Готові списки джерел за темами / Data Mining and Knowledge DiscoveryID

Добірка наукової літератури з теми "Data Mining and Knowledge DiscoveryID"

Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Data Mining and Knowledge DiscoveryID"

Bhojani, Shital Hitesh. "Geospatial Data Mining Techniques: Knowledge Discovery in Agricultural." Indian Journal of Applied Research 3, no. 1 (October 1, 2011): 22–24. http://dx.doi.org/10.15373/2249555x/jan2013/10.

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Abdulkadium, Ahmed Mahdi, Raid Abd Alreda Shekan, and Haitham Ali Hussain. "Application of Data Mining and Knowledge Discovery in Medical Databases." Webology 19, no. 1 (January 20, 2022): 4912–24. http://dx.doi.org/10.14704/web/v19i1/web19329.

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While technical improvements in the form of computer-based healthcare information applications as well as hardware are enabling collecting of and access to healthcare data wieldier. In this context, there are tools to analyse and examine this medical data once it has been acquired and saved. Analysis of documented medical data records may help in the identification of hidden features and patterns that could significantly increase our understanding of disease onset and treatment therapies. Significantly, the progress in information and communications technologies (ICT) has outpaced our capacity to assess summarise, and extract insight from the data. Today, database management system has equipped us with the fundamental tools for the effective storage as well as lookup of massive data sets, but the topic of how to allow human beings to interpret and analyse huge data remains a challenging and unsolved challenge. So, sophisticated methods for automated data mining and knowledge discovery are required to deal with large data. In this study, an effort was made employing machine learning approach to acquire knowledge that will aid various personnel in taking decisions that will guarantee that the sustainability objectives on Health is achieved. Finally, the present data mining methodologies with data mining methods and also its deployment tools that are more helpful for healthcare services are addressed in depth.

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KAWANO, Hiroyuki. "Knowledge Discovery and Data Mining." Journal of Japan Society for Fuzzy Theory and Systems 9, no. 6 (1997): 851–60. http://dx.doi.org/10.3156/jfuzzy.9.6_851.

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IWASAKI, Manabu. "Data Mining and Knowledge Discovery." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 26, no. 1 (1999): 46–58. http://dx.doi.org/10.2333/jbhmk.26.46.

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Chikalkar, Siddharth Nandakumar. "Knowledge Discovery and Data Mining." International Journal for Research in Applied Science and Engineering Technology 8, no. 10 (October 31, 2020): 874–76. http://dx.doi.org/10.22214/ijraset.2020.32045.

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Brodley, Carla, Terran Lane, and Timothy Stough. "Knowledge Discovery and Data Mining." American Scientist 87, no. 1 (1999): 54. http://dx.doi.org/10.1511/1999.16.807.

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Lee, Hing-Yan, Hongjun Lu, and Hiroshi Motoda. "Knowledge discovery and data mining." Knowledge-Based Systems 10, no. 7 (May 1998): 401–2. http://dx.doi.org/10.1016/s0950-7051(98)00033-1.

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Clancy, Thomas Roy, and Lillee Gelinas. "Knowledge Discovery and Data Mining." JONA: The Journal of Nursing Administration 46, no. 9 (September 2016): 422–24. http://dx.doi.org/10.1097/nna.0000000000000369.

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Ruzgas, Tomas, Kristina Jakubėlienė, and Aistė Buivytė. "Big Data Mining and Knowledge Discovery." Journal of Communications Technology, Electronics and Computer Science 9 (December 27, 2016): 5. http://dx.doi.org/10.22385/jctecs.v9i0.134.

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Анотація:

The article dealt with exploration methods and tools for big data. It identifies the challenges encountered in the analysis of big data. Defined notion of big data. describe the technology for big data analysis. Article provides an overview of tools which are designed for big data analytics.

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Gupta, Aman. "Data Mining to Discovery of Knowledge." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 309–11. http://dx.doi.org/10.22214/ijraset.2022.43643.

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Дисертації з теми "Data Mining and Knowledge DiscoveryID"

Amado, Vanessa. "Knowledge discovery and data mining from freeway section traffic data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5591.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on June 8, 2009) Vita. Includes bibliographical references.

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Engels, Robert. "Component based user guidance in knowledge discovery and data mining /." Sankt Augustin : Infix, 1999. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=008752552&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Ponsan, Christiane. "Computing with words for data mining." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310744.

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Abedjan, Ziawasch. "Improving RDF data with data mining." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7133/.

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Анотація:

Linked Open Data (LOD) comprises very many and often large public data sets and knowledge bases. Those datasets are mostly presented in the RDF triple structure of subject, predicate, and object, where each triple represents a statement or fact. Unfortunately, the heterogeneity of available open data requires significant integration steps before it can be used in applications. Meta information, such as ontological definitions and exact range definitions of predicates, are desirable and ideally provided by an ontology. However in the context of LOD, ontologies are often incomplete or simply not available. Thus, it is useful to automatically generate meta information, such as ontological dependencies, range definitions, and topical classifications. Association rule mining, which was originally applied for sales analysis on transactional databases, is a promising and novel technique to explore such data. We designed an adaptation of this technique for min-ing Rdf data and introduce the concept of “mining configurations”, which allows us to mine RDF data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. To this end, we present rule-based approaches for auto-completion, data enrichment, ontology improvement, and query relaxation. Auto-completion remedies the problem of inconsistent ontology usage, providing an editing user with a sorted list of commonly used predicates. A combination of different configurations step extends this approach to create completely new facts for a knowledge base. We present two approaches for fact generation, a user-based approach where a user selects the entity to be amended with new facts and a data-driven approach where an algorithm discovers entities that have to be amended with missing facts. As knowledge bases constantly grow and evolve, another approach to improve the usage of RDF data is to improve existing ontologies. Here, we present an association rule based approach to reconcile ontology and data. Interlacing different mining configurations, we infer an algorithm to discover synonymously used predicates. Those predicates can be used to expand query results and to support users during query formulation. We provide a wide range of experiments on real world datasets for each use case. The experiments and evaluations show the added value of association rule mining for the integration and usability of RDF data and confirm the appropriateness of our mining configuration methodology.
Linked Open Data (LOD) umfasst viele und oft sehr große öffentlichen Datensätze und Wissensbanken, die hauptsächlich in der RDF Triplestruktur bestehend aus Subjekt, Prädikat und Objekt vorkommen. Dabei repräsentiert jedes Triple einen Fakt. Unglücklicherweise erfordert die Heterogenität der verfügbaren öffentlichen Daten signifikante Integrationsschritte bevor die Daten in Anwendungen genutzt werden können. Meta-Daten wie ontologische Strukturen und Bereichsdefinitionen von Prädikaten sind zwar wünschenswert und idealerweise durch eine Wissensbank verfügbar. Jedoch sind Wissensbanken im Kontext von LOD oft unvollständig oder einfach nicht verfügbar. Deshalb ist es nützlich automatisch Meta-Informationen, wie ontologische Abhängigkeiten, Bereichs-und Domänendefinitionen und thematische Assoziationen von Ressourcen generieren zu können. Eine neue und vielversprechende Technik um solche Daten zu untersuchen basiert auf das entdecken von Assoziationsregeln, welche ursprünglich für Verkaufsanalysen in transaktionalen Datenbanken angewendet wurde. Wir haben eine Adaptierung dieser Technik auf RDF Daten entworfen und stellen das Konzept der Mining Konfigurationen vor, welches uns befähigt in RDF Daten auf unterschiedlichen Weisen Muster zu erkennen. Verschiedene Konfigurationen erlauben uns Schema- und Wertbeziehungen zu erkennen, die für interessante Anwendungen genutzt werden können. In dem Sinne, stellen wir assoziationsbasierte Verfahren für eine Prädikatvorschlagsverfahren, Datenvervollständigung, Ontologieverbesserung und Anfrageerleichterung vor. Das Vorschlagen von Prädikaten behandelt das Problem der inkonsistenten Verwendung von Ontologien, indem einem Benutzer, der einen neuen Fakt einem Rdf-Datensatz hinzufügen will, eine sortierte Liste von passenden Prädikaten vorgeschlagen wird. Eine Kombinierung von verschiedenen Konfigurationen erweitert dieses Verfahren sodass automatisch komplett neue Fakten für eine Wissensbank generiert werden. Hierbei stellen wir zwei Verfahren vor, einen nutzergesteuertenVerfahren, bei dem ein Nutzer die Entität aussucht die erweitert werden soll und einen datengesteuerten Ansatz, bei dem ein Algorithmus selbst die Entitäten aussucht, die mit fehlenden Fakten erweitert werden. Da Wissensbanken stetig wachsen und sich verändern, ist ein anderer Ansatz um die Verwendung von RDF Daten zu erleichtern die Verbesserung von Ontologien. Hierbei präsentieren wir ein Assoziationsregeln-basiertes Verfahren, der Daten und zugrundeliegende Ontologien zusammenführt. Durch die Verflechtung von unterschiedlichen Konfigurationen leiten wir einen neuen Algorithmus her, der gleichbedeutende Prädikate entdeckt. Diese Prädikate können benutzt werden um Ergebnisse einer Anfrage zu erweitern oder einen Nutzer während einer Anfrage zu unterstützen. Für jeden unserer vorgestellten Anwendungen präsentieren wir eine große Auswahl an Experimenten auf Realweltdatensätzen. Die Experimente und Evaluierungen zeigen den Mehrwert von Assoziationsregeln-Generierung für die Integration und Nutzbarkeit von RDF Daten und bestätigen die Angemessenheit unserer konfigurationsbasierten Methodologie um solche Regeln herzuleiten.

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PaÌirceÌir, RoÌnaÌn. "Knowledge discovery from distributed aggregate data in data warehouses and statistical databases." Thesis, University of Ulster, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274398.

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Sharma, Sumana. "An Integrated Knowledge Discovery and Data Mining Process Model." VCU Scholars Compass, 2008. http://scholarscompass.vcu.edu/etd/1615.

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Анотація:

Enterprise decision making is continuously transforming in the wake of ever increasing amounts of data. Organizations are collecting massive amounts of data in their quest for knowledge nuggets in form of novel, interesting, understandable patterns that underlie these data. The search for knowledge is a multi-step process comprising of various phases including development of domain (business) understanding, data understanding, data preparation, modeling, evaluation and ultimately, the deployment of the discovered knowledge. These phases are represented in form of Knowledge Discovery and Data Mining (KDDM) Process Models that are meant to provide explicit support towards execution of the complex and iterative knowledge discovery process. Review of existing KDDM process models reveals that they have certain limitations (fragmented design, only a checklist-type description of tasks, lack of support towards execution of tasks, especially those of the business understanding phase etc) which are likely to affect the efficiency and effectiveness with which KDDM projects are currently carried out. This dissertation addresses the various identified limitations of existing KDDM process models through an improved model (named the Integrated Knowledge Discovery and Data Mining Process Model) which presents an integrated view of the KDDM process and provides explicit support towards execution of each one of the tasks outlined in the model. We also evaluate the effectiveness and efficiency offered by the IKDDM model against CRISP-DM, a leading KDDM process model, in aiding data mining users to execute various tasks of the KDDM process. Results of statistical tests indicate that the IKDDM model outperforms the CRISP model in terms of efficiency and effectiveness; the IKDDM model also outperforms CRISP in terms of quality of the process model itself.

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DharaniK and Kalpana Gudikandula. "Actionable Knowledge Discovery using Multi-Step Mining." International Journal of Computer Science and Network (IJCSN), 2012. http://hdl.handle.net/10150/271493.

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Data mining at enterprise level operates on huge amount of data such as government transactions, banks, insurance companies and so on. Inevitably, these businesses produce complex data that might be distributed in nature. When mining is made on such data with a single-step, it produces business intelligence as a particular aspect. However, this is not sufficient in enterprise where different aspects and standpoints are to be considered before taking business decisions. It is required that the enterprises perform mining based on multiple features, data sources and methods. This is known as combined mining. The combined mining can produce patterns that reflect all aspects of the enterprise. Thus the derived intelligence can be used to take business decisions that lead to profits. This kind of knowledge is known as actionable knowledge.
Data mining is a process of obtaining trends or patterns in historical data. Such trends form business intelligence that in turn leads to taking well informed decisions. However, data mining with a single technique does not yield actionable knowledge. This is because enterprises have huge databases and heterogeneous in nature. They also have complex data and mining such data needs multi-step mining instead of single step mining. When multiple approaches are involved, they provide business intelligence in all aspects. That kind of information can lead to actionable knowledge. Recently data mining has got tremendous usage in the real world. The drawback of existing approaches is that insufficient business intelligence in case of huge enterprises. This paper presents the combination of existing works and algorithms. We work on multiple data sources, multiple methods and multiple features. The combined patterns thus obtained from complex business data provide actionable knowledge. A prototype application has been built to test the efficiency of the proposed framework which combines multiple data sources, multiple methods and multiple features in mining process. The empirical results revealed that the proposed approach is effective and can be used in the real world.

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Atzmüller, Martin. "Knowledge-intensive subgroup mining : techniques for automatic and interactive discovery /." Berlin : Aka, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2928288&prov=M&dok_var=1&dok_ext=htm.

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Butler, Patrick Julian Carey. "Knowledge Discovery in Intelligence Analysis." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/48422.

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Intelligence analysts today are faced with many challenges, chief among them being the need to fuse disparate streams of data, as well as rapidly arrive at analytical decisions and quantitative predictions for use by policy makers. These problems are further exacerbated by the sheer volume of data that is available to intelligence analysts. Machine learning methods enable the automated transduction of such large datasets from raw feeds to actionable knowledge but successful use of such methods require integrated frameworks for contextualizing them within the work processes of the analyst. Intelligence analysts typically distinguish between three classes of problems: collections, analysis, and operations. This dissertation specifically focuses on two problems in analysis: i) the reconstruction of shredded documents using a visual analytic framework combining computer vision techniques and user input, and ii) the design and implementation of a system for event forecasting which allows an analyst to not just consume forecasts of significant societal events but also understand the rationale behind these alerts and the use of data ablation techniques to determine the strength of conclusions. This work does not attempt to replace the role of the analyst with machine learning but instead outlines several methods to augment the analyst with machine learning. In doing so this dissertation also explores the responsibilities of an analyst in evaluating complex models and decisions made by these models. Finally, this dissertation defines a list of responsibilities for models designed to aid the analyst's work in evaluating and verifying the models.
Ph. D.

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Atzmüller, Martin. "Knowledge-intensive subgroup mining techniques for automatic and interactive discovery." Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2928288&prov=M&dok_var=1&dok_ext=htm.

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Книги з теми "Data Mining and Knowledge DiscoveryID"

Tan, Honghua, ed. Knowledge Discovery and Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27708-5.

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Maimon, Oded, and Mark Last. Knowledge Discovery and Data Mining. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3296-2.

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Tan, Honghua. Knowledge Discovery and Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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1948-, Bramer M. A., ed. Knowledge discovery and data mining. London: The Institution of Electrical Engineers, 1999.

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David, Taniar, ed. Data mining and knowledge discovery technologies. Hershey: IGI Pub., 2008.

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Z, Maimon Oded, and Rokach Lior, eds. Data mining and knowledge discovery handbook. New York: Springer, 2005.

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J, Miller Harvey, and Han Jiawei, eds. Geographic data mining and knowledge discovery. London: Taylor & Francis, 2001.

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Petrushin, Valery A., and Latifur Khan, eds. Multimedia Data Mining and Knowledge Discovery. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-799-2.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. Data Mining Methods for Knowledge Discovery. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6.

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Gaber, Mohamed Medhat, ed. Scientific Data Mining and Knowledge Discovery. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-02788-8.

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Частини книг з теми "Data Mining and Knowledge DiscoveryID"

Kumar, Hanuma, Rohit Paravastu, and Vikram Pudi. "Specialty Mining." In Data Warehousing and Knowledge Discovery, 227–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15105-7_18.

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Fayyad, Usama. "Knowledge Discovery in Databases: An Overview." In Relational Data Mining, 28–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04599-2_2.

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Gaber, Mohamed Medhat, Arkady Zaslavsky, and Shonali Krishnaswamy. "Data Stream Mining." In Data Mining and Knowledge Discovery Handbook, 759–87. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_39.

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Shekhar, Shashi, Pusheng Zhang, and Yan Huang. "Spatial Data Mining." In Data Mining and Knowledge Discovery Handbook, 837–54. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_43.

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Džeroski, Sašo. "Relational Data Mining." In Data Mining and Knowledge Discovery Handbook, 887–911. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_46.

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Moyle, Steve. "Collaborative Data Mining." In Data Mining and Knowledge Discovery Handbook, 1029–39. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_54.

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Nemati, Hamid R., and Christopher D. Barko. "Organizational Data Mining." In Data Mining and Knowledge Discovery Handbook, 1041–48. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_55.

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Zhang, Zhongfei (Mark), and Ruofei Zhang. "Multimedia Data Mining." In Data Mining and Knowledge Discovery Handbook, 1081–109. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_57.

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Piatetsky-Shapiro, Gregory. "The Journey of Knowledge Discovery." In Journeys to Data Mining, 173–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28047-4_13.

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Maimon, Oded, and Mark Last. "Advanced data mining methods." In Knowledge Discovery and Data Mining, 123–33. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3296-2_8.

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Тези доповідей конференцій з теми "Data Mining and Knowledge DiscoveryID"

Caputo, G. M., V. M. Bastos, A. M. Cister, and N. F. F. Ebecken. "Knowledge discovery for CRM improvement." In DATA MINING 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/data080171.

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Milne, R. "Knowledge guided data mining." In IEE Colloquium on Knowledge Discovery in Databases. IEE, 1995. http://dx.doi.org/10.1049/ic:19950117.

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Peyton, L., and J. Hu. "Knowledge discovery in a circle of trust." In DATA MINING & INFORMATION ENGINEERING 2007. Southampton, UK: WIT Press, 2007. http://dx.doi.org/10.2495/data070221.

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Luo, Qi. "Advancing Knowledge Discovery and Data Mining." In First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008). IEEE, 2008. http://dx.doi.org/10.1109/wkdd.2008.153.

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Khabaza, T. "Data mining with Clementine." In IEE Colloquium on Knowledge Discovery in Databases. IEE, 1995. http://dx.doi.org/10.1049/ic:19950121.

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Scarfe, R. T. "Data mining applications in BT." In IEE Colloquium on Knowledge Discovery in Databases. IEE, 1995. http://dx.doi.org/10.1049/ic:19950125.

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Horeis, Timo, and Bernhard Sick. "Collaborative Knowledge Discovery & Data Mining: From Knowledge to Experience." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368905.

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Kecahdi, M.-Tahar, and Ilias K. Savvas. "Cooperative Knowledge Discovery & Data Mining CKDD." In 2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises. IEEE, 2010. http://dx.doi.org/10.1109/wetice.2010.21.

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Gilmore, John F., Michael A. Pagels, and Justin Palk. "Asymmetric threat data mining and knowledge discovery." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Belur V. Dasarathy. SPIE, 2001. http://dx.doi.org/10.1117/12.421076.

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JANOSCOVA, RENATA. "COMPUTER AIDED KNOWLEDGE DISCOVERY AND DATA MINING." In QUALITY AND LEADING INNOVATION´2014. Gaudeamus Hradec Kralove, 2014. http://dx.doi.org/10.12776/qali.v1.10.

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Звіти організацій з теми "Data Mining and Knowledge DiscoveryID"

Phillips, Laurence R., Danyelle N. Jordan, Travis L. Bauer, Mark T. Elmore, Jim N. Treadwell, Rossitza A. Homan, Leon Darrel Chapman, and Shannon V. Spires. Knowledge Discovery and Data Mining (KDDM) survey report. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/922750.

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Pou, Jose, Jeff Duffany, and Alfredo Cruz. Terrorist Activity Evaluation and Pattern Detection (TAE&PD) in Afghanistan: A Knowledge Discovery and Data Mining (KDDM) Approach for Counter-Terrorism. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada581564.

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Swaminathan, Subramanyam. A System for Discovering Bioengineered Threats by Knowledge Base Driven Mining of Toxin Data. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada429799.

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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.), April 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 this dependence, allowing theoretically infinite projections of the data to reveal discoverable relationships that are hidden by traditional use case-driven, static query systems. In this paper, we propose a framework for a data-responsive query algebra based on a dynamic hyperbolic surface model. Such a model could provide more intuitive access to analytics and insights from massive, aggregated datasets than existing methods. This model will significantly alter the means of addressing the underlying data by representing it as an arrangement on a dynamic, hyperbolic plane. Consequently, querying the data can be viewed as a process similar to quantum annealing, in terms of characterizing data representation as an energy minimization problem with numerous minima.

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.

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Nenci, Silvia, and Francesco Quatraro. Innovation and Competitiveness in Mining Value Chains in Latin America. Inter-American Development Bank, December 2021. http://dx.doi.org/10.18235/0003805.

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This paper provides an international overview of the mining global value chain (GVC) and its most recent transformations and trends, focusing on Argentina, Brazil, and Peru. The study uses international trade data and patent and scientific publications data. By using trade in value added, we first investigate the role of those countries in the international mining trade, and their specialization, participation, and position in the mining GVC for the period 2005-15. The analysis is carried out for both mining products and mining-related services, and also looks at the contribution of services to mining exports. Second, we analyze the evolution of innovative activity and the direction of technological change in the mining sector over the past 40 years by looking at patent applications, both internationally and with attention to the three target countries. We also provide an overview of, and some insights on, knowledge flow in the mining sector based on scientific production.

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, February 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 critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

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