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Journal articles on the topic 'Data frameworks'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

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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Karan, Patel, Sakaria Yash, and Bhadane Chetashri. "Real Time Data Processing Frameworks." International Journal of Data Mining & Knowledge Management Process (IJDKP) 5, no. 5 (2019): 49–63. https://doi.org/10.5281/zenodo.3406010.

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On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being

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Häußler, Helena. "Data Ethics Frameworks." Information - Wissenschaft & Praxis 72, no. 5-6 (2021): 291–98. http://dx.doi.org/10.1515/iwp-2021-2178.

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Zusammenfassung Zuletzt veröffentlichten viele Organisationen ethische Richtlinien, um ihre Haltung gegen Diskriminierung durch Algorithmen zu betonen. Vier dieser Frameworks werden mithilfe der kritischen Diskursanalyse untersucht. Ziel ist es, die darin vermittelten Werte und Wertkonflikte zu identifizieren. Die Ergebnisse weisen darauf hin, dass etablierte Werte aus der Computer- und Informationsethik aufgegriffen und bestehende Machtstrukturen zwischen Akteuren verstärkt werden.

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Reddy Koilakonda, Raghunath. "Implementing Data Governance Frameworks for Enhanced Decision Making." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 1239–43. http://dx.doi.org/10.21275/sr24618105346.

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Joseph, Aaron Tsapa. "Leading the Data-Driven Enterprise: Integrating Robust Data Governance and Quality Frameworks for Sustainable Success." Journal of Scientific and Engineering Research 8, no. 6 (2021): 135–41. https://doi.org/10.5281/zenodo.11220731.

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This paper underscores data governance and quality control system development strategies to ensure sustainable data-driven business operations. It focuses on how to handle data growth and how data is owned and emphasizes the need for data management and assurance of quality data. The aim has been discussed to reveal those pitfalls and to teach the best way to use the data. Data governance and quality frameworks need to be enacted, meaning the basis of that is data reliability, acceptance, and utility in decision-making informed by deep analytics and innovation. It will be a paper compiling var

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Tongeren, Jan W. van. "Designing analytical data frameworks." Review of Income and Wealth 50, no. 2 (2004): 279–97. http://dx.doi.org/10.1111/j.0034-6586.2004.00126.x.

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Abhijit, Joshi. "Scalable Data Integration Frameworks: Enhancing Data Cohesion in Complex Systems." Journal of Scientific and Engineering Research 9, no. 10 (2022): 83–94. https://doi.org/10.5281/zenodo.12772820.

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Data integration in large-scale environments is crucial for organizations to leverage diverse data sources for advanced analytics and decision-making. This paper delves into the latest frameworks and methodologies designed to enhance data cohesion in complex systems. We explore the challenges associated with integrating heterogeneous data sources and present scalable solutions to achieve seamless data integration. The study highlights advanced techniques and tools, including ETL processes, data lakes, and modern data integration platforms. Through detailed methodologies, pseudocode, and illust

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Abhijit, Joshi. "Scalable Data Integration Frameworks: Enhancing Data Cohesion in Complex Systems." Journal of Scientific and Engineering Research 9, no. 10 (2022): 83–94. https://doi.org/10.5281/zenodo.13337884.

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<strong>Abstract </strong>Data integration in large-scale environments is crucial for organizations to leverage diverse data sources for advanced analytics and decision-making. This paper delves into the latest frameworks and methodologies designed to enhance data cohesion in complex systems. We explore the challenges associated with integrating heterogeneous data sources and present scalable solutions to achieve seamless data integration. The study highlights advanced techniques and tools, including ETL processes, data lakes, and modern data integration platforms. Through detailed methodologi

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Miller, Russell, Sai Hin Matthew Chan, Harvey Whelan, and João Gregório. "A Comparison of Data Quality Frameworks: A Review." Big Data and Cognitive Computing 9, no. 4 (2025): 93. https://doi.org/10.3390/bdcc9040093.

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This study reviews various data quality frameworks that have some form of regulatory backing. The aim is to identify how these frameworks define, measure, and apply data quality dimensions. This review identified generalisable frameworks, such as TDQM, ISO 8000, and ISO 25012, and specialised frameworks, such as IMF’s DQAF, BCBS 239, WHO’s DQA, and ALCOA+. A standardised data quality model was employed to map the dimensions of the data from each framework to a common vocabulary. This mapping enabled a gap analysis that highlights the presence or absence of specific data quality dimensions acro

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Sivananda, Reddy Julakanti, Satya kiranmayee Sattiraju Naga, and Julakanti Rajeswari. "Data Protection through Governance Frameworks." Journal of Computational Analysis and Applications 31, no. 1 (2023): 158–62. https://doi.org/10.5281/zenodo.14715381.

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In today’s increasingly digital world, data has become one of the most valuable assets for organizations. With the rise in cyberattacks, data breaches, and the stringent regulatory environment, it is imperative to adopt robust data protection strategies. One such approach is the use of governance frameworks, which provide structured guidelines, policies, and processes to ensure data protection, compliance, and ethical usage. This paper explores the role of data governance frameworks in protecting sensitive information and maintaining organizational data security. It delves into the princ

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N., Dr Krishnaraj. "Improved Distributed Frameworks to Incorporate Big Data through Deep Learning." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (2020): 332–38. http://dx.doi.org/10.5373/jardcs/v12sp3/20201269.

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Karim, Saadia, Tariq Rahim Soomro, and S. M. Aqil Burney. "Spatiotemporal Aspects of Big Data." Applied Computer Systems 23, no. 2 (2018): 90–100. http://dx.doi.org/10.2478/acss-2018-0012.

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Abstract Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient results and, therefore, many different types of frameworks have been introduced in cooperate world. In the present research, a qualitative approach is used to present the framework classification in two categories: architecture and features. Fr

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Shahat Osman, Ahmed M., and Ahmed Elragal. "Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case." Smart Cities 4, no. 1 (2021): 286–313. http://dx.doi.org/10.3390/smartcities4010018.

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Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles add

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Samatha, P. K., and Mohamed Rafi Dr. "Expelling Information of Events from Critical Public Space using Social Sensor Big Data." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 445–48. https://doi.org/10.5281/zenodo.3589900.

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Open foundation frameworks give a significant number of the administrations that are basic to the wellbeing, working, and security of society. A considerable lot of these frameworks, in any case, need persistent physical sensor checking to have the option to recognize disappointment occasions or harm that has struck these frameworks. We propose the utilization of social sensor enormous information to recognize these occasions. We center around two primary framework frameworks, transportation and vitality, and use information from Twitter streams to identify harm to spans, expressways, gas line

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Khalid, Madiha, and Muhammad Murtaza Yousaf. "A Comparative Analysis of Big Data Frameworks: An Adoption Perspective." Applied Sciences 11, no. 22 (2021): 11033. http://dx.doi.org/10.3390/app112211033.

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The emergence of social media, the worldwide web, electronic transactions, and next-generation sequencing not only opens new horizons of opportunities but also leads to the accumulation of a massive amount of data. The rapid growth of digital data generated from diverse sources makes it inapt to use traditional storage, processing, and analysis methods. These limitations have led to the development of new technologies to process and store very large datasets. As a result, several execution frameworks emerged for big data processing. Hadoop MapReduce, the pioneering framework, set the ground fo

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Al-Badi, Ali, Ali Tarhini, and Asharul Islam Khan. "Exploring Big Data Governance Frameworks." Procedia Computer Science 141 (2018): 271–77. http://dx.doi.org/10.1016/j.procs.2018.10.181.

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17

Mannila, Heikki. "Theoretical frameworks for data mining." ACM SIGKDD Explorations Newsletter 1, no. 2 (2000): 30–32. http://dx.doi.org/10.1145/846183.846191.

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Berzal, Fernando, Ignacio Blanco, Juan-Carlos Cubero, and Nicolas Marin. "Component-based data mining frameworks." Communications of the ACM 45, no. 12 (2002): 97–100. http://dx.doi.org/10.1145/585597.585624.

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19

Marlowe, T. J., and B. G. Ryder. "Properties of data flow frameworks." Acta Informatica 28, no. 2 (1990): 121–63. http://dx.doi.org/10.1007/bf01237234.

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20

Chen, Yu Ke, and Tai Xiang Zhao. "Data Collection for Medical Data Warehouse Research." Advanced Materials Research 717 (July 2013): 816–19. http://dx.doi.org/10.4028/www.scientific.net/amr.717.816.

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We propose an easy method for data collection and central data warehouse design. This method can be used with or without other software development frameworks.We explain thoroughly those aspects that influenced the methodology building.The methodology is defined by four steps, which can be aligned with various iterative development frameworks. We describe here the alignment of our methodology with the RUP(rational unified process) framework.

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Mao, Zijun, Jingyi Wu, Yali Qiao, and Hong Yao. "Government data governance framework based on a data middle platform." Aslib Journal of Information Management 74, no. 2 (2021): 289–310. http://dx.doi.org/10.1108/ajim-03-2021-0068.

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PurposeThe present paper constructed a new framework for government data governance based on the concept of a data middle platform to elicit the detailed requirements and functionalities of a government data governance framework.Design/methodology/approachFollowing a three-cycle activity, the design science research (DSR) paradigm was used to develop design propositions. The design propositions are obtained based on a systematic literature review of government data governance and data governance frameworks. Cases and experts further assessed the effectiveness of the implementation of the artif

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Vaghani Divyeshkumar. "Data sovereignty frameworks for space-based data platforms." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 653–64. http://dx.doi.org/10.30574/wjarr.2024.22.3.1770.

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Despite widespread support, data sovereignty is still in its nascent stages. Consequently, current methods for transferring sovereign data are underdeveloped and could be enhanced. To address these issues, this paper introduces an architecture that utilizes edge computing resources to host both the data and the connector. Additionally, we develop a system based on this architecture, enabling the transfer of sovereign data between users using space-based data platforms. To assess our approach, we deploy our system in a real-world environment with users located in different countries and conduct

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Vaghani, Divyeshkumar. "Data sovereignty frameworks for space-based data platforms." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 653–64. https://doi.org/10.5281/zenodo.14733683.

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Despite widespread support, data sovereignty is still in its nascent stages. Consequently, current methods for transferring sovereign data are underdeveloped and could be enhanced. To address these issues, this paper introduces an architecture that utilizes edge computing resources to host both the data and the connector. Additionally, we develop a system based on this architecture, enabling the transfer of sovereign data between users using space-based data platforms. To assess our approach, we deploy our system in a real-world environment with users located in different countries and conduct

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24

Srinivasa, Rao Putta. "COMPARATIVE ANALYSIS OF BIG DATA USING HADOOP FRAMEWORK." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 4 (2019): 477–80. https://doi.org/10.5281/zenodo.2657666.

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Big data is an important concept in the field of information technology where companies and organization take advantage of the data that they have stored to find meaningful patterns and predictions to help them in making informed decisions. Big data analysis involves the use of advanced tools and techniques that are used in the processing the large volumes of data that is produced by the organization (Sammer, 2012, p. 23). The Hadoop framework is an important framework in enabling easy and efficient processing of large data sets thus making it one of the most popular big data analytics fr

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A. SULTAN, Nagham, and Dhuha B. ABDULLAH. "A COMPREHENSIVE STUDY ON BIG DATA FRAMEWORKS." MINAR International Journal of Applied Sciences and Technology 05, no. 01 (2023): 34–48. http://dx.doi.org/10.47832/2717-8234.14.4.

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With the advent of cloud computing technology, the generation of data from various sources has increased during the last few years. The current data processing technology must handle the enormous volumes of newly created data. Therefore, the studies in the literature have concentrated on big data, which has enormous volumes of almost unstructured data. Dealing with such data needs well-designed frameworks that fulfil developers’ needs and fit colourful purposes. Moreover, these frameworks can use for storing, processing, structuring, and analyzing data. The main problem facing cloud computing

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Ake, Anuoluwa. "Enhancing US Energy Sector Performance Through Advanced Data-Driven Analytical Frameworks." International Journal of Research Publication and Reviews 5, no. 12 (2024): 3336–56. https://doi.org/10.55248/gengpi.5.1224.250111.

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Mositsa, Ramakolote Judas, John Andrew Van der Poll, and Cyrille Dongmo. "Towards a Conceptual Framework for Data Management in Business Intelligence." Information 14, no. 10 (2023): 547. http://dx.doi.org/10.3390/info14100547.

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Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate data from several operational databases. BI further incorporates a combination of analytics, data management, and reporting tools, together with associated methodologies for managing and analyzing data. An important goal of BI initiatives is to improve business decision-making for organizatio

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Miles, Mathew. "Using web2py Python framework for creating data-driven web applications in the academic library." Library Hi Tech 34, no. 1 (2016): 164–71. http://dx.doi.org/10.1108/lht-08-2015-0082.

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Purpose – Many libraries have a need to develop their own data-driven web applications, but their technical staff often lacks the required specialized training – which includes knowledge of SQL, a web application language like PHP, JavaScript, CSS, and jQuery. The web2py framework greatly reduces the learning curve for creating data-driven websites by focussing on three main goals: ease of use; rapid development; and security. web2py follows a strict MVC framework where the controls and web templates are all written in pure Python. No additional templating language is required. The paper aims

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Kumar, Prashant, and Khushboo Pandeya. "Big Data and Distributed Data Mining: An Example of Future Networks." International Journal of Advance Research and Innovation 1, no. 2 (2013): 12–15. http://dx.doi.org/10.51976/ijari.121303.

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This paper describes the perspective on the analytics of big data generated by sensors and devices on the edge of networks. The paper includes a discussion of the importance of data at the edge of networks where some of ―biggest‖ big data is generated. Also quick overview of emerging technologies, including distributed frameworks such as the Apache Hadoop framework and Apache* Map Reduce.

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Researcher. "DECENTRALIZED DATA ECOSYSTEMS: A COMPREHENSIVE ANALYSIS OF STRATEGIES FOR ENHANCED RESILIENCE AND COMPLIANCE." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 321–34. https://doi.org/10.5281/zenodo.13838649.

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Data decentralization has emerged as a critical paradigm in modern data management, offering enhanced scalability, fault tolerance, and compliance with localized regulations. However, implementing effective decentralization strategies presents significant challenges in maintaining data accessibility, consistency, and security across distributed systems. This article proposes a comprehensive framework for achieving robust data decentralization, synthesizing best practices and leveraging cutting-edge technologies. Through a systematic review of existing literature and analysis of industry c

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Moses, T., and H. C. Inyiama. "Survey of big data programming frameworks." Journal of Computer Science and Its Application 26, no. 2 (2020): 84. http://dx.doi.org/10.4314/jcsia.v26i2.9.

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Cichy, Corinna, and Stefan Rass. "An Overview of Data Quality Frameworks." IEEE Access 7 (2019): 24634–48. http://dx.doi.org/10.1109/access.2019.2899751.

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Pivarski, Jim, David Lange, and Peter Elmer. "Nested data structures in array frameworks." Journal of Physics: Conference Series 1525 (April 2020): 012053. http://dx.doi.org/10.1088/1742-6596/1525/1/012053.

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Oursatyev, A. A. "Some Frameworks for Analytics Big Data." Upravlâûŝie sistemy i mašiny, no. 3 (263) (June 2016): 29–42. http://dx.doi.org/10.15407/usim.2016.03.029.

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Sampath Kini K. "Exploring Real-Time Data Processing Using Big Data Frameworks." Communications on Applied Nonlinear Analysis 31, no. 8s (2024): 620–34. http://dx.doi.org/10.52783/cana.v31.1561.

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Big data frameworks that weaken the throughput of data processing, allowing for real-time data processing like Apache Spark, Kafka, and Flink are other developments. Regarding quick decisions by each measurement, the scalability, fault tolerance, and latency of three architectures Here each stream processing, lambda, and Kappa have been further studied and measured to approach a conclusion. Based on a methodical survey of literature, performance laws, and case studies, all three frameworks and architectures pros and cons measure us, which can then be used for separate operations use situations

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Mr., Ketan Bagade, Anjali Gharat Mrs., and Helina Tandel Mrs. "A Review Paper on Big Data and Hadoop for Data Science." International Journal of Trend in Scientific Research and Development 4, no. 1 (2019): 1216–21. https://doi.org/10.5281/zenodo.3610061.

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Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A R

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Chawla, Karan. "An Examination of Big Data Analytics Frameworks for Targeted Cyber-Attack Detection." International Journal of Scientific Engineering and Research 11, no. 5 (2023): 39–44. https://doi.org/10.70729/se23513130704.

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Adebanjo (DoP), Abiodun, Gloria Chigbu, and Christopher M. Osazuwa. "EFFECT OF DATA PROTECTION FRAMEWORKS AGAINST CYBERCRIMES ON CYBER SECURITY IN NIGERIA." American Journal of Political Science Law and Criminology 6, no. 9 (2024): 64–84. http://dx.doi.org/10.37547/tajpslc/volume06issue09-07.

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The paper Effect of Data Security Frameworks against Cybercrimes and Cyber Security in Nigeria critically examines the various data protection frameworks in Nigeria and how these are impacting curtailing cybercrimes in the country. The paper examined the institutions responsible for data security and how they carry it out. The work utilized both the secondary and primary sources of data collection. Key Informant Interview (KII) and the observation method were deployed to collect primary data for the study. Information on data protection was also collected from (NDPC) and other relevant agencie

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Fadiya, Samson, and Arif Sari. "The importance of big data technology." International Journal of Engineering & Technology 7, no. 4.5 (2018): 485. http://dx.doi.org/10.14419/ijet.v7i4.5.21139.

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The adoption of Web 2.0 technologies, Internet of Things, etc. by individuals and organization has led to an explosion of data. As it stands, existing Relational Database Management Systems (RDBMSs) are incapable of handling this deluge of data. The term Big Data was coined to represent these vast, fast and complex datasets that regular RDBMSs could not handle. Special tools or frameworks were developed to deal with processing, managing and storing this big data. These tools are capable of functioning in distributed industry- standard environments thereby maintaining efficiency and effectivene

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Lynn Segarra, Laura, Hamed Almalki, John Elabd, et al. "A Framework for Boosting Revenue Incorporating Big Data." Journal of Innovation Management 4, no. 1 (2016): 39–68. http://dx.doi.org/10.24840/2183-0606_004.001_0005.

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Complex industry partnerships, innovative strategies, and cross-cutting industry competition, challenge business leaders in making strategic and operational decisions that support growth and competitiveness. Companies seeking to inform their business decisions by leveraging “big data” face challenges in processing and analyzing such large and rapid datasets. However leveraging big data can create value for businesses. Although various frameworks exist for implementing analytics, few accommodate the implementation of big data analytics. Our goal is to develop a framework by studying big data on

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Ghawana, Tarun, Lyubka Pashova, and Sisi Zlatanova. "Geospatial Data Utilisation in National Disaster Management Frameworks and the Priorities of Multilateral Disaster Management Frameworks: Case Studies of India and Bulgaria." ISPRS International Journal of Geo-Information 10, no. 9 (2021): 610. http://dx.doi.org/10.3390/ijgi10090610.

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Facing the increased frequency of disasters and resulting in massive damages, many countries have developed their frameworks for Disaster Risk Management (DRM). However, these frameworks may differ concerning legal, policy, planning and organisational arrangements. We argue that geospatial data is a crucial binding element in each national framework for different stages of the disaster management cycle. The multilateral DRM frameworks, like the Sendai Framework 2015–2030 and the United Nations Committee of Experts on Global Geospatial Information Management (UNGGIM) Strategic Framework on Geos

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Krishna, Chenchu Murali, Kirti Ruikar, and Kumar Neeraj Jha. "Determinants of Data Quality Dimensions for Assessing Highway Infrastructure Data Using Semiotic Framework." Buildings 13, no. 4 (2023): 944. http://dx.doi.org/10.3390/buildings13040944.

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The rapid accumulation of highway infrastructure data and their widespread reuse in decision-making poses data quality issues. To address the data quality issue, it is necessary to comprehend data quality, followed by approaches for enhancing data quality and decision-making based on data quality information. This research aimed to identify the critical data quality dimensions that affect the decision-making process of highway projects. Firstly, a state-of-the-art review of data quality frameworks applied in various fields was conducted to identify suitable frameworks for highway infrastructur

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Thimmareddy, Avinash Reddy. "Mastering Data Pipeline Frameworks: A Comprehensive Guide." European Journal of Computer Science and Information Technology 13, no. 48 (2025): 142–53. https://doi.org/10.37745/ejcsit.2013/vol13n48142153.

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The rapid evolution of data pipeline frameworks has fundamentally transformed how organizations process and manage their data assets. These frameworks serve as critical infrastructure components, enabling automated data movement, transformation, and integration across diverse environments. The increasing complexity of data ecosystems has driven innovations in pipeline architecture, emphasizing scalability, reliability, and security. Modern implementations focus on real-time processing capabilities, automated quality controls, and robust error handling mechanisms. The integration of privacy and

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Balaji, K., and S. S. Manikandasaran. "Data Security and Deduplication Framework for Securing and Deduplicating Users’ Data in Public and Private Cloud Environment." Journal of Scientific Research 14, no. 1 (2022): 153–65. http://dx.doi.org/10.3329/jsr.v14i1.54063.

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Maintaining the security of data stored in the public or private cloud is a more tedious task. The cloud is the only arrangement for storing enormous amounts of data, but there is a possibility of storing the same data more than once. The traditional security system generates different unreadable data for the same readable content of a file. Therefore, it is necessary to address data security of the cloud and duplication in cloud storage. This paper concentrates on developing a data security and deduplication framework with different security techniques and mechanisms to address the said diffi

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Do, Nam H., Tien Van Do, Lóránt Farkas, and Csaba Rotter. "Provisioning Input and Output Data Rates in Data Processing Frameworks." Journal of Grid Computing 18, no. 3 (2020): 491–506. http://dx.doi.org/10.1007/s10723-020-09508-0.

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Abstract This paper is motivated by the need of deadline-bounded applications in live mobile network environments to obtain the guarantee and the appropriate share of an input and output (I/O) data rate. However, data processing frameworks only support the request of memory and the computing capacity at present. In this paper, we propose a solution that allows the control of disk I/O and network I/O for data processing applications in YARN and Mesos frameworks. Experimental results show that our tool can provision the I/O data rate sharing of competing data processing applications.

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Zheng, Haowen. "Research on the Application of Homomorphic Encryption and Federated Learning in the Internet of Vehicles Environment." Highlights in Science, Engineering and Technology 119 (December 11, 2024): 593–606. https://doi.org/10.54097/1dmsr222.

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The rapid growth of the Internet of Things has significantly increased data volumes, leading to heightened concerns over security risks such as data theft and leakage. As machine learning becomes increasingly integral to various applications, data security in training processes has emerged as a critical issue. The Internet of Vehicles (IoV), as a crucial branch of the IoT, faces particular challenges in securely and efficiently training data. While current machine learning frameworks enable fast and efficient data training in IoV environments, security risks remain a pressing concern. This stu

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Abuqabita, Flasteen, Razan Al-Omoush, and Jaber Alwidian. "A Comparative Study on Big Data Analytics Frameworks, Data Resources and Challenges." Modern Applied Science 13, no. 7 (2019): 1. http://dx.doi.org/10.5539/mas.v13n7p1.

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Recently, huge amount of data has been generated in all over the world; these data are very huge, extremely fast and varies in its type. In order to extract the value from this data and make sense of it, a lot of frameworks and tools are needed to be developed for analyzing it. Until now a lot of tools and frameworks were generated to capture, store, analyze and visualize it. In this study we categorized the existing frameworks which is used for processing the big data into three groups, namely as, Batch processing, Stream analytics and Interactive analytics, we discussed each of them in detai

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Szabari, Bence, and Attila Kiss. "Word pattern prediction using Big Data frameworks." Acta Universitatis Sapientiae, Informatica 12, no. 1 (2020): 51–69. http://dx.doi.org/10.2478/ausi-2020-0004.

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AbstractUsing software applications or services, which provide word or even word pattern recommendation service has become part of our lives. Those services appear in many form in our daily basis, just think of our smartphones keyboard, or Google search suggestions and this list can be continued. With the help of these tools, we can not only find the suitable word that fits into our sentence, but we can also express ourselves in a much more nuanced, diverse way. To achieve this kind of recommendation service, we use an algorithm which is capable to recommend word by word pattern queries. Word

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Babita Kumari. "Intelligent Data Governance Frameworks : A Technical Overview." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 141–54. http://dx.doi.org/10.32628/cseit24106161.

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This comprehensive article explores the transformative potential of AI-driven data governance frameworks in addressing modern data management challenges. The article examines the limitations of traditional governance methods in the face of exponential data growth, regulatory complexities, and diverse data sources. It delves into the architecture, core components, and benefits of intelligent governance systems that leverage advanced AI technologies such as machine learning, natural language processing, and reinforcement learning. The article highlights significant improvements in operational ef

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Noorullah Shah, Syed. "A Comparative Study of Big Data Frameworks." Societal Transformation: AI and Big Data Journal 1, no. 1 (2023): 30–43. http://dx.doi.org/10.20547/aibd.231103.

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