scholarly journals MODELING OF LARGE VOLUMES OF DATA WITH THE USE OF NoSQL

2020 ◽  
Vol 69 (1) ◽  
pp. 323-326
Author(s):  
N.B. Zhapsarbek ◽  
Keyword(s):  
Big Data ◽  
Real Time ◽  
High Speed ◽  
Relational Databases ◽  
Accumulation Rate ◽  
Structured Data ◽  
Modern World ◽  
High Accumulation ◽  
Real Time Processing ◽  
Definition Of

In the modern world, specialists and the information systems they create are increasingly faced with the need to store, process and move huge amounts of data. The definition of large amounts of data, Big Data, is used to denote technologies such as storing and analyzing large amounts of data that require high speed and real-time decision making during processing. In this case, large volumes, high accumulation rate, and the lack of a strict internal structure of "big data" are considered. All of this also means that classic relational databases are not well suited for storing them. In this article, we showed solutions for processing large amounts of data for pharmacy chains using NoSQL. This paper presents technologies for modeling large amounts of data using NoSQL, including MongoDB, and also analyzes possible solutions, limitations that do not allow this to be done effectively. This article provides an overview of three modern approaches to working with big data: NoSQL, DataMining and real-time processing of event flows. In this article, as an implementation of the studied methods and technology, we consider a database of pharmacies for processing, searching, analyzing, forecasting big data. Also, when using NoSQL, we showed work with structured and poorly structured data in parallel in different aspects and showed a comparative analysis of the newly developed application for pharmacy workers.

scholarly journals A Real Time Processing system for big data in astronomy: Applications to HERA

2021 ◽  
pp. 100489
Author(s):  
Paul La Plante ◽  
P.K.G. Williams ◽  
M. Kolopanis ◽  
J.S. Dillon ◽  
A.P. Beardsley ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Processing System ◽  
Real Time Processing ◽  
Time Processing

Big Data Deep Analytics for Geosocial Networks

2018 ◽  
pp. 120-140
Author(s):  
Muhammad Mazhar Ullah Rathore ◽  
Awais Ahmad ◽  
Anand Paul
Keyword(s):  
Big Data ◽  
Real Time ◽  
High Speed ◽  
Heterogeneous Data ◽  
Network Data ◽  
Social Network Data ◽  
Current Trends ◽  
The Government ◽  
Geosocial Networks

Geosocial network data provides the full information on current trends in human, their behaviors, their living style, the incidents and events, the disasters, current medical infection, and much more with respect to locations. Hence, the current geosocial media can work as a data asset for facilitating the national and the government itself by analyzing the geosocial data at real-time. However, there are millions of geosocial network users, who generates terabytes of heterogeneous data with a variety of information every day with high-speed, termed as Big Data. Analyzing such big amount of data and making real-time decisions is an inspiring task. Therefore, this book chapter discusses the exploration of geosocial networks. A system architecture is discussed and implemented in a real-time environment in order to process the abundant amount of various social network data to monitor the earth events, incidents, medical diseases, user trends and thoughts to make future real-time decisions as well as future planning.

New Trends in Databases to NonSQL Databases

2021 ◽  
pp. 791-799
Author(s):  
Antonio Sarasa-Cabezuelo
Keyword(s):  
Big Data ◽  
Information Processing ◽  
Real Time ◽  
Relational Databases ◽  
Data Models ◽  
Relational Model ◽  
Alternative Mechanism ◽  
New Models ◽  
Database Models

The appearance of the “big data” phenomenon has meant a change in the storage and information processing needs. This new context is characterized by 1) enormous amounts of information are available in heterogeneous formats and types, 2) information must be processed almost in real time, and 3) data models evolve periodically. Relational databases have limitations to respond to these needs in an optimal way. For these reasons, some companies such as Google or Amazon decided to create new database models (different from the relational model) that solve the needs raised in the context of big data without the limitations of relational databases. These new models are the origin of the so-called NonSQL databases. Currently, NonSQL databases have been constituted as an alternative mechanism to the relational model and its use is widely extended. The main objective of this chapter is to introduce the NonSQL databases.

Development of real-time big data analysis system for RHIPE-based marketing in the automobile industry

2018 ◽  
Vol 7 (3.33) ◽  
pp. 248
Author(s):  
Young-Woon Kim ◽  
Hyeopgeon Lee
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Real Time ◽  
Automobile Industry ◽  
Relational Databases ◽  
Distributed Processing ◽  
Big Data Analysis ◽  
Distributed Structure ◽  
Analysis System ◽  
Data Analysis System

In the automobile industry, the contract information of vehicles contracted through sales activities, as well as the order data of customers who purchased cars, and vehicle maintenance history information all accumulate in relational databases over time. Although accumulated customer and vehicle information is used for marketing purposes, processing and analyzing this massive data is difficult, as its volume con-stantly increases. This problem of managing big data is commonly solved by utilizing the MapReduce distributed structure of Hadoop, which uses big data distributed processing technology, and R, which is a widely used big data analysis technology. Among the methods that interconnect Hadoop and R, the R and Hadoop integrated programming environment (RHIPE) was developed in this study as a real-time big data analysis system for marketing in the automobile industry. RHIPE allows us to maintain an interactive environment and use the powerful analytical features of R, which is an interpreter language, while achieving a high processing speed using Map and Reduce func-tions. In this study, we developed a real-time big data analysis system that can analyze the orders, reservations, and maintenance history contained in big data using the RHIPE method. 

Big Data Analytics Framework for Real-Time Genome Analysis: A Comprehensive Approach

2019 ◽  
Vol 16 (8) ◽  
pp. 3419-3427
Author(s):  
Shishir K. Shandilya ◽  
S. Sountharrajan ◽  
Smita Shandilya ◽  
E. Suganya
Keyword(s):  
Big Data ◽  
Real Time ◽  
Genome Analysis ◽  
High Speed ◽  
High Performance ◽  
Big Data Analytics ◽  
Good Precision ◽  
Entire Genome ◽  
Big Data Technologies ◽  
Sequencing Platforms

Big Data Technologies are well-accepted in the recent years in bio-medical and genome informatics. They are capable to process gigantic and heterogeneous genome information with good precision and recall. With the quick advancements in computation and storage technologies, the cost of acquiring and processing the genomic data has decreased significantly. The upcoming sequencing platforms will produce vast amount of data, which will imperatively require high-performance systems for on-demand analysis with time-bound efficiency. Recent bio-informatics tools are capable of utilizing the novel features of Hadoop in a more flexible way. In particular, big data technologies such as MapReduce and Hive are able to provide high-speed computational environment for the analysis of petabyte scale datasets. This has attracted the focus of bio-scientists to use the big data applications to automate the entire genome analysis. The proposed framework is designed over MapReduce and Java on extended Hadoop platform to achieve the parallelism of Big Data Analysis. It will assist the bioinformatics community by providing a comprehensive solution for Descriptive, Comparative, Exploratory, Inferential, Predictive and Causal Analysis on Genome data. The proposed framework is user-friendly, fully-customizable, scalable and fit for comprehensive real-time genome analysis from data acquisition till predictive sequence analysis.

Design and Implementation of Audio Process System Based on DSP

2014 ◽  
Vol 945-949 ◽  
pp. 1752-1755
Author(s):  
Chui Xin Chen ◽  
Yang Hong Mao
Keyword(s):  
Real Time ◽  
Digital Signal Processor ◽  
High Speed ◽  
Audio Signal ◽  
Reference Value ◽  
Digital Signal ◽  
Real Time Processing ◽  
Audio Processing ◽  
Time Processing ◽  
Process System

The real-time processing for the input analog audio signal, audio processing program is proposed based on DSP. The system use FFT algorithm as the core, first, the input analog audio signal is sampled and A/D conversion using TLV320AIC23, and then use high speed digital signal processor to make real-time processing for the signal. Theoretical and experimental results show that the system can meet the design requirements, it has the advantage of high real-time and simple structure. The system has a good application and reference value for the development and design of data collecting and remote monitoring.

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