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Artykuły w czasopismach na temat „Data”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Gade, Kishore. "Data Analytics: Data Privacy, Data Ethics, Data Monetization." International Journal of Science and Research (IJSR) 9, no. 2 (2020): 1953–59. http://dx.doi.org/10.21275/sr20027110931.

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Rawish Siddiqui, Muhammad. "Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond." Chemistry Research and Practice 1, no. 2 (2024): 01–06. https://doi.org/10.64030/3065-906x.01.02.04.

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In the age of digital transformation, the rise of Big Data has fundamentally altered how organizations store, process, and utilize information. This whitepaper provides a comprehensive analysis comparing Big Data with traditional data systems, data warehousing, business intelligence (BI), artificial intelligence (AI), data science, and NoSQL databases. By exploring key differentiators such as volume, variety, velocity, and processing capabilities, this paper aims to shed light on how Big Data has reshaped modern technology infrastructures and its role in advancing analytics, decision-making, a

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Nicolae, Sfetcu. "Megadatele (Big Data) pe Internet." IT & C 1, no. 1 (2022): 23–27. https://doi.org/10.58679/IT47091.

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Termenul Big Data se referă la extragerea, manipularea și analiza unor seturi de date care sunt prea mari pentru a fi tratate în mod obișnuit. Din această cauză se utilizează software special și, în multe cazuri, și calculatoare și echipamente hardware special dedicate. În general la aceste date analiza se face statistic. Pe baza analizei datelor respective se fac de obicei predicții ale unor grupuri de persoane sau alte entități, pe baza comportamentului acestora în diverse situații și folosind tehnici analitice avansate. Se pot identifica astfel tendințe, necesități ș

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Gade, Kishore. "Data Analytics: Data Mesh Architecture and Its Implications for Data Management." International Journal of Science and Research (IJSR) 8, no. 11 (2019): 2061–67. http://dx.doi.org/10.21275/sr19113110630.

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Migdał-Najman, Kamila, and Krzysztof Najman. "BIG DATA = CLEAR + DIRTY + DARK DATA." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, no. 469 (2017): 131–39. http://dx.doi.org/10.15611/pn.2017.469.13.

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Rakholiya, Kalpesh R., and Dr Dhaval Kathiriya. "Data Mining for Moving Object Data." Indian Journal of Applied Research 2, no. 3 (2011): 111–13. http://dx.doi.org/10.15373/2249555x/dec2012/34.

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Arputhamary, B., and L. Arockiam. "Data Integration in Big Data Environment." Bonfring International Journal of Data Mining 5, no. 1 (2015): 01–05. http://dx.doi.org/10.9756/bijdm.8001.

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Chomboon, K., N. Kaoungku, K. Kerdprasop, and N. Kerdprasop. "Data Mining in Semantic Web Data." International Journal of Computer Theory and Engineering 6, no. 6 (2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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Zvyagin, L. S. "DATA MINING: BIG DATA AND DATA SCIENCE." SOFT MEASUREMENTS AND COMPUTING 5, no. 54 (2022): 81–90. http://dx.doi.org/10.36871/2618-9976.2022.05.006.

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Data mining is the process of discovering information that can be used in large amounts of data. This method uses mathematical analysis, which helps to identify patterns and trends in the data. Such patterns cannot be noticed during normal data viewing due to the complexity of the relationships that arise with a large amount of data. All of them are a set of tools and methods that help humanity in the changing world around us. It is becoming more and more voluminous, we receive huge aggregates of data on various processes. Big Data and Data Science allow large companies to systematize informat

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10

Remize, Michel. "La data pour dada." Archimag N°310, no. 10 (2017): 1. http://dx.doi.org/10.3917/arma.310.0001.

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Kumar Sinha, Gaurav. "A Data Mesh-Driven Data Lake Architecture for Oil Field Data Consolidation." International Journal of Science and Research (IJSR) 12, no. 11 (2023): 2151–57. https://doi.org/10.21275/sr24320144456.

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Specht, Alison, Matthew Bolton, Bryn Kingsford, Raymond Specht, and Lee Belbin. "A story of data won, data lost and data re-found: the realities of ecological data preservation." Biodiversity Data Journal 6 (November 7, 2018): e28073. https://doi.org/10.3897/BDJ.6.e28073.

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This paper discusses the process of retrieval and updating legacy data to allow on-line discovery and delivery. There are many pitfalls of institutional and non-institutional ecological data conservation over the long term. Interruptions to custodianship, old media, lost knowledge and the continuous evolution of species names makes resurrection of old data challenging. We caution against technological arrogance and emphasise the importance of international standards. We use a case study of a compiled set of continent-wide vegetation survey data for which, although the analyses had been publish

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13

Chukanova, Svitlana. "The Notion of "Research Data": Types and Kinds of Research Data in the Context of Data Management Practice." Ukrainian Journal on Library and Information Science, no. 8 (December 20, 2021): 128–38. https://doi.org/10.31866/2616-7654.8.2021.247590.

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With the rapid development of the concept of Open Science, the quantitative growth of data obtained during the research, scientific attention to the practice of research data management (research data management) increases, which actualizes the definition of “research data” and identifying types of research data within the practice of their management, justification and coverage of the specifics of such data. The methodological tools of the study are based on the terminological method, the use of which was due to the need to identify relevant interpretations of the concept of &ldqu

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14

Arif, Bramantoro. "Data Cleaning Service for Data Warehouse: An Experimental Comparative Study on Local Data." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 2 (2018): 834–42. https://doi.org/10.12928/telkomnika.v16.i2.7669.

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Data warehouse is a collective entity of data from various data sources. Data are prone to several complications and irregularities in data warehouse. Data cleaning service is non trivial activity to ensure data quality. Data cleaning service involves identification of errors, removing them and improve the quality of data. One of the common methods is duplicate elimination. This research focuses on the service of duplicate elimination on local data. It initially surveys data quality focusing on quality problems, cleaning methodology, involved stages and services within data warehouse environme

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15

Gültepe, Yasemin. "Querying Bibliography Data Based on Linked Data." Journal of Software 10, no. 8 (2015): 1014–20. http://dx.doi.org/10.17706//jsw.10.8.1014-1020.

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Sharma, Mansi, Palak Mittal, Nidhi Garg, and Prateek Jain. "Data Analysis FIFA World Cup Data Set." Indian Journal of Science and Technology 12, no. 39 (2019): 1–4. http://dx.doi.org/10.17485/ijst/2019/v12i39/145565.

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Yerbulatov, Sultan. "Data Security and Privacy in Data Engineering." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 232–36. http://dx.doi.org/10.21275/es24318121241.

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18

Abhijit, Joshi. "Architectural Paradigms in Data Management: Evaluating Data Lakes and Data Warehouses for Enterprise Data Ecosystems." Journal of Scientific and Engineering Research 6, no. 4 (2019): 221–28. https://doi.org/10.5281/zenodo.11820647.

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In the digital era, the exponential growth of data has necessitated the evolution of robust architectures for efficient data management, storage, and analysis. Data lakes and data warehouses represent two fundamentally different approaches to data storage and utilization. This whitepaper delves into the technical nuances of each architecture, assessing their structural, operational, and functional distinctions. By comparing the two in terms of data integration, scalability, flexibility, and performance, the document aims to furnish businesses with a clear understanding of how each architecture

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19

Kavita, Ahuja, and N.N.Jani. "A STUDY OF TRADITIONAL DATA ANALYSIS AND SENSOR DATA ANALYTICS." International Journal of Information Sciences and Techniques (IJIST) 6, no. 1/2 (2016): 185–90. https://doi.org/10.5281/zenodo.7743171.

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The growth of smart and intelligent devices known as sensors generate large amount of data. These generated data over a time span takes such a large volume which is designated as big data. The data structure of repository holds unstructured data. The traditional data analytics methods well developed and used widely to analyze structured data and to limit extend the semi-structured data which involves additional processing over heads. The similar methods used to analyze unstructured data are different because of distributed computing approach where as there is a possibility of centralized proce

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Kõljalg, Urmas, Kessy Abarenkov, Allan Zirk, et al. "PlutoF: Biodiversity data management platform for the complete data lifecycle." Biodiversity Information Science and Standards 3 (June 26, 2019): e37398. https://doi.org/10.3897/biss.3.37398.

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PlutoF online platform (https://plutof.ut.ee) is built for the management of biodiversity data. The concept is to provide a common workbench where the full data lifecycle can be managed and support seamless data sharing between single users, workgroups and institutions. Today, large and sophisticated biodiversity datasets are increasingly developed and managed by international workgroups. PlutoF's ambition is to serve such collaborative projects as well as to provide data management services to single users, museum or private collections and research institutions. Data management in PlutoF fol

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T., Aditya Sai Srinivas, Sravanthi Y., Vinod Kumar Y., and Dwaraka Srihith I.V. "Data Standardization: Key to Effective Data Integration." Advanced Innovations in Computer Programming Languages 6, no. 1 (2023): 1–4. https://doi.org/10.5281/zenodo.10060920.

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<i>Data standardization is a critical step in data preprocessing and analysis. This process involves transforming data to have a consistent scale, enabling meaningful comparisons and effective modeling. In this digital age, where data fuels decision-making across industries, understanding and implementing data standardization techniques is essential. This abstract introduces the concept of data standardization, emphasizing its importance in enhancing data quality, supporting data integration efforts, and facilitating data-driven decision-making. We explore various methods and tools for standar

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22

Reddy Desani, Nithin. "Enhancing Data Governance through AI - Driven Data Quality Management and Automated Data Contracts." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 2519–25. http://dx.doi.org/10.21275/es23812104904.

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Sánchez, Luis, Jorge Lanza, Juan Ramón Santana, et al. "Data Enrichment Toolchain: A Data Linking and Enrichment Platform for Heterogeneous Data." IEEE Access 11 (September 20, 2023): 103079–91. https://doi.org/10.1109/ACCESS.2023.3317705.

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Proliferation of data sources associated to Internet of Things (IoT) deployment as well as those bound to Open Data Portals (e.g. European Data Portal, Municipalities Open Data Portals, etc.) and Social Media platforms is creating an abundance of information that is called to bring benefits for both the private and public sectors, through the development of added-value services, increasing administrations’ transparency and availability or fostering efficiency of public services. However, pieces of information without a context are significantly less valuable. Raw data lacks semantics and

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24

Sutanapong., Chanoknath, and P. Louangrath. "Data Classification and Distribution." Inter. J. Res. Methodol. Soc. Sci 1, no. 2 (2015): 36–47. https://doi.org/10.5281/zenodo.1320784.

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The objective of this paper is to explain the four main types of data. The classification of data by type is important for statistical analysis. In particular, data classification is useful for quantitative research in social science. Data are defined as a quantitative measurement of qualitative fact. Data are classified into three types: quantitative, ordinal and nominal. Quantitative data are those that may be subject to mathematical operations: addition, subtraction, multiplication and division. Ordinal data are those that rank the values in a data set in an ascending order (from low to hig

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25

Hemanth, Kumar MS, and M. P. Pavithra. "Revolution in Data Market: A Study on Data Consumption in India." International Journal of Current Science Research and Review 07, no. 05 (2024): 3327–35. https://doi.org/10.5281/zenodo.11382665.

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Abstract : <strong> </strong>India has been a developing country; in the process of development India has seen massive revolution in information technology. As information technology industry is growing in a fast phase that has enabled the people to use smartphones and computers extensively which has resulted in excessive consumption of data. Smart phones are not just phones now they have become an integral part of our life, as a result of which the data consumption has also seen an immense growth. In this article we will be discussing on how smart phones have replaced many elements and a

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26

Vala, Mr Manish, Kajal Patel, and Harsh Lad. "Multi Model Biometrics Data Retrieval Through: Big-Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 1273–77. http://dx.doi.org/10.31142/ijtsrd15933.

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S, Monisha, and Dr S. Venkateshkumar. "Cloud Computing in Data Backup and Data Recovery." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 865–67. http://dx.doi.org/10.31142/ijtsrd18652.

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Lenk, Peter, Michael Street, Ivana Ilic Mestric, et al. "Data Science as a Service: The Data Range." Information & Security: An International Journal 47, no. 2 (2020): 157–71. http://dx.doi.org/10.11610/isij.4711.

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Shastri, Shankarayya, M. Pooja, and R. M. Soumyashree. "Data Analytics for Linked Data in Real Time." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (2016): 32–36. http://dx.doi.org/10.9756/bijsesc.8238.

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Martha, Ranjith. "Real-Time Data Ingestion for Big Data Processing." International Journal of Science and Research (IJSR) 14, no. 2 (2025): 570–72. https://doi.org/10.21275/sr25209075243.

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Ingenloff, Kate. "Enhancing data integration with existing FAIR enabling resources: a case for using the Darwin Core and its extensions to integrate eLTER biosphere data with other biodiversity data." ARPHA Conference Abstracts 8 (May 28, 2025): e151692. https://doi.org/10.3897/aca.8.e151692.

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Regional and global efforts to monitor the status of, and identify mitigating actions for, climate change impacts and biodiversity loss requires copious amounts of survey and monitoring data that can only be obtained through the integration and analysis of multiple independently derived primary datasets from across a broad range of disciplines. Despite mandates to make ecological data FAIR and open (Wilkinson et al. 2016), data scientists and researchers attempting to integrate data from heterogeneous sources continue to face a series of pre-analysis hurdles including: finding data fit-for-use

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Scaife, Anna M. M., and Sally E. Cooper. "The DARA Big Data Project." Proceedings of the International Astronomical Union 14, A30 (2018): 569. http://dx.doi.org/10.1017/s174392131900543x.

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AbstractThe DARA Big Data project is a flagship UK Newton Fund & GCRF program in partnership with the South African Department of Science & Technology (DST). DARA Big Data provides bursaries for students from the partner countries of the African VLBI Network (AVN), namely Botswana, Ghana, Kenya, Madagascar, Mauritius, Mozambique, Namibia and Zambia, to study for MSc(R) and PhD degrees at universities in South Africa and the UK. These degrees are in the three data intensive DARA Big Data focus areas of astrophysics, health data and sustainable agriculture. The project also provides trai

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Sandhya Kona, Sree. "Ensuring Data Integrity in Big Data Ingestion: Techniques and Best Practices for Data Quality Assurance." International Journal of Science and Research (IJSR) 9, no. 5 (2020): 1866–69. http://dx.doi.org/10.21275/sr24522140238.

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Arjun, Mantri. "Enhancing Data Quality in Data Engineering using Data Testing Framework: Types and Tradeoffs." European Journal of Advances in Engineering and Technology 7, no. 10 (2020): 95–100. https://doi.org/10.5281/zenodo.13354036.

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Ensuring high data quality is critical in the era of big data, where reliable data is essential for accurate decision-making and business intelligence. This paper reviews various data testing frameworks designed to enhance data quality, including data validation, data cleansing, data profiling, data lineage, and automated testing frameworks. Each type of framework offers unique functionalities and presents distinct tradeoffs, such as customization versus complexity and real-time versus batch processing. By understanding these frameworks and their tradeoffs, data engineers can make informed dec

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Fasihuddin, Mirza. "Integrating with Various Data Sources and Formats, Including Structured, Semi-Structured, and Unstructured Data." Journal of Scientific and Engineering Research 8, no. 2 (2021): 263–68. https://doi.org/10.5281/zenodo.11216190.

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The increasing availability and importance of data in various formats have led to the necessity for efficient integration methods to extract meaningful insights. This academic journal explores the challenges and solutions associated with integrating data from multiple sources, including structured, semi-structured, and unstructured data. The study aims to provide an overview of the techniques and tools available to businesses and researchers for effectively integrating diverse data types, enabling better decision-making and improving overall data-driven processes.

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Villarroel, José Domingo, Maria Merino Maestre, and Alvaro Antón. "Data." IKASTORRATZA.e-journal on Didactics, September 23, 2020. http://dx.doi.org/10.37261/data.

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prihtiadi, hafizh. "dama test simulated data." November 30, 2022. https://doi.org/10.5281/zenodo.7379071.

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"Data Processing through Data Warehouse and Data mining." International Journal of Modern Trends in Engineering & Research 4, no. 5 (2017): 45–48. http://dx.doi.org/10.21884/ijmter.2017.4151.1ea3x.

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David, John. "Data Mining and Data Warehousing." SIJ Transactions on Computer Science Engineering & its Applications (CSEA), June 28, 2019, 17–19. http://dx.doi.org/10.9756/sijcsea/v7i3/07010010105.

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Amelia, Ethan. "Ensuring Data Integrity in Big Data Environments." October 7, 2022. https://doi.org/10.5281/zenodo.8415841.

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The era of Big Data presents organizations with unprecedented opportunities for extracting valuable insights, but it also brings significant challenges, particularly regarding data integrity. This paper explores the critical aspects of ensuring data integrity in Big Data environments. It delves into the complexities of data sources, volume, and velocity, as well as the methods and technologies for maintaining data integrity. Through real-world examples and best practices, this paper provides insights into how organizations can effectively preserve the accuracy, consistency, and reliability of

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Suchitra, B. "Semi-Structured Data Structured Data Conversion Using Data Mining Methods." International Journal of Emerging Trends in Science and Technology 4, no. 10 (2017). http://dx.doi.org/10.18535/ijetst/v4i10.15.

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Abbey, Nitin. "Data Localization For Data Centers." November 28, 2022. https://doi.org/10.5281/zenodo.7379894.

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There is a new saying that data is gold for this generation. The foundation of the digital economy is entirely dependent on data. To organize, manage, store, and process the data, a specialized facility known as a data center is adopted by companies. Data localization, in simple terms, is a compliance process of restricting data flow from one country of origin to another. Today, 75% of all nations have deployed data localization rules at varying levels. These restrictions significantly impact data governance, IT footprints, data architectures, and interactions with local regulators. So, let&rs

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"Data Mining from Heterogeneous Data Sources." International Journal of Science and Research (IJSR) 6, no. 1 (2017): 2076–79. http://dx.doi.org/10.21275/art20164530.

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"Data Anonymization Approach for Data Privacy." International Journal of Science and Research (IJSR) 4, no. 12 (2015): 1534–39. http://dx.doi.org/10.21275/v4i12.12121502.

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Hari, Prasad Bomma. "AI Integrated Data Governance and Data Lineage." International Journal on Science and Technology 15, no. 2 (2024). https://doi.org/10.71097/IJSAT.v15.i2.1645.

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Abstract In this digital era, artificial intelligence (AI) is revolutionizing all the fields known to man. Data governance and data lineage practices are no exception. AI is making its mark in the area of Data governance and Lineage, making them more efficient and reliable. This paper explores the integration of AI into data governance frameworks, emphasizing its role in automating data quality checks, enhancing regulatory compliance, and ensuring data security. Furthermore, the study examines how AI driven data lineage provides comprehensive visibility into data movements, improving transpare

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Gardner, Lea, and Susan Wallace. "Data Snapshot: Nasogastric Tube Misplacements." Patient Safety, March 17, 2021, 79–83. http://dx.doi.org/10.33940/data/2021.3.8.

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Nasogastric and orogastric tubes, herein collectively referred to as nasogastric tubes (NGT), are inserted into a patient’s nasal or oral cavity to administer feedings or medications or remove stomach contents. Tube misplacement is a known complication that can occur during insertion. This NGT misplacement data snapshot provides updated information.

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Jordon, Smith. "Data Governance Strategies for Maintaining Data Integrity." October 7, 2023. https://doi.org/10.5281/zenodo.8415852.

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Data governance is crucial for maintaining data integrity in an era where data is abundant, diverse, and constantly evolving. This paper explores effective data governance strategies to preserve data integrity. It delves into the importance of data governance, the challenges it addresses, and the best practices organizations can implement. Key topics include data quality, data lifecycle management, data stewardship, and the role of technology in supporting data governance. By examining real-world case studies and industry standards, this paper provides valuable insights for organizations seeki

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Margoni, Thomas, Charlotte Ducuing, and Luca Schirru. "Data property, data governance and Common European Data Spaces." April 26, 2023. https://doi.org/10.5281/zenodo.7906945.

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The Data Act proposal of February 2022 constitutes a central element of a broader and ambitious initiative of the European Commission (EC) to regulate the data economy through the erection of a new general regulatory framework for data and digital markets. The resulting framework may be represented as a model of governance between a pure market-driven model and a fully regulated approach, thereby combining elements that traditionally belong to private law (e.g., property rights, contracts) and public law (e.g., regulatory authorities, limitation of contractual freedom). This article discusses

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Jo, Barratt, Rono Serah, and Walsh Paul. "Frictionless Data and Data Packages." June 29, 2018. https://doi.org/10.5281/zenodo.1301152.

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There is significant friction in the acquisition, sharing, and reuse of research data. It is estimated that eighty percent of data analysis is invested in the cleaning and mapping of data. This friction hampers researchers not well versed in data processing techniques from reusing an ever-increasing amount of research data available on the web and within scientific data repositories. Frictionless Data is an ongoing project at Open Knowledge International focused on removing this friction in a variety of circumstances. We are doing this by developing a set of tools, specifications, and best pra

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Md, Hamim Al Mizan, and Asraf-Ud-Doula-khan Mohammad. "Data Mining." February 18, 2022. https://doi.org/10.5281/zenodo.6132472.

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Data mining is the process of extracting knowledge from data by using data analysis tools to conduct pattern recognition and predictive modeling tasks. This article explains how to use data mining technology to analyze educational data from Croatian universities. The event log acquired from the real e-course e-learning environment was used for the analysis. Cluster analysis and decision trees were used as data mining approaches in this investigation. Cluster analysis divided the students into groups based on their behavior when using the material. It was a method that I was interested in gener

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