Relevant bibliographies by topics / Data integration

Contents

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

Academic literature on the topic 'Data integration'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Data integration"

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

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Ahmed Salamkar, Muneer. "Data Integration: AI-Driven Approaches to Streamline Data Integration from Various Sources." International Journal of Science and Research (IJSR) 12, no. 3 (2023): 1855–63. https://doi.org/10.21275/sr230311115337.

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Samrat Medavarapu, Sachin. "XML-Based Data Integration." International Journal of Science and Research (IJSR) 13, no. 8 (2024): 1984–86. http://dx.doi.org/10.21275/sr24810074326.

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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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Sherin, Andrew, and Mary Kennedy. "Future pathways for sharing and integrating data: 04 Symposium: Advances in data accessibility and data management for marine species occurrence data: Discussion Panel 2." Biodiversity Information Science and Standards 1 (August 18, 2017): e20406. https://doi.org/10.3897/tdwgproceedings.1.20406.

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Future Pathways for sharing and integrating data is a discussion panel following the second session of the symposium Advances in data accessibility and data management for marine species occurrence data. Panelists will include presenters from the session and invited guest panelists. Questions for discussion will be: Questions for discussion: In your view, how advanced is the marine science research community in data discovery and accessibility? What tools exist to facilitate integration of species occurrence information, associated measurements and environmental data? What tools exist to facil
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T., Aditya Sai Srinivas, Sravanthi Y., Vinod Kumar Y., and Dwaraka Srihith I.V. "Data Standardization: Key to Effective Data Integration." Advanced Innovations in Computer Programming Languages 6, no. 1 (2023): 1–4. https://doi.org/10.5281/zenodo.10060920.

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<i>Data standardization is a critical step in data preprocessing and analysis. This process involves transforming data to have a consistent scale, enabling meaningful comparisons and effective modeling. In this digital age, where data fuels decision-making across industries, understanding and implementing data standardization techniques is essential. This abstract introduces the concept of data standardization, emphasizing its importance in enhancing data quality, supporting data integration efforts, and facilitating data-driven decision-making. We explore various methods and tools for standar
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Fasihuddin, Mirza. "Integrating with Various Data Sources and Formats, Including Structured, Semi-Structured, and Unstructured Data." Journal of Scientific and Engineering Research 8, no. 2 (2021): 263–68. https://doi.org/10.5281/zenodo.11216190.

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The increasing availability and importance of data in various formats have led to the necessity for efficient integration methods to extract meaningful insights. This academic journal explores the challenges and solutions associated with integrating data from multiple sources, including structured, semi-structured, and unstructured data. The study aims to provide an overview of the techniques and tools available to businesses and researchers for effectively integrating diverse data types, enabling better decision-making and improving overall data-driven processes.
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Olmsted, Aspen. "Heterogeneous system integration data integration guarantees." Journal of Computational Methods in Sciences and Engineering 17 (January 19, 2017): S85—S94. http://dx.doi.org/10.3233/jcm-160682.

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Kumar Reddy Thumburu, Sai. "Data Integration Strategies in Hybrid Cloud Environments." International Journal of Science and Research (IJSR) 11, no. 3 (2022): 1642–49. http://dx.doi.org/10.21275/sr22032114643.

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Dissertations / Theses on the topic "Data integration"

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Nadal, Francesch Sergi. "Metadata-driven data integration." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/666947.

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Data has an undoubtable impact on society. Storing and processing large amounts of available data is currently one of the key success factors for an organization. Nonetheless, we are recently witnessing a change represented by huge and heterogeneous amounts of data. Indeed, 90% of the data in the world has been generated in the last two years. Thus, in order to carry on these data exploitation tasks, organizations must first perform data integration combining data from multiple sources to yield a unified view over them. Yet, the integration of massive and heterogeneous amounts of data requires
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Jakonienė, Vaida. "Integration of biological data /." Linköping : Linköpings universitet, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7484.

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Akeel, Fatmah Y. "Secure data integration systems." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/415716/.

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As the web moves increasingly towards publishing data, a significant challenge arises when integrating data from diverse sources that have heterogeneous security and privacy policies and requirements. Data Integration Systems (DIS) are concerned with integrating data from multiple data sources to resolve users' queries. DIS are prone to data leakage threats, e.g. unauthorised disclosure or secondary use of the data, that compromise the data's confidentiality and privacy. We claim that these threats are caused by the failure to implement or correctly employ confidentiality and privacy technique
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Eberius, Julian. "Query-Time Data Integration." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-191560.

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Today, data is collected in ever increasing scale and variety, opening up enormous potential for new insights and data-centric products. However, in many cases the volume and heterogeneity of new data sources precludes up-front integration using traditional ETL processes and data warehouses. In some cases, it is even unclear if and in what context the collected data will be utilized. Therefore, there is a need for agile methods that defer the effort of integration until the usage context is established. This thesis introduces Query-Time Data Integration as an alternative concept to tradition
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Jakonienė, Vaida. "Integration of Biological Data." Doctoral thesis, Linköpings universitet, IISLAB - Laboratoriet för intelligenta informationssystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7484.

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Data integration is an important procedure underlying many research tasks in the life sciences, as often multiple data sources have to be accessed to collect the relevant data. The data sources vary in content, data format, and access methods, which often vastly complicates the data retrieval process. As a result, the task of retrieving data requires a great deal of effort and expertise on the part of the user. To alleviate these difficulties, various information integration systems have been proposed in the area. However, a number of issues remain unsolved and new integration solutions are ne
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Peralta, Veronika. "Data Quality Evaluation in Data Integration Systems." Phd thesis, Université de Versailles-Saint Quentin en Yvelines, 2006. http://tel.archives-ouvertes.fr/tel-00325139.

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Les besoins d'accéder, de façon uniforme, à des sources de données multiples, sont chaque jour plus forts, particulièrement, dans les systèmes décisionnels qui ont besoin d'une analyse compréhensive des données. Avec le développement des Systèmes d'Intégration de Données (SID), la qualité de l'information est devenue une propriété de premier niveau de plus en plus exigée par les utilisateurs. Cette thèse porte sur la qualité des données dans les SID. Nous nous intéressons, plus précisément, aux problèmes de l'évaluation de la qualité des données délivrées aux utilisateurs en réponse à leurs re
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Peralta, Costabel Veronika del Carmen. "Data quality evaluation in data integration systems." Versailles-St Quentin en Yvelines, 2006. http://www.theses.fr/2006VERS0020.

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This thesis deals with data quality evaluation in Data Integration Systems (DIS). Specifically, we address the problems of evaluating the quality of the data conveyed to users in response to their queries and verifying if users’ quality expectations can be achieved. We also analyze how quality measures can be used for improving the DIS and enforcing data quality. Our approach consists in studying one quality factor at a time, analyzing its impact within a DIS, proposing techniques for its evaluation and proposing improvement actions for its enforcement. Among the quality factors that have been
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Neumaier, Sebastian, Axel Polleres, Simon Steyskal, and Jürgen Umbrich. "Data Integration for Open Data on the Web." Springer International Publishing AG, 2017. http://dx.doi.org/10.1007/978-3-319-61033-7_1.

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In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different data formats prevalent on the Web, namely, standard formats for publishing and exchanging tabular, tree-shaped, and graph data. Secondly, not all Open Data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open Data, as
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Cheng, Hui. "Data integration and visualization for systems biology data." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/77250.

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Systems biology aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins and metabolites. Comprehensive visualization tools for exploring multivariate data are needed to gain insight into the physiological processes reflected in these molecular profiles. Data fusion methods are required to integratively study high-throughput transcriptomics, metabolomics and proteomics data combined before systems biology can live up to its potential. In this work I explored mathematical and statistical methods and visualization tools t
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Hackl, Peter, and Michaela Denk. "Data Integration: Techniques and Evaluation." Austrian Statistical Society, 2004. http://epub.wu.ac.at/5631/1/435%2D1317%2D1%2DSM.pdf.

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Within the DIECOFIS framework, ec3, the Division of Business Statistics from the Vienna University of Economics and Business Administration and ISTAT worked together to find methods to create a comprehensive database of enterprise data required for taxation microsimulations via integration of existing disparate enterprise data sources. This paper provides an overview of the broad spectrum of investigated methodology (including exact and statistical matching as well as imputation) and related statistical quality indicators, and emphasises the relevance of data integration, especially fo
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Books on the topic "Data integration"

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Genesereth, Michael. Data Integration. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01550-2.

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Dyché, Jill, and Evan Levy, eds. Customer Data Integration. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119202127.

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Majkić, Zoran. Big Data Integration Theory. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04156-8.

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Doan, AnHai. Principles of data integration. Morgan Kaufmann, 2012.

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Goldfedder, Jarrett. Building a Data Integration Team. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5653-4.

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Davino, Cristina, and Luigi Fabbris, eds. Survey Data Collection and Integration. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-21308-3.

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Viola de Azevedo Cunha, Mario. Market Integration Through Data Protection. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6085-1.

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Kerr, W. Scott. Data integration using virtual repositories. Kerr, 1999.

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Ning, Kang, ed. Methodologies of Multi-Omics Data Integration and Data Mining. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1.

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Office, United States Department of the Interior Bureau of Land Management ALMRS Project. Bureau of Land Management data integration. Bureau of Land Management, ALMRS Project Office, 1985.

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Book chapters on the topic "Data integration"

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Genesereth, Michael. "Basic Concepts." In Data Integration. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01550-2_2.

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Genesereth, Michael. "Introduction." In Data Integration. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01550-2_1.

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Genesereth, Michael. "Query Folding." In Data Integration. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01550-2_3.

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Genesereth, Michael. "Query Planning." In Data Integration. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01550-2_4.

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Genesereth, Michael. "Master Schema Management." In Data Integration. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01550-2_5.

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Curtis, Bobby. "Data Integration." In Pro Oracle GoldenGate for the DBA. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1179-3_9.

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Revesz, Peter. "Data Integration." In Texts in Computer Science. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84996-095-3_17.

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Shekhar, Shashi, and Hui Xiong. "Data Integration." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_244.

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Fait, Aaron, and Alisdair R. Fernie. "Data Integration." In Plant Metabolic Networks. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-78745-9_6.

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Bergamaschi, Sonia, Domenico Beneventano, Francesco Guerra, and Mirko Orsini. "Data Integration." In Handbook of Conceptual Modeling. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15865-0_14.

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Conference papers on the topic "Data integration"

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Mork, P., A. Rosenthal, J. Korb, and K. Samuel. "Integration Workbench: Integrating Schema Integration Tools." In 22nd International Conference on Data Engineering Workshops (ICDEW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdew.2006.69.

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Lenzerini, Maurizio. "Data integration." In the twenty-first ACM SIGMOD-SIGACT-SIGART symposium. ACM Press, 2002. http://dx.doi.org/10.1145/543613.543644.

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Golshan, Behzad, Alon Halevy, George Mihaila, and Wang-Chiew Tan. "Data Integration." In SIGMOD/PODS'17: International Conference on Management of Data. ACM, 2017. http://dx.doi.org/10.1145/3034786.3056124.

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Saito, Toru, and Jinsong Ouyang. "Client-side data visualization." In Integration (IRI). IEEE, 2009. http://dx.doi.org/10.1109/iri.2009.5211550.

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Sheng, Hao, Huajun Chen, Tong Yu, and Yelei Feng. "Linked data based semantic similarity and data mining." In Integration (2010 IRI). IEEE, 2010. http://dx.doi.org/10.1109/iri.2010.5558957.

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Dong, X. L., and D. Srivastava. "Big data integration." In 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544914.

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Ala'i, Riaz. "Borehole data integration." In SEG Technical Program Expanded Abstracts 1998. Society of Exploration Geophysicists, 1998. http://dx.doi.org/10.1190/1.1820493.

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Tan, Wang-Chiew. "Deep Data Integration." In SIGMOD/PODS '21: International Conference on Management of Data. ACM, 2021. http://dx.doi.org/10.1145/3448016.3460534.

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Cudre-Mauroux, Philippe. "Big Data Integration." In 2017 14th International Conference on Telecommunications (ConTEL). IEEE, 2017. http://dx.doi.org/10.23919/contel.2017.8000011.

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Mirza, Ghulam Ali. "Value name conflict while integrating data indatabase integration." In 2014 11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2014. http://dx.doi.org/10.1109/iccwamtip.2014.7073417.

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Reports on the topic "Data integration"

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Obaton, Dominique, and Cécile Liétard. EuroSea data integration. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d3.17.

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This D3.17 “Data Integration” deliverable has been written in complementarity of the deliverables D3.13 “Data handbook” and D3.7 “Networks harmonisation recommendations”. It has been primarily written with the aim to be useful for users, looking for in situ data or datasets, in their choice of data infrastructures (CMEMS - Copernicus Marine Environment Monitoring Service – EMODnet – European Marine Observation and Data network - and SeaDataNet) best suited to their needs. To start, this deliverable provides a description of these three major European data integrators and explains how to access
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Critchlow, T., B. Ludaescher, M. Vouk, and C. Pu. Distributed Data Integration Infrastructure. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15003342.

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Nevarez, Rene. Data Brief—Government Integration. Population Council, 2023. http://dx.doi.org/10.31899/sbsr2023.1007.

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Critchlow, T. J., L. Liu, C. Pu, et al. Scientific Data Management Center Scientific Data Integration. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15003250.

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Bray, O. H. Information integration for data fusion. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/444047.

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Miller, R. Allen. Castability Assessment and Data Integration. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/859291.

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Sturdy, James T. Military Data Link Integration Application. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada465745.

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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), 2000. http://dx.doi.org/10.2172/793555.

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Swinhoe, Martyn Thomas. Model development and data uncertainty integration. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1227409.

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Swinhoe, Martyn Thomas. Model development and data uncertainty integration. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1227933.

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