The overview of big data storage and management

Li, Jie; Xu, Zheng; Jiang, Yayun; Zhang, Rui

doi:10.1109/icci-cc.2014.6921508

Cited by 14 publications

(8 citation statements)

References 15 publications

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“…Also, many of the big data tools are open source and free to use, which could provide the opportunity for intrusive operations, hackers, and data theft. Some literatures [32][33][34][35][36][37][38] discuss obstacles in the development of big data in healthcare applications. The key challenges are listed as follows:…”

Section: Big Data Analytics Challenges In Health Carementioning

confidence: 99%

Big Data Analytics in Health Care: A Review Paper

Yousef¹

2021

IJCSIT

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The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.

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Section: Big Data Analytics Challenges In Health Carementioning

confidence: 99%

Big Data Analytics in Health Care: A Review Paper

Yousef¹

2021

IJCSIT

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“…The classification process performs after the training phase; i.e., training process is especially utilized to create a model for classification which helps for classification in further phase. According to [8], the training process will not get affected by data size. Different authors mainly worked on big data classification problems [9] [10].…”

Section: Introductionmentioning

confidence: 99%

NFDDC: Novel Neuro-Fuzzy Logic based Methodology for Distributed Data Classification

Parveen M*

2019

IJRTE

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With the growing culture of Internet applications and their usage lead to challenging task for storing a massive volume of high-velocity data from different fields. This result an evolution of big data with integrated, i.e. Volume, Velocity, and Variety (3V's). The voluminous data extraction is a very complex task which is not possible form classical data mining techniques. Therefore, a big data mining technique is introducing by modifying traditional data mining scheme using a novel of Neuro-Fuzzy Logic based approach, i.e. named as NFDDC. The proposed distributed data classification model performs into three stages first- reduce the data set dimension, second- data clustering, and third-data classification using the neuro-fuzzy method. The performance of the NFDDC system is analysed using two different datasets, i.e. medical data and e-commerce datasets. Additionally, comparative analysis is performed by measuring the accuracy of existing CCSA algorithm with proposed NFDDC algorithm and will get 90% accuracy in data classification

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“…The existing work in BSVD management has mostly emphasized on some characteristics (volume, variety, or velocity) of big spatial data, and solved certain problems in the technical level or applications. Although there already have several studies related to big spatial data (Eldawy & Mokbel, 2015a;Lee & Kang, 2015;Yang, Yan, & Nebert, 2013) and big data management (Kanchi et al, 2015;Karim et al, 2017;Li, Xu, Jiang, & Zhang, 2014;Siddiqa et al, 2016;Storey & Song, 2017), however, by now, no scholar has directly focused on big spatial vector data (BSVD) management and reviewed the latest literatures to provide a comprehensive survey of the existing techniques and technologies.…”

Section: Introductionmentioning

confidence: 99%

Big spatial vector data management: a review

Yao

2018

Big Earth Data

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Spatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other datasets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures. To support them more fully, firstly, we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era. Then, we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD, including data storage and organization, spatial index, processing methods, and spatial analysis. Finally, based on the above commentary and related work, several opportunities and challenges are listed as the future research interests and directions for reference.

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The overview of big data storage and management

Abstract: Recent years, data amount is increasing rapidly, indicating that we are in the era of big data, which brings opportunities and challenges. In this paper, we illustrate the big data storage and management status, analyze the problems and finally propose solutions.

Cited by 14 publications

References 15 publications

Big Data Analytics in Health Care: A Review Paper

Big Data Analytics in Health Care: A Review Paper

NFDDC: Novel Neuro-Fuzzy Logic based Methodology for Distributed Data Classification

Big spatial vector data management: a review

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