Relevant bibliographies by topics / Data Mining Techniques

Contents

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

Academic literature on the topic 'Data Mining Techniques'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "Data Mining Techniques"

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Shah Neha K, Shah Neha K. "Introduction of Data mining and an Analysis of Data mining Techniques." Indian Journal of Applied Research 3, no. 5 (2011): 137–39. http://dx.doi.org/10.15373/2249555x/may2013/41.

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Sherdiwala, Kainaz Bomi. "Data Mining Techniques in Stock Market." Indian Journal of Applied Research 4, no. 8 (2011): 327–29. http://dx.doi.org/10.15373/2249555x/august2014/82.

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S, Gowtham, and Karuppusamy S. "Review of Data Mining Classification Techniques." Bonfring International Journal of Software Engineering and Soft Computing 9, no. 2 (2019): 8–11. http://dx.doi.org/10.9756/bijsesc.9013.

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M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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SAKURAI, Shigeaki. "Data Mining Techniques." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 21, no. 3 (2009): 348–57. http://dx.doi.org/10.3156/jsoft.21.3_348.

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Hegland, Markus. "Data mining techniques." Acta Numerica 10 (May 2001): 313–55. http://dx.doi.org/10.1017/s0962492901000058.

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Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Common data mining tasks include the induction of association rules, the discovery of functional relationships (classification and regression) and the exploration of groups of similar data objects in clustering. This review provides a discussion of and pointers to efficient algorithms for the common data mining tasks in a mathematical framework. Because of the size and complexity of the data sets, efficient algorithms and often crude approximations play an important role.
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Han, Jiawei. "Data mining techniques." ACM SIGMOD Record 25, no. 2 (1996): 545. http://dx.doi.org/10.1145/235968.280351.

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D, Premalatha, Niveditha N, Poornima Vasanth V, Priyanka G. N, and Niveditha K B. "CANCER PREDICTION SYSTEM USING DATA MINING TECHNIQUES." International Journal of Current Engineering and Scientific Research 6, no. 6 (2019): 165–68. http://dx.doi.org/10.21276/ijcesr.2019.6.6.28.

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Delima, Allemar Jhone P. "Predicting Scholarship Grants Using Data Mining Techniques." International Journal of Machine Learning and Computing 9, no. 4 (2019): 513–19. http://dx.doi.org/10.18178/ijmlc.2019.9.4.834.

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Lakshmi R, Aishwarya, and SashiKumar D R. "A Survey of Data Mining Techniques for Internet." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 1 (2017): 22–35. http://dx.doi.org/10.23956/ijarcsse/v7i1/0125.

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Dissertations / Theses on the topic "Data Mining Techniques"

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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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Burgess, Martin. "Transformation techniques in data mining." Thesis, University of East Anglia, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410093.

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Transforming data is essential within data mining as a precursor to many applications such as rule induction and Multivariate Adaptive Regression Splines. The problems arising from the use of categorical valued data in rule induction are reduced confidence (accuracy), support and coverage. We introduce a technique called arcsin transformation where categorical valued data is replaced with numeric values. This technique has been used on a number of databases and has shown to be highly effective. Multivariate Adaptive Regression Splines, MARS, is a regression tool which attempts to approximate complex relationships by a series of linear regressions on different intervals of the explanatory variable ranges. Like regression methods in general, we need to know what assumptions are made and how the violation of these may disrupt performance. The two key assumptions with most regression models including MARS are additivity of effects and homoscedasticity. If any of these assumptions are not satisfied in terms of the original observations, y;, a non-linear transformation may improve matters. We use the Box-Cox transformation in which the continuous dependent variable (with non-negative responses) in a linear regression setting, might induce the regression assumptions given previously. The assumptions stated are discussed in detail using a variety of tests. The results show that on seven databases examined, an improvement has been made on six, where the models produced were
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Al-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.

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Thesis (M.S.C.S.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>The rapid development of information technology in recent decades means that data appear in a wide variety of formats — sensor data, tweets, photographs, raw data, and unstructured data. Statistics show that there were 800,000 Petabytes stored in the world in 2000. Today’s internet has about 0.1 Zettabytes of data (ZB is about 1021 bytes), and this number will reach 35 ZB by 2020. With such an overwhelming flood of information, present data management systems are not able to scale to this huge amount of raw, unstructured data—in today’s parlance, Big Data. In the present study, we show the basic concepts and design of Big Data tools, algorithms, and techniques. We compare the classical data mining algorithms to the Big Data algorithms by using Hadoop/MapReduce as a core implementation of Big Data for scalable algorithms. We implemented the K-means algorithm and A-priori algorithm with Hadoop/MapReduce on a 5 nodes Hadoop cluster. We explore NoSQL databases for semi-structured, massively large-scaling of data by using MongoDB as an example. Finally, we show the performance between HDFS (Hadoop Distributed File System) and MongoDB data storage for these two algorithms.
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Zhou, Wubai. "Data Mining Techniques to Understand Textual Data." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3493.

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More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents, news, logs, search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Text understanding is a fundamental and essential task involving broad research topics, and contributes to many applications in the areas text summarization, search engine, recommendation systems, online advertising, conversational bot and so on. However, understanding text for computers is never a trivial task, especially for noisy and ambiguous text such as logs, search queries. This dissertation mainly focuses on textual understanding tasks derived from the two domains, i.e., disaster management and IT service management that mainly utilizing textual data as an information carrier. Improving situation awareness in disaster management and alleviating human efforts involved in IT service management dictates more intelligent and efficient solutions to understand the textual data acting as the main information carrier in the two domains. From the perspective of data mining, four directions are identified: (1) Intelligently generate a storyline summarizing the evolution of a hurricane from relevant online corpus; (2) Automatically recommending resolutions according to the textual symptom description in a ticket; (3) Gradually adapting the resolution recommendation system for time correlated features derived from text; (4) Efficiently learning distributed representation for short and lousy ticket symptom descriptions and resolutions. Provided with different types of textual data, data mining techniques proposed in those four research directions successfully address our tasks to understand and extract valuable knowledge from those textual data. My dissertation will address the research topics outlined above. Concretely, I will focus on designing and developing data mining methodologies to better understand textual information, including (1) a storyline generation method for efficient summarization of natural hurricanes based on crawled online corpus; (2) a recommendation framework for automated ticket resolution in IT service management; (3) an adaptive recommendation system on time-varying temporal correlated features derived from text; (4) a deep neural ranking model not only successfully recommending resolutions but also efficiently outputting distributed representation for ticket descriptions and resolutions.
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XIAO, XIN. "Data Mining Techniques for Complex User-Generated Data." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2644046.

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Nowadays, the amount of collected information is continuously growing in a variety of different domains. Data mining techniques are powerful instruments to effectively analyze these large data collections and extract hidden and useful knowledge. Vast amount of User-Generated Data (UGD) is being created every day, such as user behavior, user-generated content, user exploitation of available services and user mobility in different domains. Some common critical issues arise for the UGD analysis process such as the large dataset cardinality and dimensionality, the variable data distribution and inherent sparseness, and the heterogeneous data to model the different facets of the targeted domain. Consequently, the extraction of useful knowledge from such data collections is a challenging task, and proper data mining solutions should be devised for the problem under analysis. In this thesis work, we focus on the design and development of innovative solutions to support data mining activities over User-Generated Data characterised by different critical issues, via the integration of different data mining techniques in a unified frame- work. Real datasets coming from three example domains characterized by the above critical issues are considered as reference cases, i.e., health care, social network, and ur- ban environment domains. Experimental results show the effectiveness of the proposed approaches to discover useful knowledge from different domains.
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Yu, Congcong. "Parallelizing ensemble techniques for data mining." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59416.pdf.

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CID, DANTE JOSE ALEXANDRE. "DATA MINING WITH ROUGH SETS TECHNIQUES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2000. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7244@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Esta dissertação investiga a utilização de Rough Sets no processo de descoberta de conhecimento em Bancos de Dados (KDD - Knowledge Discovery in Databases). O objetivo do trabalho foi avaliar o desempenho da técnica de Rough Sets na tarefa de Classificação de Dados. A Classificação é a tarefa da fase de Mineração de Dados que consiste na descoberta de regras de decisão, ou regras de inferência, que melhor representem um grupo de registros do banco de dados. O trabalho consistiu de cinco etapas principais: estudo sobre o processo de KDD; estudo sobre as técnicas de Rough Sets aplicadas à mineração de dados; análise de ferramentas de mineração de dados do mercado; evolução do projeto Bramining; e a realização de alguns estudos de caso para avaliar o Bramining. O estudo sobre o caso KDD abrangeu todas as suas fases: transformação, limpeza, seleção, mineração de dados e pós-processamento. O resultado obtido serviu de base para o aprimoramento do projeto Bramining. O estudo sobre as técnicas de Rough Sets envolveu a pesquisa de seus conceitos e sua aplicabilidade no contexto de KDD. A teoria de Rough Sets foi apresentada por Zdzislaw Pawlak no início dos anos 80 como uma abordagem matemática para a análise de dados vagos e imprecisos. Este estudo permitiu sua aplicação na ferramenta de mineração de dados desenvolvida. A análise de ferramentas de mineração de dados do mercado abrangeu o estudo e testes de aplicativos baseados em diferentes técnicas, enriquecimento a base de comparação utilizada na avaliação da pesquisa. A evolução do projeto Bramining consistiu no aprimoramento do ambiente KDD desenvolvido em estudos anteriores, passando a incluir técnica de Rough Sets em seu escopo. Os estudos de caso foram conduzidos paralelamente com o uso de Bramining e de outras ferramentas existentes, para efeito de comparação. Os índices apresentados pelo Bramining nos estudos de caso foram considerados, de forma geral, equivalentes aos do software comercial, tendo ambos obtidos regras de boa qualidade na maioria dos casos. O Bramining, entretanto, mostrou-se mais completo para o processo de KDD, graças às diversas opções nele disponíveis para preparação dos dados antes da fase de mineração. Os resultados obtidos comprovaram, através da aplicação desenvolvida, a adequação dos conceitos de Rough Sets à tarefa de classificação de dados. Alguns pontos frágeis da técnica foram identificados, como a necessidade de um mecanismo de apoio para a redução de atributos e a dificuldade em trabalhar com atributos de domínio contínuo. Porém, ao se inserir a técnica em um ambiente mais completo de KDD, como o Bramining, estas deficiências foram sanadas. As opções de preparação da base que o Bramining disponibiliza ao usuário para executar, em particular, a redução e a codificação de atributos permitem deixar os dados em estado adequado à aplicação de Rough Sets. A mineração de dados é uma questão bastante relevante nos dias atuais, e muitos métodos têm sido propostos para as diversas tarefas que dizem respeito a esta questão. A teoria de Rough Sets não mostrou significativas vantagens ou desvantagens em relação a outras técnicas já consagradas, mas foi de grande valia comprovar que há caminhos alternativos para o processo de descoberta de conhecimento.<br>This dissertation investigates the application of Rough Sets to the process of KDD - Knowledge Discovery in Databases. The main goal of the work was to evaluate the performance of Rough Sets techniques in solving the classification problem. Classification is a task of the Data Mining step in KDD Process that performs the discovery of decision rules that best represent a group of registers in a database. The work had five major steps: study of the KDD process; study of Rough Sets techniques applied to data mining; evaluation of existing data mining tools; development of Bramining project; and execution of some case studies to evaluate Bramining. The study of KDD process included all its steps: transformation, cleaning, selection, data mining and post- processing. The results obtained served as a basis to the enhamcement of Bramining. The study of Rough Sets techniques included the research of theory´s concepts and its applicability at KDD context. The Rough Sets tehory has been introduced by Zdzislaw Pawlak in the early 80´s as a mathematical approach to the analysis of vague and uncertain data. This research made possible the implementation of the technique under the environment of the developed tool. The analysis of existing data mining tools included studying and testing of software based on different techniques, enriching the background used in the evaluation of the research. The evolution of Bramining Project consisted in the enhancement of the KDD environment developed in previous works, including the addition of Rough Sets techniques. The case studies were performed simultaneously with Bramining and a commercial minig tool, for comparison reasons. The quality of the knowledge generated by Bramining was considered equivalent to the results of commercial tool, both providing good decision rules for most of the cases. Nevertheless, Bramining proved to be more adapted to the complete KDD process, thanks to the many available features to prepare data to data mining step. The results achieved through the developed application proved the suitability of Rough Sets concepts to the data classification task. Some weaknesses of the technique were identified, like the need of a previous attribute reduction and the inability to deal with continuous domain data. But as the technique has been inserted in a more complete KDD environment like the Bramining Project, those weaknesses ceased to exist. The features of data preparation available in Bramining environment, particularly the reduction and attribute codification options, enable the user to have the database fairly adapted to the use of Rough Sets algorithms. Data mining is a very relevant issue in present days and many methods have been proposed to the different tasks involved in it. Compared to other techniques, Rough Sets Theory did not bring significant advantages or disadvantages to the process, but it has been of great value to show there are alternate ways to knowledge discovery.
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Al-Bataineh, Hussien Suleiman. "Islanding Detection Using Data Mining Techniques." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27634.

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Connection of the distributed generators (DGs), poses new challenges for operation and management of the distribution system. An important issue is that of islanding, where a part of the system gets disconnected from the DG. This thesis explores the use of several data-mining, and machine learning techniques to detect islanding. Several cases of islanding and non- islanding are simulated with a standard test-case: the IEEE 13 bus test distribution system. Different types of DGs are connected to the system and disturbances are introduced. Several classifiers are tested for their effectiveness in identifying islanded conditions under different scenarios. The simulation results show that the random forest classifier consistently outperforms the other methods for a diverse set of operating conditions, within an acceptable time after the onset of islanding. These results strengthen the case for machine-driven based tools for quick and accurate detection of islanding in microgrids.
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JANAKIRAMAN, KRISHNAMOORTHY. "ENTITY IDENTIFICATION USING DATA MINING TECHNIQUES." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin989852516.

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Books on the topic "Data Mining Techniques"

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Berry, Michael J. A. Data Mining Techniques. John Wiley & Sons, Ltd., 2004.

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

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Micheline, Kamber, ed. Data mining: Concepts and techniques. 3rd ed. Elsevier, 2011.

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Micheline, Kamber, ed. Data mining: Concepts and techniques. 2nd ed. Elsevier, 2006.

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Micheline, Kamber, ed. Data mining: Concepts and techniques. Elsevier, 2001.

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Micheline, Kamber, ed. Data mining: Concepts and techniques. Morgan Kaufmann Publishers, 2001.

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Appice, Annalisa, Anna Ciampi, Fabio Fumarola, and Donato Malerba. Data Mining Techniques in Sensor Networks. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5454-9.

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Data mining: Concepts, models and techniques. Springer-Verlag, 2011.

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Shah, Ketan, Neepa Shah, Vinaya Sawant, and Neeraj Parolia. Practical Data Mining Techniques and Applications. Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003390220.

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Carugo, Oliviero, and Frank Eisenhaber, eds. Data Mining Techniques for the Life Sciences. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2095-3.

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Book chapters on the topic "Data Mining Techniques"

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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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Ramos, Nuno M. M., João M. P. Q. Delgado, Ricardo M. S. F. Almeida, Maria L. Simões, and Sofia Manuel. "Data Mining Techniques." In Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22294-3_3.

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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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Thuraisingham, Bhavani, Mohammad Mehedy Masud, Pallabi Parveen, and Latifur Khan. "Data Mining Techniques." In Big Data Analytics with Applications in Insider Threat Detection. Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315119458-4.

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Li, Deren, Shuliang Wang, and Deyi Li. "Methods and Techniques in SDM." In Spatial Data Mining. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5_5.

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Boire, Richard. "Applying Data Mining Techniques." In Data Mining for Managers. Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137406194_12.

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Nenkova, Ani, and Kathleen McKeown. "A Survey of Text Summarization Techniques." In Mining Text Data. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3223-4_3.

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Akinkunmi, Mustapha. "Quantitative Techniques." In Data Mining and Market Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-79390-5_4.

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Akinkunmi, Mustapha. "Qualitative Techniques." In Data Mining and Market Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-79390-5_3.

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Teh, Ying Wah, and Abu Bakar Zaitun. "Data Mining Techniques in Index Techniques." In Intelligent Systems Design and Applications. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44999-7_32.

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Conference papers on the topic "Data Mining Techniques"

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Han, Jiawei. "Data mining techniques." In the 1996 ACM SIGMOD international conference. ACM Press, 1996. http://dx.doi.org/10.1145/233269.280351.

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Agarwal, Shivam. "Data Mining: Data Mining Concepts and Techniques." In 2013 International Conference on Machine Intelligence and Research Advancement (ICMIRA). IEEE, 2013. http://dx.doi.org/10.1109/icmira.2013.45.

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Gago, M., and E. Juaristi. "Can thewavelet-kernelmethodology improve other kernel techniques?" In DATA MINING 2008. WIT Press, 2008. http://dx.doi.org/10.2495/data080081.

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Mattas, Nisha, Smarika, and Deepti Mehrotra. "Comparing Data Mining Techniques for Mining Patents." In 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ACCT). IEEE, 2015. http://dx.doi.org/10.1109/acct.2015.119.

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Senkamalavalli, R., and T. Bhuvaneshwari. "Data mining techniques for CRM." In 2014 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, 2014. http://dx.doi.org/10.1109/icices.2014.7033776.

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Khan, Wajid, Fiaz Hussain, and Edmond C. Prakash. "Intelligent Techniques for Data Mining." In Annual International Conference on Computer Games, Multimedia and Allied Technology. Global Science & Technology Forum (GSTF), 2015. http://dx.doi.org/10.5176/2251-1679_cgat15.51.

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Hong, Tzung-Pei, Chun-Hao Chen, and Vincent S. Tseng. "Genetic-Fuzzy Data Mining Techniques." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.157.

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Grytsenko, T., and A. Peratta. "Performance assessment of parallel techniques." In DATA MINING & INFORMATION ENGINEERING 2007. WIT Press, 2007. http://dx.doi.org/10.2495/data070091.

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Baralis, Elena, Luca Cagliero, Tania Cerquitelli, et al. "NEMICO: Mining Network Data through Cloud-Based Data Mining Techniques." In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC). IEEE, 2014. http://dx.doi.org/10.1109/ucc.2014.72.

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Horta, R. A. M., H. M. Pires, and B. S. L. P. de Lima. "Application of data mining techniques for understanding capital structure of Brazilian companies." In DATA MINING 2009. WIT Press, 2009. http://dx.doi.org/10.2495/data090111.

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Reports on the topic "Data Mining Techniques"

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Wegman, Edward J., and Don R. Faxon. Intrusion Detection Using Data Mining Techniques. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada421061.

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Jajodia, Sushi. Integration of Audit Data Analysis and Mining Techniques into Aide. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada456840.

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Walasek, Lukasz. Web Scraping and Data Mining in R. Instats Inc., 2023. http://dx.doi.org/10.61700/ewzzyuw2l3wtw469.

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This 3-day seminar provides a comprehensive understanding of web scraping and data mining techniques using R software. This workshop is essential for PhD students, professors, and professional researchers, offering practical experience, ethical guidance, and troubleshooting strategies for data extraction and analysis, ultimately enhancing research efficiency and innovation. At the end of this course, participants will be competent at scraping web content and working with APIs. An official Instats completion certificate and 2 ECTS Equivalent points are provided at the seminar's conclusion.
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Trellue, Holly Renee, Michael Lynn Fugate, and Stephen Joesph Tobin. Data Mining Techniques to Estimate Plutonium, Initial Enrichment, Burnup, and Cooling Time in Spent Fuel Assemblies. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1209224.

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Trellue, Holly Renee, Michael Lynn Fugate, and Stephen Joseph Tobin. Data Mining Techniques to Estimate Plutonium, Initial Enrichment, Burnup, and Cooling Time in Spent Fuel Assemblies. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1209318.

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6

Boyer, A., and N. R. Billette. Orebodies and mining environment, links between geology and quantification. Natural Resources Canada/CMSS/Information Management, 1989. http://dx.doi.org/10.4095/331774.

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The application of quantitative techniques to solve geological and geoengineering problems is relatively recent. Often borrowed from other fields, models must be selected with care to ensure that they are appropriate. The introduction, increasing use and rapid development of these techniques follows the fast evolution of more powerful computer hardwares and softwares. Studies have been carried out to establish the usefulness of several quantitative techniques in solving typical geological/geotechnical problems using available exploration and production drillhole data. Results derived from these studies show the techniques are suitable for use in orebody modelling and mine planning. Practical case studies are presented in the report. Following these studies, modelling of bulk material handling systems is used to control mineral or metal feed fluctuations.
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7

Neyedley, K., J. J. Hanley, Z. Zajacz, and M. Fayek. Accessory mineral thermobarometry, trace element chemistry, and stable O isotope systematics, Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328986.

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The Mooshla Intrusive Complex (MIC) is an Archean polyphase magmatic body located in the Doyon-Bousquet-LaRonde (DBL) mining camp of the Abitibi greenstone belt, Québec, that is spatially associated with numerous gold (Au)-rich VMS, epizonal 'intrusion-related' Au-Cu vein systems, and shear zone-hosted (orogenic?) Au deposits. To elucidate the P-T conditions of crystallization, and oxidation state of the MIC magmas, accessory minerals (zircon, rutile, titanite) have been characterized using a variety of analytical techniques (e.g., trace element thermobarometry). The resulting trace element and oxythermobarometric database for accessory minerals in the MIC represents the first examination of such parameters in an Archean magmatic complex in a world-class mineralized district. Mineral thermobarometry yields P-T constraints on accessory mineral crystallization consistent with the expected conditions of tonalite-trondhjemite-granite (TTG) magma genesis, well above peak metamorphic conditions in the DBL camp. Together with textural observations, and mineral trace element data, the P-T estimates reassert that the studied minerals are of magmatic origin and not a product of metamorphism. Oxygen fugacity constraints indicate that while the magmas are relatively oxidizing (as indicated by the presence of magmatic epidote, titanite, and anhydrite), zircon trace element systematics indicate that the magmas were not as oxidized as arc magmas in younger (post-Archean) porphyry environments. The data presented provides first constraints on the depth and other conditions of melt generation and crystallization of the MIC. The P-T estimates and qualitative fO2 constraints have significant implications for the overall model for formation (crystallization, emplacement) of the MIC and potentially related mineral deposits.
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Skow, Jason, Dongliang Lu, and Smitha Koduru. PR-244-133731-R01 In-line Inspection Crack Tool Performance Evaluation. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010580.

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This report discusses the findings of a detailed analysis and evaluation of the performance capabilities of various in-line inspection (ILI) technologies for managing pipeline crack threats, including crack colonies, isolated cracks and cracks in pipe seam welds. The foundation for the study was the development of an extensive database of crack inspection data collected from pipeline operators through an industry data mining exercise. The data were validated to ensure completeness consistency and accuracy, and then stored in a database. The database was used to characterize the performance of ILI technologies with respect to detection, identification and sizing of crack features. ILI technologies considered in the study include magnetic and ultrasonic-based technologies. The study resulted in the collection and analysis of over 50,000 crack features that were identified through crack detection ILI technologies, excavation and field NDE in the ditch, or both. The majority of the features included in the database are cracks detected in stress corrosion cracking (SCC) colonies (approximately 30,000), with a more limited number of features (approximately 6,200) detected in the longitudinal seam of the pipe. These data were analyzed using a number of approaches and techniques to establish the current performance specifications for cracking ILI tools and to identify the areas where improvements can be made.
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Neyedley, K., J. J. Hanley, P. Mercier-Langevin, and M. Fayek. Ore mineralogy, pyrite chemistry, and S isotope systematics of magmatic-hydrothermal Au mineralization associated with the Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328985.

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Abstract:
The Mooshla Intrusive Complex (MIC) is an Archean polyphase magmatic body located in the Doyon-Bousquet-LaRonde (DBL) mining camp of the Abitibi greenstone belt, Québec. The MIC is spatially associated with numerous gold (Au)-rich VMS, epizonal 'intrusion-related' Au-Cu vein systems, and shear zone-hosted (orogenic?) Au deposits. To elucidate genetic links between deposits and the MIC, mineralized samples from two of the epizonal 'intrusion-related' Au-Cu vein systems (Doyon and Grand Duc Au-Cu) have been characterized using a variety of analytical techniques. Preliminary results indicate gold (as electrum) from both deposits occurs relatively late in the systems as it is primarily observed along fractures in pyrite and gangue minerals. At Grand Duc gold appears to have formed syn- to post-crystallization relative to base metal sulphides (e.g. chalcopyrite, sphalerite, pyrrhotite), whereas base metal sulphides at Doyon are relatively rare. The accessory ore mineral assemblage at Doyon is relatively simple compared to Grand Duc, consisting of petzite (Ag3AuTe2), calaverite (AuTe2), and hessite (Ag2Te), while accessory ore minerals at Grand Duc are comprised of tellurobismuthite (Bi2Te3), volynskite (AgBiTe2), native Te, tsumoite (BiTe) or tetradymite (Bi2Te2S), altaite (PbTe), petzite, calaverite, and hessite. Pyrite trace element distribution maps from representative pyrite grains from Doyon and Grand Duc were collected and confirm petrographic observations that Au occurs relatively late. Pyrite from Doyon appears to have been initially trace-element poor, then became enriched in As, followed by the ore metal stage consisting of Au-Ag-Te-Bi-Pb-Cu enrichment and lastly a Co-Ni-Se(?) stage enrichment. Grand Duc pyrite is more complex with initial enrichments in Co-Se-As (Stage 1) followed by an increase in As-Co(?) concentrations (Stage 2). The ore metal stage (Stage 3) is indicated by another increase in As coupled with Au-Ag-Bi-Te-Sb-Pb-Ni-Cu-Zn-Sn-Cd-In enrichment. The final stage of pyrite growth (Stage 4) is represented by the same element assemblage as Stage 3 but at lower concentrations. Preliminary sulphur isotope data from Grand Duc indicates pyrite, pyrrhotite, and chalcopyrite all have similar delta-34S values (~1.5 � 1 permille) with no core-to-rim variations. Pyrite from Doyon has slightly higher delta-34S values (~2.5 � 1 permille) compared to Grand Duc but similarly does not show much core-to-rim variation. At Grand Duc, the occurrence of Au concentrating along the rim of pyrite grains and associated with an enrichment in As and other metals (Sb-Ag-Bi-Te) shares similarities with porphyry and epithermal deposits, and the overall metal association of Au with Te and Bi is a hallmark of other intrusion-related gold systems. The occurrence of the ore metal-rich rims on pyrite from Grand Duc could be related to fluid boiling which results in the destabilization of gold-bearing aqueous complexes. Pyrite from Doyon does not show this inferred boiling texture but shares characteristics of dissolution-reprecipitation processes, where metals in the pyrite lattice are dissolved and then reconcentrated into discrete mineral phases that commonly precipitate in voids and fractures created during pyrite dissolution.
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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 the optimal traffic opening time. However, limitations of the data acquisition and communication systems have significantly hindered the technology’s adoption for practical applications. Furthermore, the packaging of piezoelectric sensor needs to be improved to enable robust performance and better signal quality. In this project, a wireless concrete sensor with a data transmission system was developed. It was comprised of an innovated EMI sensor and miniaturized datalogger with both wireless transmission and USB module. A cloud-based platform for data storage and computation was established, which provides the real time data visualization access to general users and data access to machine learning and data mining developers. Furthermore, field implementations were performed to prove the functionality of the innovated EMI sensor and wireless sensing system for real-time and in-place concrete strength monitoring. This project will benefit the DOTs in areas like construction, operation, and maintenance scheduling and asset management by delivering applicable concrete strength monitoring solutions.
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