Relevant bibliographies by topics / Data mining management

Contents

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

Academic literature on the topic 'Data mining management'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Data mining management"

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Duque, Jorge. "Data Mining for Knowledge Management." Procedia Computer Science 239 (2024): 257–64. http://dx.doi.org/10.1016/j.procs.2024.06.170.

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Ozimek, John. "Data Mining." Journal of Database Marketing & Customer Strategy Management 10, no. 3 (2003): 280–81. http://dx.doi.org/10.1057/palgrave.jdm.3240117.

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Shawe-Taylor, J., T. De Bie, and N. Cristianini. "Data mining, data fusion and information management." IEE Proceedings - Intelligent Transport Systems 153, no. 3 (2006): 221. http://dx.doi.org/10.1049/ip-its:20060006.

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Alkadi, Ihssan. "Data Mining." Review of Business Information Systems (RBIS) 12, no. 1 (2008): 17–24. http://dx.doi.org/10.19030/rbis.v12i1.4394.

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Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Database and information technology have been evolving systematically from primitive file processing systems to sophisticated and powerful databases systems. The research and development in database systems has le
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Lai, Maotao. "Smart Financial Management System Based on Data Ming and Man-Machine Management." Wireless Communications and Mobile Computing 2022 (January 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/2717982.

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To begin, the architecture of an intelligent financial management system is thoroughly investigated, and a new architecture of an intelligent financial management support system based on data mining is developed. Second, it goes over the definition and structure of a data warehouse and data mining, as well as how to use data mining strategy and technology in financial management. Data mining in relation to technology is being investigated, as is the development of an intelligent data mining algorithm. The flaws of the intelligent data mining algorithm are discovered through an analysis and sum
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Zhang, Jingjing, and Yang Chi. "Data Management and Service Mode of Library Based on Data Mining Algorithm." Scientific Programming 2022 (September 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/2414830.

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Data management for large-scale data library services with mining procedures improves the availability and readiness of heterogeneous sources. The heterogeneous data sources are assimilated as a single entity through mining procedures to meet the data demands. This article introduces connectivity-persistent data mining method (CDMM) to improve the data handling precision with boosting availability. The proposed method relies on federated learning for identifying the service demands, thereby providing data mining. The learning paradigm accumulates information on shared data library existence ov
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Bahadir, Cuneyt, and Adem Karahoca. "Airline revenue management via data mining." Global Journal of Information Technology: Emerging Technologies 7, no. 3 (2017): 128–48. http://dx.doi.org/10.18844/gjit.v7i3.2834.

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Revenue maximisation has been of paramount interest in the airline industry during the past few decades, and numerous studies have been reported, aiming at robust analyses. Principal analysis techniques in most of these studies include computational-based prediction algorithms that are used for a given dataset. In this study, airline specific data, which consists of cabin class passenger data, cabin class supplied capacity data, distance of flights, season, year –month data and revenue data, are analysed using various prediction algorithms. Consistencies and accuracies of different algorithms
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Padmasini, C. S., and K. Shyamala. "Data Mining in Automotive Customer Management." International Journal of Data Mining Techniques and Applications 5, no. 1 (2016): 35–38. http://dx.doi.org/10.20894/ijdmta.102.005.001.008.

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Leveridge, Michael. "Mining the data on UTUC management." Canadian Urological Association Journal 6, no. 6 (2012): 463. http://dx.doi.org/10.5489/cuaj.142.

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Schumaker, Robert P., Osama K. Solieman, and Hsinchun Chen. "Sports knowledge management and data mining." Annual Review of Information Science and Technology 44, no. 1 (2010): 115–57. http://dx.doi.org/10.1002/aris.2010.1440440110.

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Dissertations / Theses on the topic "Data mining management"

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Adderly, Darryl M. "Data mining meets e-commerce using data mining to improve customer relationship management /." [Gainesville, Fla.]: University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000500.

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Tong, Suk-man Ivy. "Techniques in data stream mining." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34737376.

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Tong, Suk-man Ivy, and 湯淑敏. "Techniques in data stream mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34737376.

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Zou, Beibei 1974. "Data mining with relational database management systems." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82456.

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With the increasing demands of transforming raw data into information and knowledge, data mining becomes an important field to the discovery of useful information and hidden patterns in huge datasets. Both machine learning and database research have made major contributions to the field of data mining. However, there is still little effort made to improve the scalability of algorithms applied in data raining tasks. Scalability is crucial for data mining algorithms, since they have to handle large datasets quite often. In this thesis we take a step in this direction by extending a popula
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Ma, Xuesong 1975. "Data mining using relational database management system." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98757.

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With the wide availability of huge amounts of data and the imminent demands to transform the raw data into useful information and knowledge, data mining has become an important research field both in the database area and the machine learning areas. Data mining is defined as the process to solve problems by analyzing data already present in the database and discovering knowledge in the data. Database systems provide efficient data storage, fast access structures and a wide variety of indexing methods to speed up data retrieval. Machine learning provides theory support for most of the popular d
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Saad, Elmak. "Optimizing E-management Using Web data mining." Thesis, University of Huddersfield, 2018. http://eprints.hud.ac.uk/id/eprint/34540/.

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Today, one of the biggest challenges that E-management systems face is the explosive growth of operating data and to use this data to enhance services. Web usage mining has emerged as an important technique to provide useful management information from user's Web data. One of the areas where such information is needed is the Web-based academic digital libraries. A digital library (D-library) is an information resource system to store resources in digital format and provide access to users through the network. Academic libraries offer a huge amount of information resources, these information re
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Ahmad, Yasmeen. "Management, visualisation & mining of quantitative proteomics data." Thesis, University of Dundee, 2012. https://discovery.dundee.ac.uk/en/studentTheses/6ed071fc-e43b-410c-898d-50529dc298ce.

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Exponential data growth in life sciences demands cross discipline work that brings together computing and life sciences in a usable manner that can enhance knowledge and understanding in both fields. High throughput approaches, advances in instrumentation and overall complexity of mass spectrometry data have made it impossible for researchers to manually analyse data using existing market tools. By applying a user-centred approach to effectively capture domain knowledge and experience of biologists, this thesis has bridged the gap between computation and biology through software, PepTracker (h
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Zeng, Chunqiu. "Large Scale Data Mining for IT Service Management." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/3051.

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More than ever, businesses heavily rely on IT service delivery to meet their current and frequently changing business requirements. Optimizing the quality of service delivery improves customer satisfaction and continues to be a critical driver for business growth. The routine maintenance procedure plays a key function in IT service management, which typically involves problem detection, determination and resolution for the service infrastructure. Many IT Service Providers adopt partial automation for incident diagnosis and resolution where the operation of the system administrators and automat
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Wang, Qing. "Intelligent Data Mining Techniques for Automatic Service Management." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3883.

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Today, as more and more industries are involved in the artificial intelligence era, all business enterprises constantly explore innovative ways to expand their outreach and fulfill the high requirements from customers, with the purpose of gaining a competitive advantage in the marketplace. However, the success of a business highly relies on its IT service. Value-creating activities of a business cannot be accomplished without solid and continuous delivery of IT services especially in the increasingly intricate and specialized world. Driven by both the growing complexity of IT environments and
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Chen, Hsinchun. "Knowledge Management Systems: A Text Mining Perspective." Knowledge Computing Corporation, 2001. http://hdl.handle.net/10150/106481.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>This bookâ s purpose is to present a balanced and integrated view of what a Knowledge Management System (KMS) is. We first define Knowledge Management (KM) from various consulting and IT perspectives and then pay particular attention to new and emerging technologies that help promote this new field. In particular, we present a review of some key KMS sub-fields: search engines, data mining, and text mining. We hope to help readers better understand the emerging technologies behind knowledge management, i.e., Knowledge M
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Books on the topic "Data mining management"

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Spendler, Lawrence I. Data mining and management. Nova Science Publisher's, 2010.

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Adriaans, Pieter. Data mining. Addison-Wesley, 1996.

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Shi, Yong, Weixuan Xu, and Zhengxin Chen, eds. Data Mining and Knowledge Management. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b104156.

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Džeroski, Sašo. Relational Data Mining. Springer Berlin Heidelberg, 2001.

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Schumaker, Robert P. Sports Data Mining. Springer Science+Business Media, LLC, 2010.

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Pyle, Dorian. Business Modeling and Data Mining. Elsevier Science, 2009.

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Olson, David Louis. Advanced Data Mining Techniques. Springer-Verlag Berlin Heidelberg, 2008.

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ChengXiang, Zhai, and SpringerLink (Online service), eds. Mining Text Data. Springer US, 2012.

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Wang, John. Data mining: Opportunities and challenges. IRM, 2003.

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Olson, David L., and Özgür M. Araz. Data Mining and Analytics in Healthcare Management. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28113-6.

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Book chapters on the topic "Data mining management"

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Olson, David L. "Descriptive Data Mining." In Computational Risk Management. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3340-7_8.

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Rajola, Federico. "Data Mining Techniques." In Customer Relationship Management. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24718-0_6.

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Rajola, Federico. "Data Mining Techniques." In Management for Professionals. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35554-7_8.

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Olson, David L., and Georg Lauhoff. "Descriptive Data Mining." In Computational Risk Management. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7181-3_8.

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Gudevada, Venkat N., and Yongjian Fu. "Multimedia Databases and Data Mining." In Data Management, 3rd ed. Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-77.

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Yeo, David. "Is Data Mining Merely Hype?" In Data Management, 3rd ed. Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-73.

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Morzy, Tadeusz, and Maciej Zakrzewicz. "Data Mining." In Handbook on Data Management in Information Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24742-5_11.

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Menon, Syam, and Ramesh Sharda. "Data Mining." In Encyclopedia of Operations Research and Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_213.

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Weir, Jason. "Data Mining: Exploring the Corporate Asset." In Data Management, 3rd ed. Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-75.

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Olson, David L., and Desheng Wu. "Knowledge Management." In Predictive Data Mining Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_1.

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Conference papers on the topic "Data mining management"

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Kaushik, P., N. Girija, S. Malarvizhi, Arvind Hans, Ankur Kumar, and Jaideep Singh. "Big Data Mining in Financial Risk Management." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895187.

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Tsumoto, Shusaku, and Yuko Tsumoto. "Mining hospital management data." In Defense and Security Symposium, edited by Belur V. Dasarathy. SPIE, 2006. http://dx.doi.org/10.1117/12.667533.

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Ertek, Gurdal, Murat Mustafa Tunc, Allan Nengsheng Zhang, Omer Tanrikulu, and Sobhan Asian. "Data Mining of Project Management Data." In the 2017 International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3176653.3176714.

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Wang, Xiaodan. "Data Mining in Network Engineering'Bayesian Networks for Data Mining." In International Conference on Education, Management, Commerce and Society. Atlantis Press, 2015. http://dx.doi.org/10.2991/emcs-15.2015.84.

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Qiao, Zhiying. "Database management and data mining." In Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), edited by Yuanchang Zhong. SPIE, 2023. http://dx.doi.org/10.1117/12.2667938.

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Vasiliev, Anatoly A., and Alexander V. Goryachev. "Data Mining in Project Management." In 2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2022. http://dx.doi.org/10.1109/elconrus54750.2022.9755501.

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Pereira, G. C., A. R. Figueiredo, and N. F. F. Ebecken. "Mining for ecological thresholds and associations in cytometric data: a coastal management perspective." In DATA MINING 2009. WIT Press, 2009. http://dx.doi.org/10.2495/data090091.

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Michael, P. A., and D. Stott Parker. "Real-time spatio-temporal data mining with the “streamonas” data stream management system." In DATA MINING 2009. WIT Press, 2009. http://dx.doi.org/10.2495/data090121.

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Kechadi, M.-Tahar. "The Data Wave: Data Management and Mining." In 2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises. IEEE, 2010. http://dx.doi.org/10.1109/wetice.2010.56.

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Ektefa, Mohammadreza, Sara Memar, Fatimah Sidi, and Lilly Suriani Affendey. "Intrusion detection using data mining techniques." In Knowledge Management (CAMP). IEEE, 2010. http://dx.doi.org/10.1109/infrkm.2010.5466919.

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Reports on the topic "Data mining management"

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Bond, W., Maria Seale, and Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/43886.

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Data management systems impose structure on data via a static representation schema or data structure. Information from the data is extracted by executing queries based on predefined operators. This paradigm restricts the searchability of the data to concepts and relationships that are known or assumed to exist among the objects. While this is an effective and efficient means of retrieving simple information, we propose that such a structure severely limits the ability to derive breakthrough knowledge that exists in data under the guise of “unknown unknowns.” A dynamic system will alleviate th
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Price, Roz. Taxation and Public Financial Management of Mining Revenue in the Democratic Republic of Congo. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/k4d.2021.144.

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This rapid review provides a summary of the evidence on the taxation and public financial management of mining revenues in the Democratic Republic of Congo (DRC). This is a very complex topic, with a large and growing literature base, a huge interest by donors, non-governmental organisations and businesses, with some conflicting information at times. In particular, specific data on provincial budgets and spending was not identified during this review. No specific information on public financial management in either of these provinces was identified during the course of this review. Given the b
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Vizmanos, Jana Flor, Jose Ramon Albert, Mika Muñoz, et al. Addressing Data Gaps with Innovative Data Sources. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.55.

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With the advent of digital transformation, information and communications technology innovations have also led to a "data revolution" wherein more data is being captured, produced, stored, accessed, analyzed, archived, and reanalyzed at an exponential pace. An examination of new data sources, including big data and crowd-sourced data, can complement traditional sources of statistics and unlock insights that can ultimately lead to interventions for better outcomes by informing policies and actions toward attaining robust, sustainable, and inclusive development. This study will examine PIDS webs
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Desa, Hazry, and Muhammad Azizi Azizan. OPTIMIZING STOCKPILE MANAGEMENT THROUGH DRONE MAPPING FOR VOLUMETRIC CALCULATION. Penerbit Universiti Malaysia Perlis, 2023. http://dx.doi.org/10.58915/techrpt2023.004.

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Stockpile volumetric calculation is an important aspect in many industries, including construction, mining, and agriculture. Accurate calculation of stockpile volumes is essential for efficient inventory management, logistics planning, and quality control. Traditionally, stockpile volumetric calculation is done using ground-based survey methods, which can be time-consuming, labour-intensive, and often inaccurate. However, with the recent advancements in drone technology, it has become possible to use drones for stockpile volumetric calculation, providing a faster, safer, and more accurate solu
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Robert, Gillian. PR-420-153722-R01 Pipeline Right-of-Way Ground Movement Monitoring from InSAR. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0011463.

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Longwall mining induces large surface motion that may impact active pipelines. Typical remediation for longwall mining involves shutting down and exposing the pipeline. The use of InSAR has the potential to provide accurate measurements confirming the expected ground movement that will occur with the mining operations. Used correctly, with an appropriate survey design, InSAR can provide extremely high densities of ground movement over time. Exploiting the wide-area capabilities of InSAR could become an important part of integrity management for pipelines where longwall mining is a consideratio
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Kindt, Roeland, Ian K Dawson, Jens-Peter B Lillesø, Alice Muchugi, Fabio Pedercini, and James M Roshetko. The one hundred tree species prioritized for planting in the tropics and subtropics as indicated by database mining. World Agroforestry, 2021. http://dx.doi.org/10.5716/wp21001.pdf.

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A systematic approach to tree planting and management globally is hindered by the limited synthesis of information sources on tree uses and species priorities. To help address this, the authors ‘mined’ information from 23 online global and regional databases to assemble a list of the most frequent tree species deemed useful for planting according to database mentions, with a focus on tropical regions. Using a simple vote count approach for ranking species, we obtained a shortlist of 100 trees mentioned in at least 10 of our data sources (the ‘top-100’ species). A longer list of 830 trees that
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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other c
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Kong, Zhihao, and Na Lu. Determining Optimal Traffic Opening Time Through Concrete Strength Monitoring: Wireless Sensing. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317613.

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Construction and concrete production are time-sensitive and fast-paced; as such, it is crucial to monitor the in-place strength development of concrete structures in real-time. Existing concrete strength testing methods, such as the traditional hydraulic compression method specified by ASTM C 39 and the maturity method specified by ASTM C 1074, are labor-intensive, time consuming, and difficult to implement in the field. INDOT’s previous research (SPR-4210) on the electromechanical impedance (EMI) technique has established its feasibility for monitoring in-situ concrete strength to determine t
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Waigner, Lisa, Solange Filoso, Kim Gazenski, Elizabeth Murray, Charles Theiling, and Shawn Komlos. Applying the ecosystem goods and services (EGS) framework : Meramec case study. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49520.

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This technical report explores ecosystem goods and services (EGS) assessment to support US Army Corps of Engineers (USACE) decision-making by applying the recently published proposed EGS framework (Wainger et al. 2020) to a case study. A joint effort of the Environmental Protection Agency (EPA) and USACE, the Meramec River Basin Ecosystem Restoration Feasibility Study provides an opportunity to investigate the practicality of EGS analysis and how it might determine complementarity or antagonism among study partner goals. The EPA seeks primarily to protect hu-man health, while USACE aims to res
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Bowles, David, Michael Williams, Hope Dodd, et al. Protocol for monitoring aquatic invertebrates of small streams in the Heartland Inventory & Monitoring Network: Version 2.1. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2284622.

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The Heartland Inventory and Monitoring Network (HTLN) is a component of the National Park Service’s (NPS) strategy to improve park management through greater reliance on scientific information. The purposes of this program are to design and implement long-term ecological monitoring and provide information for park managers to evaluate the integrity of park ecosystems and better understand ecosystem processes. Concerns over declining surface water quality have led to the development of various monitoring approaches to assess stream water quality. Freshwater streams in network parks are threaten
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