Relevant bibliographies by topics / Data wrangling

Contents

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

Academic literature on the topic 'Data wrangling'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Data wrangling"

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Joni, Joni, and Teguh Prasandy. "Peningkatan Kualitas Data dalam Konsolidasi Data Karyawan melalui Data Wrangling." JOINS (Journal of Information System) 9, no. 2 (2025): 135–46. https://doi.org/10.33633/joins.v9i2.9423.

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Data wrangling is a critical process in data preparation that can significantly improve data quality. Effective data wrangling techniques, which consists of 6 steps i.e. Discovery, Structuring, Cleaning, Enriching, Validating, Publishing, can help Corporate Human Resource Division to ensure that their data is of high quality and ready for analysis. In this case study, we explore how effective data wrangling techniques can be used to improve data quality in employee data consolidation. We found employee data downloaded from various sources, captured incomplete, unreliable, or incorrect so that
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Goldston, David. "Big data: Data wrangling." Nature 455, no. 7209 (2008): 15. http://dx.doi.org/10.1038/455015a.

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Blankenberg, Daniel, James E. Johnson, James Taylor, and Anton Nekrutenko. "Wrangling Galaxy’s reference data." Bioinformatics 30, no. 13 (2014): 1917–19. http://dx.doi.org/10.1093/bioinformatics/btu119.

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Endel, Florian, and Harald Piringer. "Data Wrangling: Making data useful again." IFAC-PapersOnLine 48, no. 1 (2015): 111–12. http://dx.doi.org/10.1016/j.ifacol.2015.05.197.

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Bogatu, Alex, Norman W. Paton, Alvaro A. A. Fernandes, and Martin Koehler. "Towards Automatic Data Format Transformations: Data Wrangling at Scale." Computer Journal 62, no. 7 (2018): 1044–60. http://dx.doi.org/10.1093/comjnl/bxy118.

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Abstract Data wrangling is the process whereby data are cleaned and integrated for analysis. Data wrangling, even with tool support, is typically a labour intensive process. One aspect of data wrangling involves carrying out format transformations on attribute values, for example so that names or phone numbers are represented consistently. Recent research has developed techniques for synthesizing format transformation programs from examples of the source and target representations. This is valuable, but still requires a user to provide suitable examples, something that may be challenging in ap
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Prakash, Somasundaram. "Leveraging Cloud-Native Architectures for Enhanced Data Wrangling Efficiency: A Security and Performance Perspective." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 4 (2024): 17–21. https://doi.org/10.35940/ijitee.D9821.13040324.

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<strong>Abstract:</strong> In the contemporary landscape of big data analytics, cloud computing environments have emerged as pivotal platforms for data-wrangling processes, catering to the ingestion and transformation of vast datasets. This research paper explores optimization strategies for data wrangling within cloud computing environments, a critical component in the realm of big data analytics. It addresses the significant security and performance challenges encountered during data pipeline execution in cloud platforms. By proposing a novel strategy that includes executing data pipelines w
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Mäkelä, Eetu, Krista Lagus, Leo Lahti, et al. "Wrangling with Non-Standard Data." Digital Humanities in the Nordic and Baltic Countries Publications 3, no. 1 (2020): 81–96. http://dx.doi.org/10.5617/dhnbpub.11180.

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Research in the digital humanities and computational social sciences requires overcoming complexity in research data, methodology, and research questions. In this article, we show through case studies of three different digital humanities and computational social science projects, that these problems are prevalent, multiform, as well as laborious to counter. Yet, without facilities for acknowledging, detecting, handling and correcting for such bias, any results based on the material will be faulty. Therefore, we argue for the need for a wider recognition and acknowledgement of the problematic
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McNamara, Amelia, and Nicholas J. Horton. "Wrangling Categorical Data in R." American Statistician 72, no. 1 (2018): 97–104. http://dx.doi.org/10.1080/00031305.2017.1356375.

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Darono, Agung. "Data Wrangling Dalam Administrasi Pajak Indonesia: Studi Etnografi Digital." Kajian Ekonomi dan Keuangan 6, no. 1 (2024): 1–16. http://dx.doi.org/10.31685/pw16ny97.

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The Indonesian tax administration requires data analytics for the purpose of monitoring taxpayer compliance. Data wrangling is one of the stages in data analytics as a combination of various techniques and computing tools in such a way as to prepare data from various sources and formats to prepare data sets that can be analyzed to support organizational decision-making and actions. Using a digital ethnographic research strategy, this study found that data wrangling is part of the “digital-self” in the everyday life of the organizations-actors in the implementation of data analytics as part
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Azeroual, Otmane. "Data Wrangling in Database Systems: Purging of Dirty Data." Data 5, no. 2 (2020): 50. http://dx.doi.org/10.3390/data5020050.

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Researchers need to be able to integrate ever-increasing amounts of data into their institutional databases, regardless of the source, format, or size of the data. It is then necessary to use the increasing diversity of data to derive greater value from data for their organization. The processing of electronic data plays a central role in modern society. Data constitute a fundamental part of operational processes in companies and scientific organizations. In addition, they form the basis for decisions. Bad data quality can negatively affect decisions and have a negative impact on results. The
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Dissertations / Theses on the topic "Data wrangling"

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Contreras, Ochando Lidia. "Towards Data Wrangling Automation through Dynamically-Selected Background Knowledge." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/160724.

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[ES] El proceso de ciencia de datos es esencial para extraer valor de los datos. Sin embargo, la parte más tediosa del proceso, la preparación de los datos, implica una serie de formateos, limpieza e identificación de problemas que principalmente son tareas manuales. La preparación de datos todavía se resiste a la automatización en parte porque el problema depende en gran medida de la información del dominio, que se convierte en un cuello de botella para los sistemas de última generación a medida que aumenta la diversidad de dominios, formatos y estructuras de los datos. En esta tesis nos e
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Books on the topic "Data wrangling"

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Boehmke, Ph.D., Bradley C. Data Wrangling with R. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45599-0.

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Institute, SAS, ed. Mastering the SAS DS2 procedure: Advanced data wrangling techniques / Mark Jordan. SAS Institute, 2016.

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Data Wrangling. University of California, 2018. http://dx.doi.org/10.4135/9781529795646.

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Sheoran, Kavita, Geetika Dhand, Prabhjot Kaur, and M. Niranjanamurthy. Data Wrangling. Wiley & Sons, Limited, John, 2023.

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Data Wrangling. Wiley & Sons, Incorporated, John, 2023.

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Data Wrangling. Wiley & Sons, Limited, John, 2023.

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author, Jarmul Katharine, ed. Data wrangling with Python. O'Reilly Media, 2016.

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Bradley C. Boehmke Ph. D. Data Wrangling with R. Springer, 2016.

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Davis, Ashley. Data Wrangling with JavaScript. Manning Publications, 2018.

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Davis, Ashley. Data Wrangling with JavaScript. Manning Publications Co. LLC, 2018.

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Book chapters on the topic "Data wrangling"

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Wang, Lily. "Data Wrangling." In Data Science for Infectious Disease Data Analytics. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003256328-2.

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Heer, Jeffrey, Joseph M. Hellerstein, and Sean Kandel. "Data Wrangling." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-319-63962-8_9-2.

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Ball, Robert, and Brian Rague. "Data Wrangling." In The Beginner's Guide to Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07865-1_3.

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Whitmore, Nathan. "Data wrangling." In R for Conservation and Development Projects. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429262180-ch09.

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Heer, Jeffrey, Joseph M. Hellerstein, and Sean Kandel. "Data Wrangling." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_9-1.

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Musa, Kamarul Imran, Wan Nor Arifin Wan Mansor, and Tengku Muhammad Hanis. "Data Wrangling." In Data Analysis in Medicine and Health using R. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003296775-5.

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Lin, Hui, and Ming Li. "Data Wrangling." In Practitioner’s Guide to Data Science. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781351132916-6.

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Ismay, Chester, and Albert Y. Kim. "Data Wrangling." In Statistical Inference via Data Science. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780367409913-3.

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Rosett, Christopher M., and Austin Hagerty. "Data Wrangling." In Introducing HR Analytics with Machine Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67626-1_13.

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Teoh, Teik Toe, and Zheng Rong. "Data Wrangling." In Machine Learning: Foundations, Methodologies, and Applications. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8615-3_9.

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Conference papers on the topic "Data wrangling"

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Akella, Ashlesha, Abhijit Manatkar, Krishnasuri Narayanam, and Sameep Mehta. "CodeGenWrangler: Data Wrangling task automation using Code-Generating Models." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-industry.70.

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Koehler, Martin, Alex Bogatu, Cristina Civili, et al. "Data context informed data wrangling." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258015.

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Mazilu, Lacramioara, Norman W. Paton, Nikolaos Konstantinou, and Alvaro A. A. Fernandes. "Fairness in Data Wrangling." In 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2020. http://dx.doi.org/10.1109/iri49571.2020.00056.

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Nikolov, Nikolay, Michele Ciavotta, and Flavio De Paoli. "Data wrangling at scale." In ECSA '18: 12th European Conference on Software Architecture. ACM, 2018. http://dx.doi.org/10.1145/3241403.3241437.

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Yu, Jia, and Mohamed Sarwat. "Spatial Data Wrangling with GeoSpark." In SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2019. http://dx.doi.org/10.1145/3356394.3365589.

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MacAvaney, Sean, Andrew Yates, Sergey Feldman, Doug Downey, Arman Cohan, and Nazli Goharian. "Simplified Data Wrangling with ir_datasets." In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2021. http://dx.doi.org/10.1145/3404835.3463254.

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Chopra, Bhavya, Anna Fariha, Sumit Gulwani, et al. "CoWrangler: Recommender System for Data-Wrangling Scripts." In SIGMOD/PODS '23: International Conference on Management of Data. ACM, 2023. http://dx.doi.org/10.1145/3555041.3589722.

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Ahuja, Shilpi, Mary Roth, Rashmi Gangadharaiah, Peter Schwarz, and Rafael Bastidas. "Using Machine Learning to Accelerate Data Wrangling." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0055.

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Kahn, Jennifer. "Youth Data Wrangling and Modeling Family Migration." In 2019 AERA Annual Meeting. AERA, 2019. http://dx.doi.org/10.3102/1444257.

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Crisan, Anamaria, and Tamara Munzner. "Uncovering Data Landscapes through Data Reconnaissance and Task Wrangling." In 2019 IEEE Visualization Conference (VIS). IEEE, 2019. http://dx.doi.org/10.1109/visual.2019.8933542.

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Reports on the topic "Data wrangling"

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Moral, Rafael. Advancing in R: Statistical Modeling and Data Visualization. Instats Inc., 2024. http://dx.doi.org/10.61700/6kwxq9jjgpsyx1478.

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This self-paced workshop provides a comprehensive exploration of statistical modeling and data visualization using R, tailored for PhD students, professors, and professional researchers. Participants will gain hands-on experience with data wrangling, visualization, and advanced statistical models, equipping them with essential skills for high-quality research and publications.
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Briggs, Cheryl J., and Samantha Sambado. Data Analysis in R to Gain Insights for Conservation: Examples from Long-Term Ecological Research. American Museum of Natural History, 2025. https://doi.org/10.5531/cbc.ncep.0189.

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The R programming language is a powerful tool for analyzing ecological datasets and gaining valuable insights to inform conservation efforts. This module is designed in two parts to help develop foundational skills for working with ecological data in R. The first part introduces you to R and RStudio, providing a solid foundation for data analysis. The second part focuses on key techniques for data wrangling and visualization. Whether you’re new to R or looking to expand your skills from a fresh perspective, this module offers something for those early in their R journey. Throughout, we will us
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Li, Daijiang, Sydne Record, Eric Sokol, et al. Standardized NEON Organismal Data for Biodiversity Research. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48421.

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Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is limited by a lack of standardized, high-quality, empirical data spanning large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the US and will be collected for at least 30 years. The breadth and scope
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