scholarly journals Apache Hadoop, platform for the collection, processing and analysis of large data sets

2017 ◽  
Vol 4 ◽  
pp. 70-75
Author(s):  
Mateusz Gil
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
The Internet ◽  
Data Sets ◽  
Application Performance ◽  
Apache Hadoop ◽  
Hadoop Platform

The article presents the possibilities of using Hadoop platform to manage large data sets. The development of application performance has been shown based on available sources. Additionally, the article describes the organizations that have been successful in the Internet thanks to the implemented software.

scholarly journals Performance Optimization System for Hadoop and Spark Frameworks

2020 ◽  
Vol 20 (6) ◽  
pp. 5-17
Author(s):  
Hrachya Astsatryan ◽  
Aram Kocharyan ◽  
Daniel Hagimont ◽  
Arthur Lalayan
Keyword(s):  
Performance Optimization ◽  
Large Scale ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Apache Hadoop ◽  
Compression Factor ◽  
Large Scale Data ◽  
Additional Processing ◽  
Mapreduce Model

AbstractThe optimization of large-scale data sets depends on the technologies and methods used. The MapReduce model, implemented on Apache Hadoop or Spark, allows splitting large data sets into a set of blocks distributed on several machines. Data compression reduces data size and transfer time between disks and memory but requires additional processing. Therefore, finding an optimal tradeoff is a challenge, as a high compression factor may underload Input/Output but overload the processor. The paper aims to present a system enabling the selection of the compression tools and tuning the compression factor to reach the best performance in Apache Hadoop and Spark infrastructures based on simulation analyzes.

scholarly journals Construction of economic indicators using internet searches

10.26458/1513 ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 25 ◽  
Author(s):  
Mioara POPESCU
Keyword(s):  
Economic Analysis ◽  
Large Data ◽  
Large Data Sets ◽  
The Internet ◽  
Data Sets ◽  
Online Data ◽  
Additional Information ◽  
Search Data ◽  
Internet Searches ◽  
Further Development

The volume of online data searches can be used as indicators of economic analysis and forecasting. This paper reviews some of the applications that use the large data sets provided by the Internet user searches and presents a very specific case for Romanian economy. These data provide some additional information relative to existing surveys and with further development, internet search data could become an important tool for analysis and prediction. 

Internet Data Mining

2011 ◽  
pp. 236-253
Author(s):  
Kuldeep Kumar ◽  
John Baker
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Web Mining ◽  
Large Data ◽  
Customer Segmentation ◽  
Large Data Sets ◽  
The Internet ◽  
Data Sets ◽  
Large Databases ◽  
The Web

Data mining has emerged as one of the hottest topics in recent years. It is an extraordinarily broad area and is growing in several directions. With the advancement of the Internet and cheap availability of powerful computers, data is flooding the market at a tremendous pace. However, the technology for navigating, exploring, visualizing and summarizing large databases are still in their infancy. The quantity and diversity of data available to make decisions has increased dramatically during the past decade. Large databases are being built to hold and deliver these data. Data mining is defined as the process of seeking interesting or valuable information within large data sets. Some examples of data mining applications in the area of management science are analysis of direct-mailing strategies, sales data analysis for customer segmentation, credit card fraud detection, mass customization, etc. With the advancement of the Internet and World Wide Web, both management scientists and interested end-users can get large data sets for their research from this source. The Web not only contains a vast amount of useful information, but also provides a powerful infrastructure for communication and information sharing. For example, Ma, Liu and Wong (2000) have developed a system called DS-Web that uses the Web to help data mining. A recent survey on Web mining research can be seen in the paper by Kosala and Blockeel (2000).

Application and Research on Distributed Collaborative Filtering Recommendation Algorithm Based on Hadoop

2015 ◽  
Vol 713-715 ◽  
pp. 1615-1621
Author(s):  
Xiu Juan Li ◽  
He Biao Yang
Keyword(s):  
Parallel Computing ◽  
Comparative Analysis ◽  
Collaborative Filtering ◽  
Computational Efficiency ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Recommendation Algorithm ◽  
Exponential Expansion ◽  
Hadoop Platform

Coupled with exponential expansion of the data, efficient computing of existing recommendation algorithm has become an important issue, and the traditional collaborative filtering recommendation algorithm also exist the problem of sparsity. Based on the detailed analysis, the article introduce Hadoop platform into improved collaborative filtering recommendation algorithm, the improved collaborative filtering recommendation algorithm solve the problem of data sparsity, MapReduce parallel computing of recommendation also solve the promble of computational efficiency. In the experiments, the comparative analysis between Hadoop platform implementation and the previous implementation draws the conclusion that the Hadoop platform improves collaborative filtering recommendation algorithm computation efficiently under conditions of large data sets.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

2018 ◽  
Vol 2018 (6) ◽  
pp. 38-39
Author(s):  
Austa Parker ◽  
Yan Qu ◽  
David Hokanson ◽  
Jeff Soller ◽  
Eric Dickenson ◽  
...  
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Statistical Tools

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 47
Author(s):  
Fariha Iffath ◽  
A. S. M. Kayes ◽  
Md. Tahsin Rahman ◽  
Jannatul Ferdows ◽  
Mohammad Shamsul Arefin ◽  
...  
Keyword(s):  
Source Code ◽  
Large Data ◽  
Large Data Sets ◽  
Detection Technique ◽  
Data Sets ◽  
Plagiarism Detection ◽  
Source Codes ◽  
Efficient Detection ◽  
Mathematical Problems ◽  
Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

Sign in / Sign up

Export Citation Format

Share Document