Relevant bibliographies by topics / Scalable Databases

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

Academic literature on the topic 'Scalable Databases'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Scalable Databases"

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Shekhar Mishra. "Building Scalable Cloud Databases with Database Reliability Engineering." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1322–33. https://doi.org/10.32628/cseit251112125.

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This comprehensive article explores the evolution and implementation of Database Reliability Engineering (DBRE) in cloud environments, focusing on the transformation from traditional database management to modern cloud-based solutions. The article examines key aspects of scalable database architectures, including elastic scalability, serverless solutions, and advanced scaling techniques. The article investigates various strategies for ensuring database reliability, performance optimization, and cost management while addressing challenges in data distribution and consistency maintenance. Throug
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Bhatewara, Ankita, and Kalyani Waghmare. "Highly Scalable Network Management Solution Using Cassandra." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (2014): 5085–89. http://dx.doi.org/10.24297/ijct.v13i10.2330.

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With the current emphasis on Big Data, NOSQL databases have surged in popularity. These databases are claimed to perform better than SQL databases. The traditional database is designed for the structured data and the complex query. In the environment of the cloud, the scale of data is very large, the data is non-structured, the request of the data is dynamic, these characteristics raise new challenges for the data storage and administration, in this context, the NOSQL database comes into picture. This paper discusses about some non-structured databases. It also shows how Cassandra is used to i
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Sethu, Sesha Synam Neeli. "Serverless Databases: A Cost-Effective and Scalable Solution." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 6 (2019): 1–7. https://doi.org/10.5281/zenodo.15362290.

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Traditional database management systems (DBMS) require significant operational overhead, involving complex infrastructure provisioning, configuration, and ongoing maintenance. This approach presents challenges for modern applications demanding scalability, agility, and cost-efficiency. Serverless databases address these limitations by abstracting away the underlying infrastructure management, providing a fully managed service.This paper examines the key advantages of serverless databases, including their auto-scaling capabilities (dynamic resource allocation based on workload demands), reduced
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Bouguettaya, Athman, Zaki Malik, Abdelmounaam Rezgui, and Lori Korff. "A Scalable Middleware for Web Databases." Journal of Database Management 17, no. 4 (2006): 20–46. http://dx.doi.org/10.4018/jdm.2006100102.

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Pang, HweeHwa, Jilian Zhang, and Kyriakos Mouratidis. "Scalable verification for outsourced dynamic databases." Proceedings of the VLDB Endowment 2, no. 1 (2009): 802–13. http://dx.doi.org/10.14778/1687627.1687718.

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Hambardzumyan, Sasun. "Sharding Strategies for Scalable Vector Databases." International Journal of Latest Research in Engineering and Technology (IJLRET) 11, no. 6 (2025): 43–47. https://doi.org/10.56581/ijlret.11.06.43-47.

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Jain, Amit Kumar. "Architecting Scalable and Distributed Cloud Database Systems." International Journal of Technology, Management and Humanities 11, no. 02 (2025): 1–9. https://doi.org/10.21590/ijtmh.11.02.04.

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The rapid growth of data-intensive applications has led to an increasing demand for scalable, distributed cloud-based databases capable of ensuring high availability, fault tolerance, and efficient data management. Scalable architectures in this domain are essential for meeting diverse workload requirements while maintaining optimal performance and costefficiency. This paper explores various architectural designs and techniques employed to achieve scalability in cloud-based databases, including horizontal scaling, sharding, and replication. Emphasis is placed on the balance between consistency
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Qin, Dai, Angela Demke Brown, and Ashvin Goel. "Scalable replay-based replication for fast databases." Proceedings of the VLDB Endowment 10, no. 13 (2017): 2025–36. http://dx.doi.org/10.14778/3151106.3151107.

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Bernecker, Thomas, Hans-Peter Kriegel, Nikos Mamoulis, Matthias Renz, and Andreas Zuefle. "Scalable Probabilistic Similarity Ranking in Uncertain Databases." IEEE Transactions on Knowledge and Data Engineering 22, no. 9 (2010): 1234–46. http://dx.doi.org/10.1109/tkde.2010.78.

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Kumar, Tilak, Surya Surya, and Kushagra Deep. "NoSQL Database Integration with Scalable Machine Learning Pipelines." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40362.

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The increasing requirement to process large amounts of diverse and high-velocity data has made the integration of NoSQL databases with scalable machine learning (ML) pipelines an important research topic. With a focus on scalability, flexibility, and real-time processing, this article examines the architectural and technical obstacles to a smooth integration between NoSQL databases and ML workflows. We look into the suitability of different NoSQL database models (such as document-based, columnar, and graph-based) for handling and storing the variety of data types needed for machine learning tr
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Dissertations / Theses on the topic "Scalable Databases"

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Mathiason, Gunnar. "Virtual Full Replication for Scalable Distributed Real-Time Databases." Doctoral thesis, Linköpings universitet, Institutionen för datavetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-20661.

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A fully replicated distributed real-time database provides high availability and predictable access times, independent of user location, since all the data is available at each node. However, full replication requires that all updates are replicated to every node, resulting in exponential growth of bandwidth and processing demands with the number of nodes and objects added. To eliminate this scalability problem, while retaining the advantages of full replication, this thesis explores Virtual Full Replication (ViFuR); a technique that gives database users a perception of using a fully replicate
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Battle, Leilani Marie. "Interactive visualization of big data leveraging databases for scalable computation." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84906.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 55-57).<br>Modern database management systems (DBMS) have been designed to efficiently store, manage and perform computations on massive amounts of data. In contrast, many existing visualization systems do not scale seamlessly from small data sets to enormous ones. We have designed a three-tiered visualization system called ScalaR to deal with this issue. ScalaR dynamically performs resolution re
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Ramesh, Kartic. "An XML based scalable implementation of Temporal Databases using Parametric Model." [Ames, Iowa : Iowa State University], 2010. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1476339.

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Mehta, Nishant K. "A Hierarchy Navigation Framework: Supporting Scalable Interactive Exploration over Large Databases." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0827104-114148/.

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Zhang, Shiming, and 张世明. "Scalable skyline evaluation in multidimensional and partially ordered domains." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46973916.

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Stefanova, Silvia. "Scalable Preservation, Reconstruction, and Querying of Databases in terms of Semantic Web Representations." Doctoral thesis, Uppsala universitet, Avdelningen för datalogi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-199573.

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This Thesis addresses how Semantic Web representations, in particular RDF, can enable flexible and scalable preservation, recreation, and querying of databases. An approach has been developed for selective scalable long-term archival of relational databases (RDBs) as RDF, implemented in the SAQ (Semantic Archive and Query) system. The archival of user-specified parts of an RDB is specified using an extension of SPARQL, A-SPARQL. SAQ automatically generates an RDF view of the RDB, the RD-view. The result of an archival query is RDF triples stored in: i) a data archive file containing the preser
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Wingerath, Wolfram [Verfasser], and Norbert [Akademischer Betreuer] Ritter. "Scalable Push-Based Real-Time Queries on Top of Pull-Based Databases / Wolfram Wingerath ; Betreuer: Norbert Ritter." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2019. http://d-nb.info/1189319861/34.

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Fink, Robert D. "Evaluation of relational algebra queries on probabilistic databases : tractability and approximation." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:b2188d19-ce55-44ae-bd20-7d50ea6f519b.

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Query processing is a core task in probabilistic databases: Given a query and a database that encodes uncertainty in data by means of probability distributions, the problem is to compute possible query answers together with their respective probabilities of being correct. This thesis advances the state of the art in two aspects of query processing in probabilistic databases: complexity analysis and query evaluation techniques. A dichotomy is established for non-repeating, con- junctive relational algebra queries with negation that separates #P-hard queries from those with PTIME data complexity
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Pang, Gene. "Scalable Transactions for Scalable Distributed Database Systems." Thesis, University of California, Berkeley, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3733329.

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<p> With the advent of the Internet and Internet-connected devices, modern applications can experience very rapid growth of users from all parts of the world. A growing user base leads to greater usage and large data sizes, so scalable database systems capable of handling the great demands are critical for applications. With the emergence of cloud computing, a major movement in the industry, modern applications depend on distributed data stores for their scalable data management solutions. Many large-scale applications utilize NoSQL systems, such as distributed key-value stores, for their scal
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Milliner, Stephen William. "Dynamic resolution of conceptual heterogenity in large scale distributed information systems." Thesis, Queensland University of Technology, 2001.

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Books on the topic "Scalable Databases"

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Peter, Thanisch, Bloor Robin, and Jowitt Tom, eds. Parallel database technology: An evaluation and comparison of scalable sytems. Bloor Research Group, 1995.

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McClure, Wallace B. Building highly scalable database applications with .NET. Wiley Pub., 2002.

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Alberto, Ferrari, Russo Marco, and ebrary Inc, eds. Expert cube development with Microsoft SQL Server 2008 Analysis Services: Design and implement fast, scalable, and maintainable cubes. Packt Publishing, 2009.

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1976-, Laurent Anne, and Lesot Marie-Jeanne 1978-, eds. Scalable fuzzy algorithms for data management and analysis: Methods and design. Information Science Reference, 2009.

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Alston, Darrell W. The instrumentation of a parallel and scalable database computer--the Multi-Backend Database Computer, for benchmarking its complex operations. Naval Postgraduate School, 1989.

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Sebastian, Link, Fober Thomas, Seeger Bernhard, and SpringerLink (Online service), eds. Scalable Uncertainty Management: 6th International Conference, SUM 2012, Marburg, Germany, September 17-19, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Morle, James. Scaling Oracle8i: Building Highly Scalable OLTP System Architectures. Addison-Wesley Professional, 1999.

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Stolte, Etzard. A Scalable Architecture for Large Scientific Databases. 2003.

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MongoDB: Powerful and Scalable Data Storage. O'Reilly Media, Incorporated, 2019.

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Singh, Karan, Anthony D'Atri, and Vaibhav Bhembre. Learning Ceph - Second Edition: Unifed, scalable, and reliable open source storage solution. Packt Publishing - ebooks Account, 2017.

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Book chapters on the topic "Scalable Databases"

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Heinz, Ernst A. "Knowledgeable Endgame Databases." In Scalable Search in Computer Chess. Vieweg+Teubner Verlag, 2000. http://dx.doi.org/10.1007/978-3-322-90178-1_8.

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Heinz, Ernst A. "Index Schemes of Endgame Databases." In Scalable Search in Computer Chess. Vieweg+Teubner Verlag, 2000. http://dx.doi.org/10.1007/978-3-322-90178-1_7.

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Jasny, Matthias, Tobias Ziegler, and Carsten Binnig. "Scalable Data Management on Next-Generation Data Center Networks." In Scalable Data Management for Future Hardware. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74097-8_8.

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Abstract The landscape of distributed Database Management Systems (DBMSs) has been fundamentally transformed by advancements in data center network technologies. Traditionally, these systems were designed to minimize network communication, which was perceived as a significant bottleneck. However, with the advent of high-speed networks and technologies such as Remote Direct Memory Access (RDMA), this perception is changing. RDMA allows direct memory access to remote machines at speeds comparable to local memory access, challenging the long-standing belief that network communication is inherentl
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Kim, Jungkee, and Geoffrey Fox. "Scalable Hybrid Search on Distributed Databases." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428862_59.

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Borthwick, Andrew, Stephen Ash, Bin Pang, Shehzad Qureshi, and Timothy Jones. "Scalable Blocking for Very Large Databases." In ECML PKDD 2020 Workshops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65965-3_20.

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Türkmen, Ali Caner, Yuyang Wang, and Alexander J. Smola. "FastPoint: Scalable Deep Point Processes." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46147-8_28.

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Dam, Tanmoy, Mahardhika Pratama, MD Meftahul Ferdaus, Sreenatha Anavatti, and Hussein Abbas. "Scalable Adversarial Online Continual Learning." In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_23.

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Khalid, Majdi, Indrakshi Ray, and Hamidreza Chitsaz. "Scalable Nonlinear AUC Maximization Methods." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10928-8_18.

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Zezula, Pavel. "Scalable Similarity Search in Computer Networks." In Advances in Databases and Information Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11827252_3.

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Dmitriev, Misha, and Craig Hamilton. "Towards Scalable and Recoverable Object Evolution for the PJama Persistent Platform." In Objects and Databases. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44677-x_4.

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Conference papers on the topic "Scalable Databases"

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Chaudhuri, S., U. Fayyad, and J. Bernhardt. "Scalable classification over SQL databases." In Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337). IEEE, 1999. http://dx.doi.org/10.1109/icde.1999.754963.

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Rastogi, Rajeev, and Kyuseok Shim. "Scalable algorithms for mining large databases." In Tutorial notes of the fifth ACM SIGKDD international conference. ACM Press, 1999. http://dx.doi.org/10.1145/312179.312187.

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Gorton, Ian, John Klein, and Albert Nurgaliev. "Architecture Knowledge for Evaluating Scalable Databases." In 2015 12th Working IEEE/IFIP Conference on Software Architecture (WICSA). IEEE, 2015. http://dx.doi.org/10.1109/wicsa.2015.26.

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Vera, I., A. Dobrzycki, A. M. Chavan, P. Nass, and J. S. Lockhart. "ESO scalable architecture for operational databases." In SPIE Astronomical Telescopes + Instrumentation, edited by Roger J. Brissenden and David R. Silva. SPIE, 2008. http://dx.doi.org/10.1117/12.787587.

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Swaminathan, Surya Narayanan, and Ramez Elmasri. "Quantitative Analysis of Scalable NoSQL Databases." In 2016 IEEE International Congress on Big Data (BigData Congress). IEEE, 2016. http://dx.doi.org/10.1109/bigdatacongress.2016.49.

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Stefanova, Silvia, and Tore Risch. "Scalable reconstruction of RDF-archived relational databases." In the Fifth Workshop. ACM Press, 2013. http://dx.doi.org/10.1145/2484712.2484717.

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Lyu, Bingqing, Lu Qin, Xuemin Lin, Lijun Chang, and Jeffrey Xu Yu. "Scalable supergraph search in large graph databases." In 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498237.

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Seybold, Daniel, Nicolas Wagner, Benjamin Erb, and Jorg Domaschka. "Is elasticity of scalable databases a Myth?" In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840931.

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Sharma, Gitanjali, and Pankaj Deep Kaur. "Architecting Solutions for Scalable Databases in Cloud." In the Third International Symposium. ACM Press, 2015. http://dx.doi.org/10.1145/2791405.2791432.

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Shen, Entong, and Lyublena Antova. "Reversing statistics for scalable test databases generation." In the Sixth International Workshop. ACM Press, 2013. http://dx.doi.org/10.1145/2479440.2479445.

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Reports on the topic "Scalable Databases"

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Gorton, Ian, John Klein, and Albert Nurgaliev. Architecture Knowledge for Evaluating Scalable Databases. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada614251.

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Sharma, Bhavna, Bryan Swanton, Joseph Kuo, et al. Use of Life Cycle Assessment in the Healthcare Industry: Environmental Impacts and Emissions Associated With Products, Processes, and Waste. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepctb48.

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Objectives. The objective of this Technical Brief is to assess the current use of life cycle assessment (LCA) frameworks in healthcare research and practice, understand the components of those frameworks, review LCA studies that have been conducted, and assess gaps in research and practice to guide future directions. Review methods. A scoping review combined with Key Informant interviews provided the input for the report. We searched a combination of biomedical (PubMed®); environmental (Agricultural &amp; Environmental Science Collection, Environmental Science Database, Environment Index); and
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