Computer Data Processing and Pattern Construction in the Internet Era
Pages 155 - 160
Abstract
With the continuous improvement of technological level, computer data processing technology has also developed and advanced. The rapidly developing information age has also put forward higher requirements for data processing. People need to constantly improve their processing modes in order to ensure data and information security, achieve higher processing efficiency, and meet the growing needs of people. Therefore, this article conducts research on computer data processing and pattern construction from this perspective. Through data comparison methods, this article compares the performance of different data processing methods and technologies in various aspects such as processing quality, work efficiency, and classification and screening accuracy to evaluate their respective advantages and disadvantages. It proposes corresponding suggestions based on the research results, analyzes the computer data processing and models in the current Internet era, and promotes the continuous development and optimization of processing technology. The research results indicate that intelligent computing data processing methods have significant advantages, with a processing quality of 93%, which is much better than other methods in terms of processing efficiency.
References
[1]
Zhang T, Gao L, He C, Mi Zhang, Bhaskar Krishnamachari,A. Salman Avestimehr . Federated learning for the internet of things: Applications, challenges, and opportunities[J]. IEEE Internet of Things Magazine, 2022, 5(1): 24-29.
[2]
Ladron de Guevara Rodríguez M, Lopez-Agudo L A, Prieto-Latorre C. Internet use and academic performance: An interval approach[J]. Education and Information Technologies, 2022, 27(8): 11831-11873.
[3]
Dalkin S, Forster N, Hodgson PMonique Lhussier, Susan M Carr. Using computer assisted qualitative data analysis software (CAQDAS; NVivo) to assist in the complex process of realist theory generation, refinement and testing[J]. International Journal of Social Research Methodology, 2021, 24(1): 123-134.
[4]
O'Kane P, Smith A, Lerman M P. Building transparency and trustworthiness in inductive research through computer-aided qualitative data analysis software[J]. Organizational Research Methods, 2021, 24(1): 104-139.
[5]
Crowell B. Blockchain-based metaverse platforms: augmented analytics tools, interconnected decision-making processes, and computer vision algorithms[J]. Linguistic and Philosophical Investigations, 2022 (21): 121-136.
[6]
Kaur J, Ramkumar K R. The recent trends in cyber security: A review[J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(8): 5766-5781.
[7]
Perwej Y, Abbas S Q, Dixit J P. A systematic literature review on the cyber security[J]. International Journal of scientific research and management, 2021, 9(12): 669-710.
[8]
Florackis C, Louca C, Michaely R, Michael Weber. Cybersecurity risk[J]. The Review of Financial Studies, 2023, 36(1): 351-407.
[9]
Lv Z, Singh A K. Big data analysis of internet of things system[J]. ACM Transactions on Internet Technology, 2021, 21(2): 1-15.
[10]
Zhong Y, Chen L, Dan C, Amin Rezaeipanah. A systematic survey of data mining and big data analysis in internet of things[J]. The Journal of Supercomputing, 2022, 78(17): 18405-18453.
[11]
Sandhu A K. Big data with cloud computing: Discussions and challenges[J]. Big Data Mining and Analytics, 2021, 5(1): 32-40.
[12]
Khan S, Shaheen M. From data mining to wisdom mining[J]. Journal of Information Science, 2023, 49(4): 952-975.
[13]
Haoxiang W, Smys S. Big data analysis and perturbation using data mining algorithm[J]. Journal of Soft Computing Paradigm (JSCP), 2021, 3(01): 19-28.
[14]
Ageed Z S, Zeebaree S R M, Sadeeq M M, Shakir Fattah Kak, Hazha Saeed Yahia, Mayyadah R. Mahmood, Comprehensive survey of big data mining approaches in cloud systems[J]. Qubahan Academic Journal, 2021, 1(2): 29-38.
[15]
Tong Z, Ye F, Yan M, Hong Liu, Sunitha Basodi. A survey on algorithms for intelligent computing and smart city applications[J]. Big Data Mining and Analytics, 2021, 4(3): 155-172.
Information & Contributors
Information
Published In

November 2023
223 pages
ISBN:9798400709166
DOI:10.1145/3645279
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 06 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
BDMIP '23
BDMIP '23: International Conference on Big Data Mining and Information Processing
November 17 - 19, 2023
Xiamen City, China, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 8Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)2
Reflects downloads up to 17 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format