Database Systems for Big Data Storage and Retrieval

2018 ◽  
pp. 76-100 ◽  
Author(s):  
Venkat Gudivada ◽  
Amy Apon ◽  
Dhana L. Rao
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Database Systems ◽  
Query Languages ◽  
Research Issues ◽  
Storage And Retrieval ◽  
Emerging Trends ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application

Special needs of Big Data applications have ushered in several new classes of systems for data storage and retrieval. Each class targets the needs of a category of Big Data application. These systems differ greatly in their data models and system architecture, approaches used for high availability and scalability, query languages and client interfaces provided. This chapter begins with a description of the emergence of Big Data and data management requirements of Big Data applications. Several new classes of database management systems have emerged recently to address the needs of Big Data applications. NoSQL is an umbrella term used to refer to these systems. Next, a taxonomy for NoSQL systems is developed and several NoSQL systems are classified under this taxonomy. Characteristics of representative systems in each class are also discussed. The chapter concludes by indicating the emerging trends of NoSQL systems and research issues.

scholarly journals Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

2020 ◽  
Vol 10 (23) ◽  
pp. 8524
Author(s):  
Cornelia A. Győrödi ◽  
Diana V. Dumşe-Burescu ◽  
Doina R. Zmaranda ◽  
Robert Ş. Győrödi ◽  
Gianina A. Gabor ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Relational Databases ◽  
Database Systems ◽  
Application Performance ◽  
Big Data Applications ◽  
Database Technology ◽  
Important Challenge ◽  
Big Data Application ◽  
The Impact

In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

Preliminary Benefits of Big Data in the Construction Industry: A Case Study

2022 ◽  
pp. 1-11
Author(s):  
Bernard Tuffour Atuahene ◽  
Sittimont Kanjanabootra ◽  
Thayaparan Gajendran
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Construction Projects ◽  
Big Data Applications ◽  
Data Application ◽  
Construction Firm ◽  
Big Data Application ◽  
Tangible Benefit ◽  
Design Construction

Big data applications consist of i) data collection using big data sources, ii) storing and processing the data, and iii) analysing data to gain insights for creating organisational benefit. The influx of digital technologies and digitization in the construction process includes big data as one newly emerging digital technology adopted in the construction industry. Big data application is in a nascent stage in construction, and there is a need to understand the tangible benefit(s) that big data can offer the construction industry. This study explores the benefits of big data in the construction industry. Using a qualitative case study design, construction professionals in an Australian Construction firm were interviewed. The research highlights that the benefits of big data include reduction of litigation amongst projects stakeholders, enablement of near to real-time communication, and facilitation of effective subcontractor selection. By implication, on a broader scale, these benefits can improve contract management, procurement, and management of construction projects. This study contributes to an ongoing discourse on big data application, and more generally, digitization in the construction industry.

scholarly journals Federated Learning in Big Data Application and Sharing

2020 ◽  
Author(s):  
Jing Yang ◽  
Quan Zhang ◽  
Kunpeng Liu ◽  
Peng Jin ◽  
Guoyi Zhao
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
The State ◽  
Ideal Model ◽  
Local Data ◽  
Data Safety ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application ◽  
Uniform Model

In recent years, electricity big data has extensive applications in the grid companies across the provinces. However, certain problems are encountered including, the inability to generate an ideal model using the isolated data possessed by each company, and the priority concerns for data privacy and safety during big data application and sharing. In this pursuit, the present research envisaged the application of federated learning to protect the local data, and to build a uniform model for different companies affiliated to the State Grid. Federated learning can serve as an essential means for realizing the grid-wide promotion of the achievements of big data applications, while ensuring the data safety.

scholarly journals Exploration on the Construction of Sharing Platform of Power Big Data Application

2020 ◽  
Author(s):  
Jing Yang ◽  
Quan Zhang ◽  
Lihua Gong ◽  
Longzhu Zhu ◽  
Kunpeng Liu
Keyword(s):  
Big Data ◽  
Big Data Applications ◽  
Data Application ◽  
Application Sharing ◽  
Big Data Application

Big data application sharing supermarket is a big data application sharing portal. The first, one or more enterprise users can share and use the same big data application product. This product comes from the contribution of enterprise users of a certain sharing supermarket. In this way, we can solve the problem of unbalanced development of big data capabilities, and avoid repeated construction and repeated investment in the same kind of big data applications; the second. Through such a sharing platform, many enterprises carry out cooperation such as federal learning to jointly create big data application products.

scholarly journals PRIVACY-PRESERVING IN MULTI-PARTY DATA RELEASE ON CLOUD FOR BIG DATA USING FUSION LEARNING

2021 ◽  
Vol 9 (2) ◽  
pp. 370-375
Author(s):  
Divya Dangi, Et. al.
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Advantages And Disadvantages ◽  
Data Application ◽  
Data Life Cycle ◽  
Data Production ◽  
Data Volume ◽  
Big Data Application ◽  
Privacy Issues ◽  
Made In

The data volume increases every day and the next wave of apps cannot be envisaged without the created and executed data-driven algorithms. In this post, we undertook an extensive survey on privacy issues in the context of big data. At every point of the Big Data life cycle, we explored privacy challenges and discussed some of the advantages and disadvantages of the Big Data application of new privacy conservation schemes. Much progress has been made in protecting the protection of consumers from data production to data storage, but many transparent questions and hurdles exist.

scholarly journals Big Data Application in Biomedical Research and Health Care: A Literature Review

2016 ◽  
Vol 8 ◽  
pp. BII.S31559 ◽  
Author(s):  
Jake Luo ◽  
Min Wu ◽  
Deepika Gopukumar ◽  
Yiqing Zhao
Keyword(s):  
Public Health ◽  
Health Care ◽  
Big Data ◽  
Health Informatics ◽  
Imaging Informatics ◽  
Clinical Informatics ◽  
Public Health Informatics ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.

Application of Big Data in Environmental Monitoring

2013 ◽  
Vol 864-867 ◽  
pp. 887-890
Author(s):  
Jing Li
Keyword(s):  
Big Data ◽  
Environmental Monitoring ◽  
Data Storage ◽  
Environmental Problems ◽  
System Construction ◽  
Application Layer ◽  
Data Application ◽  
Big Data Application ◽  
New Phenomena ◽  
The Internet Of Things

This article analyses how to combine Big Data with the technology of the internet of things. It believes that application of Big Data management can resolve environmental monitoring problems. It sets up the system construction of environmental monitoring based on application of big dada, which includes perception layer, network and information transmission layer and application layer. It puts forward the measurements of Big Data application that solving data storage technology update, cooperation and research of discipline and inter industry, setting up specialized agencies and technical personnel reserve .Through the application of Big Data in environmental monitoring, new phenomena and problems and regular pattern of environmental problems in China can be found out on time to provide the valid basis for environmental problems.

scholarly journals Several Typical Paradigms of Industrial Big data Application

2021 ◽  
Author(s):  
Hu Shaolin ◽  
Zhang Qinghua ◽  
Su Naiquan ◽  
Li Xiwu
Keyword(s):  
Big Data ◽  
Production Scheduling ◽  
Safety Monitoring ◽  
Research Field ◽  
State Identification ◽  
Big Data Applications ◽  
Data Application ◽  
Industrial Big Data ◽  
Big Data Application ◽  
Definition Of

Industrial big data is an important part of big data family, which has important application value for industrial production scheduling, risk perception, state identification, safety monitoring and quality control, etc. Due to the particularity of the industrial field, some concepts in the existing big data research field are unable to reflect accurately the characteristics of industrial big data, such as what is industrial big data, how to measure industrial big data, how to apply industrial big data, and so on. In order to overcome the limitation that the existing definition of big data is not suitable for industrial big data, this paper intuitively proposes the concept of big data cloud and the 3M (Multi-source, Multi-dimension, Multi-span in time) definition of cloud-based big data. Based on big data cloud and 3M definition, three typical paradigms of industrial big data applications are built, including the fusion calculation paradigm, the model correction paradigm and the information compensation paradigm. These results are helpful for grasping systematically the methods and approaches of industrial big data applications.

scholarly journals Big Data Analytic Framework for Organizational Leverage

2021 ◽  
Vol 11 (5) ◽  
pp. 2340
Author(s):  
Sanjay Mathrani ◽  
Xusheng Lai
Keyword(s):  
Big Data ◽  
Web Sites ◽  
Big Data Analytics ◽  
Building Blocks ◽  
Application Service ◽  
Data Application ◽  
Other Information ◽  
Big Data Application ◽  
Diverse Data ◽  
Framework System

Web data have grown exponentially to reach zettabyte scales. Mountains of data come from several online applications, such as e-commerce, social media, web and sensor-based devices, business web sites, and other information types posted by users. Big data analytics (BDA) can help to derive new insights from this huge and fast-growing data source. The core advantage of BDA technology is in its ability to mine these data and provide information on underlying trends. BDA, however, faces innate difficulty in optimizing the process and capabilities that require merging of diverse data assets to generate viable information. This paper explores the BDA process and capabilities in leveraging data via three case studies who are prime users of BDA tools. Findings emphasize four key components of the BDA process framework: system coordination, data sourcing, big data application service, and end users. Further building blocks are data security, privacy, and management that represent services for providing functionality to the four components of the BDA process across information and technology value chains.

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