Introduction

2020 ◽  
pp. 1-12
Author(s):  
Sun-ha Hong
Keyword(s):  
Big Data ◽  
Predictive Control ◽  
Raw Data ◽  
Objective Truth ◽  
Smart Machines

Technologies of datafication pursue the familiar modern promise of better knowledge—and, in doing so, reshape what counts as knowledge in their own image. The fantasy of raw data, objective truth, and predictive control licenses a new array of fabrications: approximations are solidified into facts; algorithmic biases, endowed with neutrality; and uncertainties, upgraded into predictions. Big data and smart machines are technologies of speculation that are establishing new societal defaults for what counts as truth.

Applying the K-Means Algorithm in Big Raw Data Sets with Hadoop and MapReduce

2016 ◽  
pp. 1220-1243
Author(s):  
Ilias K. Savvas ◽  
Georgia N. Sofianidou ◽  
M-Tahar Kechadi
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
File System ◽  
Large Data ◽  
Large Data Sets ◽  
Distributed File System ◽  
Data Sets ◽  
Raw Data ◽  
Hadoop Distributed File System ◽  
Access To Data

Big data refers to data sets whose size is beyond the capabilities of most current hardware and software technologies. The Apache Hadoop software library is a framework for distributed processing of large data sets, while HDFS is a distributed file system that provides high-throughput access to data-driven applications, and MapReduce is software framework for distributed computing of large data sets. Huge collections of raw data require fast and accurate mining processes in order to extract useful knowledge. One of the most popular techniques of data mining is the K-means clustering algorithm. In this study, the authors develop a distributed version of the K-means algorithm using the MapReduce framework on the Hadoop Distributed File System. The theoretical and experimental results of the technique prove its efficiency; thus, HDFS and MapReduce can apply to big data with very promising results.

scholarly journals Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means

2020 ◽  
Vol 19 (2) ◽  
pp. 207-213
Author(s):  
Andris Faesal ◽  
Aziz Muslim ◽  
Aditya Hastami Ruger ◽  
Kusrini Kusrini
Keyword(s):  
Social Media ◽  
Big Data ◽  
Programming Language ◽  
Raw Data ◽  
R Programming Language ◽  
R Programming ◽  
Use Of Social Media ◽  
Media Sentiment ◽  
Existing Data

In this big data era, the use of social media often makes posts in his social media accounts in the form of opinions on events and things around him. One of them is making a post that gives an opinion on the events and items around it. One of them is making a post that gives an opinion on an item that has just been purchased, so that the effect is on other users who are connected to it. The more people who know it, then indirectly people will get to know the item. For that from the description of the problem above, this study raises an idea to make an analysis of social media sentiment which aims to provide a decision of consumer opinion on social media on sales products. As for the several stages of the method for the research, namely from the collection of data carried out by collecting existing data in tweets from social media Twitter using the R programming language. The data produces raw or raw data associated with sales items. With the K-means method as inputting, after each group is known from the K-Means output

scholarly journals Big Data Analytics using Swarm Intelligence based Framework for Prediction on Datasets

2019 ◽  
Vol 8 (4) ◽  
pp. 7356-7360
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Information Source ◽  
Research Work ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Raw Data

Data Analytics is a scientific as well as an engineering tool used to investigate the raw data to revamp the information to achieve knowledge. This is normally connected with obtaining knowledge from reliable information source and rapidity in information processing, and future prediction of the data analysis. Big Data analytics is strongly evolving with different features of volume, velocity and Vectors. Most of the organizations are now concentrating on analyzing information or raw data that are fascinated in deploying analytics to survive forthcoming issues and challenges. The prediction model or intelligent model is proposed in this research to apply machine learning algorithms in the data set. Then it is interpreted and to analyze the better forecast value of the study. The major objective of this research work is to find the optimum prediction from the medical data set using the machine learning techniques.

scholarly journals A predictive control for autonomous vehicles using big data analysis

2019 ◽  
Vol 52 (5) ◽  
pp. 191-196 ◽  
Author(s):  
Dániel Fényes ◽  
Balázs Németh ◽  
Péter Gáspar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Big Data Analysis

Discovering Knowledge Hidden in Big Data from Machine-Learning Techniques

2017 ◽  
pp. 167-183 ◽  
Author(s):  
Adiraju Prashantha Rao
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Raw Data ◽  
Learning Techniques ◽  
Data Analytic ◽  
Information Growth

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

scholarly journals Analyzing Large Microbiome Datasets Using Machine Learning and Big Data

2021 ◽  
Vol 1 (3) ◽  
pp. 138-165
Author(s):  
Thomas Krause ◽  
Jyotsna Talreja Wassan ◽  
Paul Mc Kevitt ◽  
Haiying Wang ◽  
Huiru Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Metagenomic Data ◽  
Data Sets ◽  
Raw Data ◽  
Public And Private ◽  
Rumen Microbiome

Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper.

scholarly journals Big Data Query Optimization -Literature Survey

2021 ◽  
Author(s):  
Anuja S. ◽  
Malathy C.
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Small Businesses ◽  
Large Data ◽  
Efficient Manner ◽  
Raw Data ◽  
Data Query ◽  
Big Data Applications ◽  
Large Databases ◽  
Private And Public

Abstract In today's world, most of the private and public sector organizations deal with massive amounts of raw data, which includes information and knowledge in their secret layer. In addition, the format, scale, variety, and velocity of generated data make it more difficult to use the algorithms in an efficient manner. This complexity necessitates the use of sophisticated methods, strategies, and algorithms to solve the challenges of managing raw data. Big data query optimization (BDQO) requires businesses to define, diagnose, forecast, prescribe, and cognize hidden growth opportunities and guiding them toward achieving market value. BDQO uses advanced analytical methods to extract information from an increasingly growing volume of data, resulting in a reduction in the difficulty of the decision-making process. Hadoop, Apache Hive, No SQL, Map Reduce, and HPCC are the technologies used in big data applications to manage large data. It is less costly to consume data for query processing because big data provides scalability. However, small businesses will never be able to query large databases. Joining tables with millions of tuples could take hours. Parallelism, which solves the problem by using more processors, may be a potential solution. Unfortunately, small businesses cannot afford to operate on a shoestring budget. There are many techniques to tackle the problem. The technologies used in the big data query optimization process are discussed in depth in this paper.

scholarly journals Automation of Big Data Analytics Using Robotic Process Automation

2021 ◽  
pp. 602-605
Author(s):  
G. Malini
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Big Data Analytics ◽  
Human Interaction ◽  
Process Automation ◽  
Raw Data ◽  
Robotic Process Automation ◽  
Manual Intervention ◽  
Almost All

Robotic Process Automation (RPA) is now becomes a buzzword and makes it mark on almost all fields in assisting automation of repetitive human intensive tasks in a simpler manner. RPA is nothing but a software solution that mimics the human interaction with computing software and applications without manual intervention. RPA has already been adapted in almost every business processes which are repetitive. As we are in the age of information the need for retrieval of patterns from raw data is increasing unimaginably so the needs for effective tools are also in a greater need. The effectiveness of RPA can be incorporated into the ever growing data analytics to automate the process of finding patterns and predictions from big data.

Discovering Knowledge Hidden in Big Data From Machine-Learning Techniques

Web Services ◽  
2019 ◽  
pp. 684-700
Author(s):  
Adiraju Prashantha Rao
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Raw Data ◽  
Learning Techniques ◽  
Data Analytic ◽  
Information Growth

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

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