct data analysis and extract safety knowledge from 500 randomly selected Close Call records relating to worker slips, trips and falls. The results demonstrate a straightforward, yet effective, way to identify hazardous conditions without having to read each report individually. This opens up new ways to perform data analysis in safety science.

scholarly journals Use of Big-Data Analytics with the Interactive Advertisement for Product/Service Representation towards its Customers

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
HarshmitKaur Saluja ◽  
Vinod Kumar Yadav ◽  
K.M. Mohapatra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Service Performance ◽  
Data Sets ◽  
Complex Data ◽  
Product Service ◽  
Literature Reviews ◽  
Complex Data Sets ◽  
The One

On the one hand, big-data analytics has brought revolution in the predictive modeler by enabling the complex data sets getting structured. On the other hand, the interactive advertisement has changed the complete scenario of the advertising sector by making advertisements content structured in such a way that it is customer-centric. The paper helps to widen the view to explore the growing urge of customization technique in advertising sector with interactive enablers. The paper further examines that how interactive advertisement and big-data has helped to represent product/service from the view of a customer and also improved the product/service performance. In order of study, exhaustive literature reviews resulting in three hypothesis are developed to take on the above-mentioned concerns.

scholarly journals Interactive web-based visualization of phylogenetic trees using Phylogeny.IO

2016 ◽  
Author(s):  
Nikola Jovanovic ◽  
Alexander S Mikheyev
Keyword(s):  
Open Source ◽  
Phylogenetic Trees ◽  
Data Driven ◽  
Data Sets ◽  
Complex Data ◽  
Web Page ◽  
Web Based ◽  
Interactive Display ◽  
Complex Data Sets

Traditional static publication formats make visualization, exploration and sharing of massive phylogenetic trees difficult. Web-based technologies, such as the Data Driven Document (D3) JavaScript library, exist to overcome such challenges by allowing interactive display of complex data sets. We here we an open-source web-based application that applies the power of D3 to the visualization of phylogenetic trees. Phylogeny.IO (http://phyloeny.io) displays trees together with a range of static (e.g., such as shapes and colors) and dynamic (e.g., pop-up text and images) annotations. Annotated trees can be shared as IFrame HTML objects easily embeddable in any web page.

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