Relevant bibliographies by topics / Data frameworks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data frameworks'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Data frameworks"

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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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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&rsquo;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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Dissertations / Theses on the topic "Data frameworks"

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Nyström, Simon, and Joakim Lönnegren. "Processing data sources with big data frameworks." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188204.

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Big data is a concept that is expanding rapidly. As more and more data is generatedand garnered, there is an increasing need for efficient solutions that can be utilized to process all this data in attempts to gain value from it. The purpose of this thesis is to find an efficient way to quickly process a large number of relatively small files. More specifically, the purpose is to test two frameworks that can be used for processing big data. The frameworks that are tested against each other are Apache NiFi and Apache Storm. A method is devised in order to, firstly, construct a data flow and sec
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Venumuddala, Ramu Reddy. "Distributed Frameworks Towards Building an Open Data Architecture." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801911/.

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Data is everywhere. The current Technological advancements in Digital, Social media and the ease at which the availability of different application services to interact with variety of systems are causing to generate tremendous volumes of data. Due to such varied services, Data format is now not restricted to only structure type like text but can generate unstructured content like social media data, videos and images etc. The generated Data is of no use unless been stored and analyzed to derive some Value. Traditional Database systems comes with limitations on the type of data format schema, a
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Nambiar, Arun N. "Data Exchange in Multi-Disciplinary Design Optimization frameworks." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1088189791.

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Randhawa, Tarlochan Singh. "Incorporating Data Governance Frameworks in the Financial Industry." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6478.

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Data governance frameworks are critical to reducing operational costs and risks in the financial industry. Corporate data managers face challenges when implementing data governance frameworks. The purpose of this multiple case study was to explore the strategies that successful corporate data managers in some banks in the United States used to implement data governance frameworks to reduce operational costs and risks. The participants were 7 corporate data managers from 3 banks in North Carolina and New York. Servant leadership theory provided the conceptual framework for the study. Methodolog
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Edling, Erik, and Emil Östergren. "An analysis of microservice frameworks." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138034.

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Microservice architecture has entered the industry to solve some of the problems with the monolithic architecture. However, this architecture comes with its own set of problems. In order to solve the microservice architecture problems while also providing additional functionalities, microservice frameworks have been developed. In this thesis, microservice frameworks were compared and thereafter two were chosen to implement a small part of a large monolithic system as microservices. This was done in order to see how well they could implement the different functionalities that the frameworks pro
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Bao, Shunxing. "Algorithmic Enhancements to Data Colocation Grid Frameworks for Big Data Medical Image Processing." Thesis, Vanderbilt University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13877282.

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<p> Large-scale medical imaging studies to date have predominantly leveraged in-house, laboratory-based or traditional grid computing resources for their computing needs, where the applications often use hierarchical data structures (e.g., Network file system file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance for laboratory-based approaches reveal that performance is impeded by standard network switches since typical processing can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. On the
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Price, Simon. "Higher-order frameworks for profiling and matching heterogeneous data." Thesis, University of Bristol, 2014. http://hdl.handle.net/1983/841f5b9b-c15f-4bb0-ba0d-e9edc752be55.

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This Thesis brings together complementary research from higher-order computational logic and workflow systems to investigate software and theoretical frameworks for profiling and matching heterogeneous data. One motivating use case is submission sifting, which matches submitted conference or journal papers to potential peer reviewers based on the similarity between the paper's abstract and the reviewer's publications as found in online bibliographic databases. Inspired by e-Science workflows, we introduce the SubSift submission sifting framework for developing web-based research intelligence a
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Kokkonda, Vijay, and Krishna Sandeep Taduri. "Managing Data Location in Cloud Computing (Evaluating Data localization frameworks in Amazon Web Services)." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2685.

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Context: Cloud Computing is an emerging technology where present IT is trending towards it. Many enterprises and users are still uncomfortable to adopt Cloud Computing as it uncovers many potential and critical threats which remind the most concerned security issue to consider. As data is migrated between different data centers dispersed globally, security for data is a very important issue. In Cloud environment Cloud user should be aware of the physical location of the data to ensure whether their data reside within certain jurisdiction as there are different data privacy laws. Evaluating dif
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Baalbaki, Hussein. "Designing Big Data Frameworks for Quality-of-Data Controlling in Large-Scale Knowledge Graphs." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS697.

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Les Knowledge Graphs (KG) sont la représentation la plus utilisée d'informations structurées sur un domaine particulier, composée de milliards de faits sous la forme d'entités (nœuds) et de relations (bords) entre eux. De plus, les informations de type sémantique des entités sont également contenues dans les KG. Le nombre de KG n'a cessé d'augmenter au cours des 20 dernières années dans divers domaines, notamment le gouvernement, la recherche universitaire, les domaines biomédicaux, etc. Les applications basées sur l'apprentissage automatique qui utilisent les KG incluent la liaison d'entités,
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Chen, Alexander Y. "Tools and frameworks for data abstraction in a performance context." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112835.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 109-111).<br>As data science is impacting more and more fields and proving to be effective in a wide variety of applications, the importance of easy-to-understand, high-performance data science tools is growing. Tools tend t
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Books on the topic "Data frameworks"

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Leonard, Andy, and Kent Bradshaw. SQL Server Data Automation Through Frameworks. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6213-9.

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Oscar, Nigro Hector, Cisaro Sandra Gonzalez, and Xodo Daniel, eds. Date mining with ontologies: Implementations, findings and frameworks. Information Science Reference, 2008.

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Balfour, James A. D. Computer analysis of structural frameworks. 2nd ed. Blackwell Scientific Publications, 1992.

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Sakr, Nourhan. Data-Driven Combinatorial Optimization and Efficient Machine Learning Frameworks. [publisher not identified], 2019.

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August-Wilhelm, Scheer, and Scheer August-Wilhelm, eds. ARIS--business process frameworks. 2nd ed. Springer, 1998.

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A, O'Brien Frances, and Dyson Robert G, eds. Supporting strategy: Frameworks, methods and models. John Wiley & Sons, 2007.

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Abawajy, Jemal H. Internet and distributed computing advancements: Theoretical frameworks and practical applications. Information Science Reference, 2012.

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International IFIP WG 10.5 Working Conference on Electronic Design Automation Frameworks (4th 1994 Gramado, Rio Grande do Sul, Brazil). Electronic design automation frameworks: Proceedings of the Fourth International IFIP WG 10.5 Working Conference on Electronic Design Automation Frameworks. Chapman & Hall, 1995.

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B, Cerrito Patricia, ed. Cases on health outcomes and clinical data mining: Studies and frameworks. Medical Information Science Reference, 2010.

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Susan, Rodrigues, ed. Using analytical frameworks for classroom research: Collecting data and analysing narrative. Routledge, 2010.

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Book chapters on the topic "Data frameworks"

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Wolf, Pieter. "Data Modeling." In Cad Frameworks. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2768-8_4.

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Barnes, Timothy J., David Harrison, A. Richard Newton, and Rick L. Spickelmier. "Data Representation." In Electronic CAD Frameworks. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3558-4_3.

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Barnes, Timothy J., David Harrison, A. Richard Newton, and Rick L. Spickelmier. "Data Management." In Electronic CAD Frameworks. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3558-4_4.

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Christen, Peter, Thilina Ranbaduge, and Rainer Schnell. "Regulatory Frameworks." In Linking Sensitive Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_2.

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Quasim, Mohammad Tabrez, Mohammad Ayoub Khan, Fahad Algarni, Abdullah Alharthy, and Goram Mufareh M. Alshmrani. "Blockchain Frameworks." In Studies in Big Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38677-1_4.

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Garn, Wolfgang. "Data Analytics Frameworks." In Data Analytics for Business. Routledge, 2024. http://dx.doi.org/10.4324/9781003336099-7.

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Kakarla, Ramcharan, Sundar Krishnan, Balaji Dhamodharan, and Venkata Gunnu. "Modeling Frameworks." In Applied Data Science Using PySpark. Apress, 2024. https://doi.org/10.1007/979-8-8688-0820-3_11.

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DeBellis, Michael, Livia Pinera, and Christopher Connor. "Interoperability Frameworks." In Data Science with Semantic Technologies. CRC Press, 2023. http://dx.doi.org/10.1201/9781003310785-13.

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Frampton, Michael. "Frameworks." In Complete Guide to Open Source Big Data Stack. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-2149-5_8.

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Didenko, Anton, Natalia Jevglevskaja, and Ross P. Buckley. "Customer perspective: the quest for customer trust." In Customer Data Sharing Frameworks. Routledge, 2024. http://dx.doi.org/10.4324/9781003414216-5.

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Conference papers on the topic "Data frameworks"

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Gayar, Ismail El, Hesham Hassan, and Khaled T. Wassif. "Data Quality Frameworks: A Systematic Review." In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10730114.

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Ramdass, Karthikeyan, Arun Mulka, Nitin Agarwal, Sugeetha Avvaru, Gopal Kumar Gupta, and Sansar Singh Chauhan. "Scalable Performance Testing for Distributed Big Data Frameworks." In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT). IEEE, 2025. https://doi.org/10.1109/ce2ct64011.2025.10939442.

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N S, Vasanth, Selva Jothi M, and Pavithra S. "Secure File Sharing using Blockchain-Based Frameworks." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011863.

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Mishra, Vaishali, Ujjwal Karn, Vasanth Rajendran, Monojit Banerjee, and Harshal Darade. "Ethical Frameworks for Artificial Intelligence: A Comparative Study." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10986906.

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Molnar, Sam, J. D. Laurence-Chasen, Yuhan Duan, Julie Bessac, and Kristi Potter. "Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models." In 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks. IEEE, 2024. http://dx.doi.org/10.1109/uncertaintyvisualization63963.2024.00006.

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Stokes, Chase, Chelsea Sanker, Bridget Cogley, and Vidya Setlur. "Voicing Uncertainty: How Speech, Text, and Visualizations Influence Decisions with Data Uncertainty." In 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks. IEEE, 2024. http://dx.doi.org/10.1109/uncertaintyvisualization63963.2024.00007.

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Twaty, Muaz, Amine Ghrab, and Sabri Skhiri. "GraphOpt: a Framework for Automatic Parameters Tuning of Graph Processing Frameworks." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006320.

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Chandarana, Parth, and M. Vijayalakshmi. "Big Data analytics frameworks." In 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA). IEEE, 2014. http://dx.doi.org/10.1109/cscita.2014.6839299.

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Cao, Paul Y., Gang Li, Guoxing Chen, and Biao Chen. "Mobile Data Collection Frameworks." In MobiHoc'15: The Sixteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing. ACM, 2015. http://dx.doi.org/10.1145/2757384.2757396.

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Londońo Peláez, Jorge Mario, María Alejandra Echavarria Arcila, Leonardo Betancur Agudelo, Diana Patricia Giraldo Ramirez, and Laura Orozco Salazar. "Shared-Data Governance Frameworks." In 15th International Conference on Society and Information Technologies. International Institute of Informatics and Cybernetics, 2024. http://dx.doi.org/10.54808/icsit2024.01.65.

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Reports on the topic "Data frameworks"

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Pulivarti, Ronald. Genomic Data Cybersecurity and Privacy Frameworks Community Profile. National Institute of Standards and Technology, 2024. https://doi.org/10.6028/nist.ir.8467.2pd.

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Conway, David. Data Governance: Frameworks and Approaches in the Current Marketplace. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-478.

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Iyer, Maithili, Hannah Stratton, Sangeeta Mathew, Satish Kumar, Paul Mathew, and Mohini Singh. Review of Building Data Frameworks across Countries: Lessons for India. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1373279.

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Bandlow, Alisa, Katherine A. Jones, and Vanessa N. Vargas. SNL Lesson Learned and Guidance for Data Repositories and Analytic Frameworks. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1463073.

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Gorzig, Marina. Disparities, Discrimination, and Data (Free Seminar). Instats Inc., 2025. https://doi.org/10.61700/mcl6o8g3dmujp1972.

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This workshop provides an essential overview of methodologies for analyzing disparities and discrimination in social science research, focusing on the practical use of Stata for data analysis. Led by Dr Marina Gorzig, participants will introduce statistical frameworks, data preparation, regression analysis, and ethical considerations, equipping them with the skills to critically assess and contribute to this field.
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Kalkar, Uma, and Natalia González Alarcón. Facilitating Data Flows through Data Collaboratives: A Practical Guide to Designing Valuable, Accessible, and Responsible Data Collaboratives. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005185.

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Data is an indispensable asset in today's society, but its production and sharing are subject to well-known market failures. Among these: neither economic nor academic markets efficiently reward costly data collection and quality assurance efforts; data providers cannot easily supervise the appropriate use of their data; and, correspondingly, users have weak incentives to pay for, acknowledge, and protect data that they receive from providers. Data collaboratives are a potential non-market solution to this problem, bringing together data providers and users to address these market failures. Th
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Paul, Prince M., Madhan Jeyaraman, Arulkumar Nallakumarasamy, Naveen Jeyaraman, and Manish Khanna. Medicolegal implications and regulatory frameworks of regenerative orthopaedics - A systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.4.0022.

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Review question / Objective: Analyzing the literature and data on medicolegal implications, issues, and challenges of regenerative orthopaedics globally as well as in India. P – Published literature on medicolegal implications and regulatory frameworks on regenerative orthopaedics. I – Regenerative therapies in orthopaedics. C – No comparator group. O – Medical legal implication of regenerative orthopaedics. Condition being studied: Medicolegal implications and regulatory frameworks in regenerative orthopaedics.
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Valencia, Oscar, and André Martínez. Enhancing Fiscal Resilience: Medium-Term Frameworks for Managing Emerging Risks. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013503.

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Climate change poses significant well-known risks to fiscal sustainability in Latin America and the Caribbean, such as annual losses of up to 1.7 percent of GDP due to extreme weather events, the devaluation of carbon-intensive assets caused by the transition to a low-carbon economy, and the loss of fiscal revenues linked to fossil fuels. This study describes how governments can integrate green medium-term fiscal frameworks (MTFFs) into their fiscal planning to mitigate these risks. Green MTFFs combine effective carbon pricing, sustainable reforms, and strategic investments in green infrastruc
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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,
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Zyphur, Michael. Instrumental Variable Analysis Using Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/vr95hvyzh3cux469.

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This seminar introduces estimating causal effects with observational data using instrumental variables in Mplus, both within a path analysitic and SEM framework. Basic introductions are first provided for path analysis and CFA, before describing instrumental variable methods in both observed- and latent-variable frameworks. Traditional bootstrapping and Bayesian methods are explored for estimation and hypothesis testing. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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