Big data: A review of analytics methods &amp; techniques

Arora, Yojna; Goyal, Dinesh

doi:10.1109/ic3i.2016.7917965

Cited by 13 publications

(2 citation statements)

References 18 publications

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“…Some techniques have been developed that can be used to analyze datasets and provide some insight into the data. Various analytical techniques are available, including data mining software, data analysis tools, visualization/dashboard tools, machine learning, deep learning, gradual approaches, cloud computing, IoT, data stream processing, and intelligent analysis [24] Storing and processing large data is a problem, and some innovations have been made to overcome these technological and processing challenges. For example, grid computing is used to manage high-volume data, cloud computing is used to handle high-speed and high-volume data, and open-source cost-reduction and virtualization technology reduces time to test, implement, and enhance processing speeds [28].…”

Section: Resultsmentioning

confidence: 99%

A Review of Algorithms and Techniques for Analyzing Big Data

2021

IJETER

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Big data isgenerated from a variety of sources.Data sets can grow rapidly: for instance, social media such as Facebook, Instagram, and Twitter generate terabytes of data every day. Desktop computers andlaptopsgenerate tremendous amountsof data. Geospatial data aregenerated by cell phones and even from satellites. IoT (Internet of Things) devices like sensorsandpocket computers are also generating a massive amount of data. “Big data”generatestremendous attention worldwide. This paper aims to provide a more comprehensive description of big data that captures its other specific and distinguishing characteristics, which metrics describe the size and other characteristics of big data, and which tools and technologies exist to leverage the potential of big data

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Section: Resultsmentioning

confidence: 99%

A Review of Algorithms and Techniques for Analyzing Big Data

2021

IJETER

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“…Big data is a term used to explain sets of data that are of several forms or structures, achieve extremely high speeds and cannot be processed successfully by traditional database management systems [1] [2]. Zhou et al [3], argued that by the end of 2015 the overall data volume is going to surpass 7.9 Zettabytes(ZB) reaching 35ZB by 2020.…”

Section: Introductionmentioning

confidence: 99%

Improving The Performance of Big Data Databases

Arif

Ali

2019

KJAR

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Real-time monitoring systems utilize two types of database, they are relational databases such as MySQL and non-relational databases such as MongoDB. A relational database management system (RDBMS) stores data in a structured format using rows and columns. It is relational because the values of the tables are connected. A non-relational database is a database that does not adopt the relational structure given by traditional. In recent years, this class of databases has also been referred to as Not only SQL (NoSQL). This paper discusses many comparisons that have been conducted on the execution time performance of types of databases (SQL and NoSQL). In SQL (Structured Query Language) databases different algorithms are used for inserting and updating data, such as indexing, bulk insert and multiple updating. However, in NoSQL different algorithms are used for inserting and updating operations such as default-indexing, batch insert, multiple updating and pipeline aggregation. As a result, firstly compared with related papers, this paper shows that the performance of both SQL and NoSQL can be improved. Secondly, performance can be dramatically improved for inserting and updating operations in the NoSQL database compared to the SQL database. To demonstrate the performance of the different algorithms for entering and updating data in SQL and NoSQL, this paper focuses on a different number of data sets and different performance results. The SQL part of the paper is conducted on 50,000 records to 3,000,000 records, while the NoSQL part of the paper is conducted on 50,000 to 16,000,000 documents (2GB) for NoSQL. In SQL, three million records are inserted within 606.53 seconds, while in NoSQL this number of documents is inserted within 67.87 seconds. For updating data, in SQL 300,000 records are updated within 271.17 seconds, while for NoSQL this number of documents is updated within just 46.02 seconds.

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Big Data and Advanced Analytics

Begum

Fatima²,

Haneef³

2019

Advances in Intelligent Systems and Computing

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Редакционная коллегия: В.А. Богуш, доктор физико-математических наук, профессор, ректор учреждения образования «Белорусский государственный университет информатики и радиоэлектроники» Республики Беларусь; Boris Zibitsker, MS, PhD and Honorable Doctor at BSUIR, President and CEO BEZNext, Professor DePaul University in Chicago, USA; С.К. Дик, кандидат физико-математических наук, доцент, депутат Палаты представителей Национального собрания Республики Беларусь седьмого созыва; Д.В. Лихачевский, кандидат технических наук, доцент, декан факультета компьютерного проектирования учреждения образования «Белорусский государственный университет информатики и радиоэлектроники» Республики Беларусь; Т.В. Казак, заведующий кафедрой инженерной психологии и эргономики БГУИР, доктор психологических наук, профессор, Республика Беларусь;

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Big data: A review of analytics methods & techniques

Cited by 13 publications

References 18 publications

A Review of Algorithms and Techniques for Analyzing Big Data

A Review of Algorithms and Techniques for Analyzing Big Data

Improving The Performance of Big Data Databases

Big Data and Advanced Analytics

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