scholarly journals Computer Data Storage and Management Platform Based on Big Data

2021 ◽  
Vol 2066 (1) ◽  
pp. 012022
Author(s):  
Cheng Luo
Keyword(s):  
Big Data ◽  
Data Management ◽  
Performance Improvement ◽  
Data Storage ◽  
Nonvolatile Memory ◽  
Linear Structure ◽  
Ring Structure ◽  
Computer Data ◽  
Management Platform ◽  
A Performance

Abstract Due to the continuous development of information technology, data has increasingly become the core of the daily operation of enterprises and institutions, the main basis for decision-making development. At the same time, due to the development of network, the storage and management of computer data has attracted more and more attention. Aiming at the common problems of computer data storage and management in practical work, this paper analyzes the object and content of data management, investigates the situation of computer data storage and management in China in recent two years, and interviews and tests the data of programming in this design platform. At the same time, in view of the related problems, the research results are applied to practice. On the basis of big data, the storage and management platform is designed. The research and design adopts a special B+ tree node linear structure of CIRC tree, and the linear node structure is changed into a ring structure, which greatly reduces the number of data persistence instructions and the performance overhead. The results show that compared with the most advanced B+ tree design for nonvolatile memory, crab tree has 3.1 times and 2.5 times performance improvement in reading and writing, respectively. Compared with the previous NV tree designed for nonvolatile memory, it has a performance improvement of 1.5 times, and a performance improvement of 8.4 times compared with the latest fast-fair. In the later stage, the expansion of the platform functions is conducive to the analysis and construction of data related storage and management functions, and further improve the ability of data management.

BIG DATA PROCESSING: BIG CHALLENGES AND OPPORTUNITIES

2012 ◽  
Vol 13 (03n04) ◽  
pp. 1250009 ◽  
Author(s):  
CHANGQING JI ◽  
YU LI ◽  
WENMING QIU ◽  
YINGWEI JIN ◽  
YUJIE XU ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Management ◽  
Data Storage ◽  
Large Scale ◽  
Distributed Applications ◽  
Big Data Processing ◽  
Cloud Data ◽  
Management Platform ◽  
Challenges And Opportunities

With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.

scholarly journals How does DICOM support big data management? Investigating its use in medical imaging community

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Marco Aiello ◽  
Giuseppina Esposito ◽  
Giulio Pagliari ◽  
Pasquale Borrelli ◽  
Valentina Brancato ◽  
...  
Keyword(s):  
Big Data ◽  
Medical Imaging ◽  
Data Management ◽  
Data Sharing ◽  
Data Storage ◽  
Big Data Analytics ◽  
Software Tools ◽  
Imaging Data ◽  
Privacy Concerns ◽  
Reading And Writing

AbstractThe diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.

Research on 3D Visualization and Data Management Integration Modeling of Digital Mine: A Case Study of Eastern Gejiu Sn-Cu Deposit in Yunnan Province, China

2014 ◽  
Vol 1073-1076 ◽  
pp. 2036-2041
Author(s):  
Ping Ping Yu ◽  
Jian Ping Chen ◽  
Miao Yu ◽  
Zhao Wu ◽  
Dong Yue Chen
Keyword(s):  
Big Data ◽  
Data Management ◽  
Information Integration ◽  
Yunnan Province ◽  
Production Management ◽  
3D Visualization ◽  
Management Platform ◽  
Digital Mine ◽  
Database Technology

In the era of big data, new information technologies introduced into the study of mining exploration to realize the wisdom prospecting has important significance. Based on 3S technology, 3D modeling and visualization technology, database technology and virtual reality technology, this paper studied the 3D integrated digital mine construction of big data era and presented a new concept of 3D visualization and data management integration modeling of digital mine. A case study of eastern Gejiu Sn-Cu deposit in Yunnan province of China achieved the integrated modeling of ground and underground, and also the multi-information integration and analysis of geology, geography, 2D and 3D. An integrated management platform was built in the application to integrate a variety of mine data organically, which provided support for mine production management, the deep prospecting practice and the comprehensive study and application of geological big data of mine.

How does cloud computing help businesses to manage big data issues

Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmad Latifian
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Data Storage ◽  
Data Privacy ◽  
High Efficiency ◽  
Future Research ◽  
It Services ◽  
Computing Technology ◽  
Content Type

PurposeBig data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.Design/methodology/approachThis paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.FindingsFrom the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.Research limitations/implicationsThere were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.Practical implicationsThe research presents new insight into the business value of cloud computing in interfirm collaborations.Originality/valuePrevious research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

scholarly journals Design of Enterprise Economic Information Management System Based on Big Data Integration Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Xiao Liu
Keyword(s):  
Big Data ◽  
Data Management ◽  
Information Management ◽  
Data Storage ◽  
Management System ◽  
Information Management System ◽  
Economic Benefits ◽  
Current Status ◽  
Integration Algorithm ◽  
Economic Information

In economic growth, the gradual increase in the effect of information technology makes the enterprise economic information management increasingly important for the survival and development of the enterprises. This paper designs an enterprise economic information management system for the complex internal economic information management business and process of enterprises. It provides daily office, information access, document preview, and transmission. The proposed design (i) copes with the inconsistency and irregularity of enterprise economic information data, (ii) quickly obtains valuable information from these massive high-frequency data, and (iii) improves the economic benefits of data assets and data management efficiency. The printing function systematizes the information management for departments such as enterprise economic information, personnel, and production. The main focus of this research includes the mode, framework, and function of the whole system software. Moreover, it also comprises of the use of Internet platform big data technology to realize the practicality, stability, and security of the system database algorithm, which has been practically used by enterprises to improve office efficiency and meet the needs of daily management of enterprises. Based on the analysis of the current status of enterprise big data application, this paper constructs an enterprise economic informational management system based on big data and also describes in detail the key technologies of enterprise economic informational data management from three aspects: NoSQL-based big data storage management, Hadoop-based economic informational big data informational and economic informational big data analysis, and mining algorithm. Provide theoretical basis and basic technical support for online decision analysis.

Modeling and Evaluating the Effects of Big Data Storage Resource Allocation in Global Scale Cloud Architectures

2016 ◽  
Vol 12 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Enrico Barbierato ◽  
Marco Gribaudo ◽  
Mauro Iacono
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Storage ◽  
Global Scale ◽  
Big Data Applications ◽  
Storage Resource ◽  
Management Techniques ◽  
Cloud Elasticity ◽  
Cloud Infrastructures ◽  
Cloud Applications

The availability of powerful, worldwide span computing facilities offering application scalability by means of cloud infrastructures perfectly matches the needs for resources that characterize Big Data applications. Elasticity of resources in the cloud enables application providers to achieve results in terms of complexity, performance and availability that were considered beyond affordability, by means of proper resource management techniques and a savvy design of the underlying architecture and of communication facilities. This paper presents an evaluation technique for the combined effects of cloud elasticity and Big Data oriented data management layer on global scale cloud applications, by modeling the behavior of both typical in memory and in storage data management.

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