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Добірка наукової літератури з теми "Data"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 5 жовтня 2024

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Статті в журналах з теми "Data"

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 (October 1, 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 (February 10, 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 (December 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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Анотація:

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 information about the markets in which they operate, which allows them to get a large amount of profit and benefits.

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

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Gültepe, Yasemin. "Querying Bibliography Data Based on Linked Data." Journal of Software 10, no. 8 (August 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 (October 20, 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 (April 5, 2024): 232–36. http://dx.doi.org/10.21275/es24318121241.

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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 (August 5, 2023): 2519–25. http://dx.doi.org/10.21275/es23812104904.

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Дисертації з теми "Data"

Riminucci, Stefania. "COVID-19,Open data e data visualization:interazione con dati epidemiologici." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21577/.

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Анотація:

L’obiettivo di questa tesi è quello di analizzare l’efficacia di diverse strategie di data visualization utilizzate per presentare open data in forma grafica alla popolazione. Come caso di studio, si è presa in considerazione la pandemia di COVID-19, e le molteplici visualizzazioni che sfruttano gli open data messi a disposizione dalle comunità scientifiche, offrendo informazioni sull'evoluzione dei contagi a livello nazionale e internazionale. Per valutare l’efficacia delle diverse visualizzazioni, è stato sviluppato e proposto al pubblico un questionario per la raccolta di dati per avere una percezione di quali siano i livelli di comprensione ed utilizzo di varie tipologie di grafici e dashboard messi a disposizione dalle diverse piattaforme online. Il questionario prevedeva sia proposte di grafici da valutare che azioni richieste agli utenti per la ricerca di informazioni su piattaforme esterne. 99 utenti hanno risposto al questionario. Analizzando i dati raccolti è emerso che i risultati relativi ai grafici proposti non hanno mostrato una netta predominanza di alcuna delle proposte presentate, fornendo solamente qualche indicazione relativamente alla preferenza di istogrammi e cartogrammi rispetto ad altre tipologie di grafici. Allo stesso modo, l’analisi sui dati relativi alla facilità di reperire le informazioni sulle diverse piattaforme esterne non ha restituito risultati rilevanti, enfatizzando l’impatto della componente soggettiva e del background della singola persona.

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Mondaini, Luca. "Data Visualization di dati spazio-temporali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16853/.

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Анотація:

Questa tesi propone, dopo aver introdotto le tipologie di dato esistenti e i concetti di Big Data, Open Data e Data Visualization, due obiettivi differenti: ottenere informazioni utilizzando le API di Google Maps, più precisamente, latitudine e longitudine di ogni indirizzo di domicilio degli studenti immatricolati in UniBo e la visualizzazione di questi dati, all'interno di un'applicazione web, mediante istogrammi e mappe digitali interattivi con l'utilizzo di tecniche di Data Visualization.

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Yu, Wenyuan. "Improving data quality : data consistency, deduplication, currency and accuracy." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8899.

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Data quality is one of the key problems in data management. An unprecedented amount of data has been accumulated and has become a valuable asset of an organization. The value of the data relies greatly on its quality. However, data is often dirty in real life. It may be inconsistent, duplicated, stale, inaccurate or incomplete, which can reduce its usability and increase the cost of businesses. Consequently the need for improving data quality arises, which comprises of five central issues of improving data quality, namely, data consistency, data deduplication, data currency, data accuracy and information completeness. This thesis presents the results of our work on the first four issues with regards to data consistency, deduplication, currency and accuracy. The first part of the thesis investigates incremental verifications of data consistencies in distributed data. Given a distributed database D, a set S of conditional functional dependencies (CFDs), the set V of violations of the CFDs in D, and updates ΔD to D, it is to find, with minimum data shipment, changes ΔV to V in response to ΔD. Although the problems are intractable, we show that they are bounded: there exist algorithms to detect errors such that their computational cost and data shipment are both linear in the size of ΔD and ΔV, independent of the size of the database D. Such incremental algorithms are provided for both vertically and horizontally partitioned data, and we show that the algorithms are optimal. The second part of the thesis studies the interaction between record matching and data repairing. Record matching, the main technique underlying data deduplication, aims to identify tuples that refer to the same real-world object, and repairing is to make a database consistent by fixing errors in the data using constraints. These are treated as separate processes in most data cleaning systems, based on heuristic solutions. However, our studies show that repairing can effectively help us identify matches, and vice versa. To capture the interaction, a uniform framework that seamlessly unifies repairing and matching operations is proposed to clean a database based on integrity constraints, matching rules and master data. The third part of the thesis presents our study of finding certain fixes that are absolutely correct for data repairing. Data repairing methods based on integrity constraints are normally heuristic, and they may not find certain fixes. Worse still, they may even introduce new errors when attempting to repair the data, which may not work well when repairing critical data such as medical records, in which a seemingly minor error often has disastrous consequences. We propose a framework and an algorithm to find certain fixes, based on master data, a class of editing rules and user interactions. A prototype system is also developed. The fourth part of the thesis introduces inferring data currency and consistency for conflict resolution, where data currency aims to identify the current values of entities, and conflict resolution is to combine tuples that pertain to the same real-world entity into a single tuple and resolve conflicts, which is also an important issue for data deduplication. We show that data currency and consistency help each other in resolving conflicts. We study a number of associated fundamental problems, and develop an approach for conflict resolution by inferring data currency and consistency. The last part of the thesis reports our study of data accuracy on the longstanding relative accuracy problem which is to determine, given tuples t1 and t2 that refer to the same entity e, whether t1[A] is more accurate than t2[A], i.e., t1[A] is closer to the true value of the A attribute of e than t2[A]. We introduce a class of accuracy rules and an inference system with a chase procedure to deduce relative accuracy, and the related fundamental problems are studied. We also propose a framework and algorithms for inferring accurate values with users’ interaction.

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Long, Christopher C. "Data Processing for NASA's TDRSS DAMA Channel." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611474.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California
Presently, NASA's Space Network (SN) does not have the ability to receive random messages from satellites using the system. Scheduling of the service must be done by the owner of the spacecraft through Goddard Space Flight Center (GSFC). The goal of NASA is to improve the current system so that random messages, that are generated on board the satellite, can be received by the SN. The messages will be requests for service that the satellites control system deems necessary. These messages will then be sent to the owner of the spacecraft where appropriate action and scheduling can take place. This new service is known as the Demand Assignment Multiple Access system (DAMA).

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Budd, Chris. "Data Protection and Data Elimination." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596395.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV
Data security is becoming increasingly important in all areas of storage. The news services frequently have stories about lost or stolen storage devices and the panic it causes. Data security in an SSD usually involves two components: data protection and data elimination. Data protection includes passwords to protect against unauthorized access and encryption to protect against recovering data from the flash chips. Data elimination includes erasing the encryption key and erasing the flash. Telemetry applications frequently add requirements such as write protection, external erase triggers, and overwriting the flash after the erase. This presentation will review these data security features.

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Furrier, Sean Alexander, and Sean Alexander Furrier. "Communicating Data: Data-Driven Storytelling." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/624989.

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Анотація:

Data is more abundant than ever, yet its utility is diminished by a lack of understanding and difficulty in communicating insights. This thesis seeks to test the effectiveness of data-driven storytelling as a means to solve this disconnect. Research conducted includes reading previous literature on the subject, interviewing journalists and data practitioners as well as learning to use various software tools. This research focuses on communicating engaging stories by finding, cleaning, analyzing and visualizing data using R, Python, Excel, Tableau, Carto and other software tools. The result is a series of data-driven stories published in the Daily Wildcat on a variety of subjects including campus life, politics, and sports. The conclusion of the thesis finds that data-driven storytelling is an effective medium for communicating data and capitalizing on its potential utility. This conclusion is drawn from the fact that humans intuitively understand narrative and data insights parsed out in this familiar form are more easily understood than data presented in an abstract manner.

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Chitondo, Pepukayi David Junior. "Data policies for big health data and personal health data." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2479.

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Анотація:

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016.
Health information policies are constantly becoming a key feature in directing information usage in healthcare. After the passing of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 and the Affordable Care Act (ACA) passed in 2010, in the United States, there has been an increase in health systems innovations. Coupling this health systems hype is the current buzz concept in Information Technology, „Big data‟. The prospects of big data are full of potential, even more so in the healthcare field where the accuracy of data is life critical. How big health data can be used to achieve improved health is now the goal of the current health informatics practitioner. Even more exciting is the amount of health data being generated by patients via personal handheld devices and other forms of technology that exclude the healthcare practitioner. This patient-generated data is also known as Personal Health Records, PHR. To achieve meaningful use of PHRs and healthcare data in general through big data, a couple of hurdles have to be overcome. First and foremost is the issue of privacy and confidentiality of the patients whose data is in concern. Secondly is the perceived trustworthiness of PHRs by healthcare practitioners. Other issues to take into context are data rights and ownership, data suppression, IP protection, data anonymisation and reidentification, information flow and regulations as well as consent biases. This study sought to understand the role of data policies in the process of data utilisation in the healthcare sector with added interest on PHRs utilisation as part of big health data.

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BRASCHI, GIACOMO. "La circolazione dei dati e l'analisi big data." Doctoral thesis, Università degli studi di Pavia, 2019. http://hdl.handle.net/11571/1244327.

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Анотація:

Descrizione degli strumenti giuridici che regolano la circolazione dei dati e analisi dei possibili sviluppi normativi auspicabile per favori la circolazione dei dati
Description of the legal instruments that regulate the circulation of data and analysis of possible legislative developments desirable to favor the circulation of data

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Perovich, Laura J. (Laura Jones). "Data Experiences : novel interfaces for data engagement using environmental health data." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95612.

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Анотація:

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 71-81).
For the past twenty years, the data visualization movement has reworked the way we engage with information. It has brought fresh excitement to researchers and reached broad audiences. But what comes next for data? I seek to create example "Data Experiences" that will contribute to developing new spaces of information engagement. Using data from Silent Spring Institute's environmental health studies as a test case, I explore Data Experiences that are immersive, interactive, and aesthetic. Environmental health datasets are ideal for this application as they are highly relevant to the general population and have appropriate complexity. Dressed in Data will focus on the experience of an individual with her/his own environmental health data while BigBarChart focuses on the experience of the community with the overall dataset. Both projects seek to present opportunities for nontraditional learning, community relevance, and social impact.
by Laura J. Perovich.
S.M.

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Wang, Yi. "Data Management and Data Processing Support on Array-Based Scientific Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436157356.

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Книги з теми "Data"

Cooper, Richard, and Jessie Kennedy, eds. Data Management. Data, Data Everywhere. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73390-4.

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Jakarta Raya (Indonesia). Badan Kesatuan Bangsa., ed. Kumpulan data: Data kerawanan, data narkoba, data tawuran pelajar. [Jakarta]: Badan Kesatuan Bangsa, Prop. DKI Jakarta, 2002.

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Monino, Jean-Louis, and Soraya Sedkaoui. Big Data, Open Data and Data Development. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119285199.

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Zhu, Zhen. Data Warehousing, Data Lakes and Data Lakehouses. Edited by Aeron Zentner. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2024. http://dx.doi.org/10.4135/9781071937471.

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Panchal, Ajay G. Data mining economic data. Manchester: UMIST, 1998.

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Autodata, ed. Diesel data: Technical data. Maidenhead: Autodata Limited, 1992.

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Press, Scripps College, and ArjoWiggins (Firm), eds. Good data, bad data. [Claremont, California]: Scripps College Press, 2014.

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Andrews, D. F., and A. M. Herzberg. Data. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5098-2.

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Herian, Robert. Data. Milton Park, Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9781003162001.

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Tyagi, Amit Kumar. Data Science and Data Analytics. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003111290.

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Частини книг з теми "Data"

Pastore y Piontti, Ana, Nicola Perra, Luca Rossi, Nicole Samay, and Alessandro Vespignani. "DATA, DATA, AND MORE DATA." In Charting the Next Pandemic, 11–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93290-3_2.

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Domokos, László. "Data About Data." In Physical Property Prediction in Organic Chemistry, 11–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1_3.

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Furner, Jonathan. "“Data”: The data." In Information Cultures in the Digital Age, 287–306. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14681-8_17.

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Luckin, Rose, Karine George, and Mutlu Cukurova. "Data, data everywhere." In AI for School Teachers, 33–48. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003193173-3.

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Bahman, Zohuri, and Mossavar-Rahmani Farhang. "Data Warehousing, Data Mining, Data Modeling, and Data Analytics." In A Model to Forecast Future Paradigms, 73–109. Includes bibliographical references and index. | Contents: Volume 1. Introduction to knowledge is power in four dimensions: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9781003000662-3.

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Busulwa, Richard, and Nina Evans. "Data, data management, data analytics, and data science technologies." In Digital Transformation in Accounting, 183–96. Abingdon, Oxon ; New York, NY : Routledge, 2021. | Series: Business & digital transformation: Routledge, 2021. http://dx.doi.org/10.4324/9780429344589-18.

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Irti, Claudia. "Personal Data, Non-personal Data, Anonymised Data, Pseudonymised Data, De-identified Data." In Services and Business Process Reengineering, 49–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3049-1_5.

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Herian, Robert. "Being in data." In Data, 67–88. Milton Park, Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9781003162001-4.

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Herian, Robert. "Proximate data – a conclusion." In Data, 111–25. Milton Park, Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9781003162001-6.

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Herian, Robert. "Introduction." In Data, 1–14. Milton Park, Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9781003162001-1.

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Тези доповідей конференцій з теми "Data"

Zhang, Xiaofeng, Zhangyang Wang, Dong Liu, and Qing Ling. "DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683197.

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Yi, Zhang, Zhao Hongkai, Wei Yongyu, Xia Yulian, and Zhang Xiaoyan. "Improved DataX Data Synchronization Technique for Distribution Grid Data Middleware Implementation." In 2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2024. http://dx.doi.org/10.1109/eebda60612.2024.10485928.

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Alfred, Rayner. "DARA: Data Summarisation with Feature Construction." In 2008 Second Asia International Conference on Modelling & Simulation (AMS). IEEE, 2008. http://dx.doi.org/10.1109/ams.2008.131.

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Hunger, Casen, Lluis Vilanova, Charalampos Papamanthou, Yoav Etsion, and Mohit Tiwari. "DATS - Data Containers for Web Applications." In ASPLOS '18: Architectural Support for Programming Languages and Operating Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3173162.3173213.

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Koehler, Martin, Alex Bogatu, Cristina Civili, Nikolaos Konstantinou, Edward Abel, Alvaro A. A. Fernandes, John Keane, Leonid Libkin, and Norman W. Paton. "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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Leadbetter, Adam, Damian Smyth, Robert Fuller, Eoin O'Grady, and Adam Shepherd. "Where big data meets linked data: Applying standard data models to environmental data streams." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840943.

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Ashok, Vikas, and Ravi Mukkamala. "Data mining without data." In the 10th annual ACM workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2046556.2046578.

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Cassavia, Nunziato, Pietro Dicosta, Elio Masciari, and Domenico Saccà. "Data Preparation for Tourist Data Big Data Warehousing." In Special Session on Knowledge Discovery meets Information Systems: Experiences and Lessons Learned Dealing with Real-life Scenarios. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005144004190426.

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Sanchez, Susan M. "DATA FARMING: BETTER DATA, NOT JUST BIG DATA." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632383.

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Lokesh, M., A. Keerthi Devi, U. Dinesh Chowdary, P. V. N. S. Divya Lakshmi, and G. Rama Koteswara Rao. "Data Redundancy, Data Phishing, and Data Cloud Backup." In 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2023. http://dx.doi.org/10.1109/icecct56650.2023.10179679.

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Martin, Mark, Lance Vowell, Ian King, and Chris Augustus. Automated Data Cleansing in Data Harvesting and Data Migration. Office of Scientific and Technical Information (OSTI), March 2011. http://dx.doi.org/10.2172/949761.

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P.L. Cloke. Data Qualification Report For: Thermodynamic Data File, DATA0.YMP.R0 For Geochemical Code, EQ3/6? Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/899946.

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Russell, H. A. J., N. Benoit, and D. Paradis. Data collection and data sources. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/298871.

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Musick, R., T. Critchlow, M. Ganesh, Z. Fidelis, A. Zemla, and T. Slezak. Data Foundry: Data Warehousing and Integration for Scientific Data Management. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/793555.

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Zhang, Jovan Yang, Hari Viswanathan, Jeffery Hyman, and Richard Middleton. Data Analytics of Hydraulic Fracturing Data. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1304742.

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Bishop, Bradley Wade. Data from Data Services Librarians Study. University of Tennessee, Knoxville Libraries, April 2020. http://dx.doi.org/10.7290/m29yhy5qen.

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Bishop, Bradley Wade. Data from Data Management Plan Compliance. University of Tennessee, Knoxville Libraries, January 2020. http://dx.doi.org/10.7290/pebuwhcq7l.

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Dosch, Brianne, and Tyler Martindate. Data from Business Journals Data Sharing. University of Tennessee, Knoxville Libraries, 2019. http://dx.doi.org/10.7290/pyxdnl2g0z.

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Tafolla, Tanya, Eappen Nelluvelil, Jacob Moore, Daniel Dunning, Nathaniel Morgan, and Robert Robey. MATAR: Data-Oriented Sparse Data Representation. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1773304.

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Hoitink, D. J., and K. W. Burk. Climatological data summary 1994, with historical data. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/90676.

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