Relevant bibliographies by topics / Data mining technologies

Academic literature on the topic 'Data mining technologies'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Data mining technologies"

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Komoliddinqizi, Quldasheva Nargiza, Begimov Oybek Mamarasulovich, and Zarpullayev Urolbek Xudayaro'g'li. "Modern data mining technologies." ACADEMICIA: An International Multidisciplinary Research Journal 10, no. 4 (2020): 657. http://dx.doi.org/10.5958/2249-7137.2020.00129.9.

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Lu, Johan. "Editorial: [XML technologies and Data Mining]." Open Information Systems Journal 3, no. 2 (September 1, 2009): 68. http://dx.doi.org/10.2174/1874133900903020068.

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Loginova, Lyudmila N., and Alexander M. Shash. "Data Mining technologies in managing the assortment of trading companies." Journal of Applied Informatics 16, no. 91 (February 26, 2021): 99–109. http://dx.doi.org/10.37791/2687-0649-2021-16-1-99-109.

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In the conditions of fierce competition, satisfaction of all customer needs provides a trading enterprise with a sustainable competitive advantage. With the traditional structure of the assortment, there is a decrease in both the potential and real level of profit, the loss of competitive positions in promising markets, and, therefore, there is a decrease in the stability of the enterprise. The development of an analysis system to determine the specifics of the product range, optimize the range, and adapt it to the conditions of the Russian market is undoubtedly an urgent task. This article provides an overview of trade and IT companies that use data mining technologies. The survey showed that many companies are using data mining technology to improve customer service, turnover and sales in stores. In this regard, the management of Familia decided to develop its own software that will combine the analysis of turnover and sales in the company's stores in order to increase sales and improve the placement of goods in stores so that the client buys the necessary things, increasing the company's profit. The paper shows the possibility of combining several data mining methods in one system; shows the results of the analysis system and shows the effectiveness of the developed analysis system at Familia. The uniqueness of the developed software is the combination of data mining algorithms into one software product. The developed analysis system, based on the joint work of two data mining algorithms K-means and Apriori, allows you to manage the range of trade enterprises, reducing company losses.
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-Marcotorchino, Jean-François. "Les technologies avancées de l'analyse de l'information : Text Mining, Data Mining et Fusion Data Mining-Text Mining." Revue de l'Electricité et de l'Electronique -, no. 07 (2001): 56. http://dx.doi.org/10.3845/ree.2001.077.

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Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (September 20, 2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.

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Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.
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Lu, Johan. "Hot Topic: [XML technologies and Data Mining]." Open Information Systems Journal 3, no. 1 (September 1, 2009): 68–145. http://dx.doi.org/10.2174/1874133900903010068.

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Lemke, Frank, and Johann-Adolf Müller. "Medical data analysis using self-organizing data mining technologies." Systems Analysis Modelling Simulation 43, no. 10 (October 2003): 1399–408. http://dx.doi.org/10.1080/02329290290027337.

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Madukwe, Atkinson Ike, Jennifer Somtochukwu Madukwe, Justina Ogochukwu Osonwa, and Obiora Chukwuemerie Ernest. "The Impact of Emerging Technologies on Data Mining." Asian Journal of Science and Applied Technology 10, no. 1 (May 15, 2021): 24–26. http://dx.doi.org/10.51983/ajsat-2021.10.1.2814.

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Business analytics has improved tremendously in recent past providing business leaders’ insights, particularly from operational data stored in transactional system. An example is e-commerce data analysis, which has recently come to be viewed as a killer appropriate for the field of data mining.
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Marinov, Miroslav, Abu Saleh Mohammad Mosa, Illhoi Yoo, and Suzanne Austin Boren. "Data-Mining Technologies for Diabetes: A Systematic Review." Journal of Diabetes Science and Technology 5, no. 6 (November 2011): 1549–56. http://dx.doi.org/10.1177/193229681100500631.

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Zaslavskaya, Veronika L. "CURRENT TECHNOLOGIES, METHODS AND TECHNIQUES OF DATA MINING." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 11/1, no. 131 (2022): 151–64. http://dx.doi.org/10.36871/ek.up.p.r.2022.11.01.019.

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The relevance of this topic has especially increased in the era of the digital age, since we use various types of information in absolutely every area and sphere of life, but it becomes especially relevant in relation to business. In the XXI century, information is transmitted with incredible speed and, with proper data analysis, can become the most valuable asset of a company. This can help companies improve certain aspects of their products and services, as well as the overall brand image and customer service quality. With the help of data analysis, you can discover the weaknesses and strengths of your competitors, opening up opportunities for increasing the potential of competitiveness.
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Dissertations / Theses on the topic "Data mining technologies"

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Mamčenko, Jelena. "Data mining technologies for distributed servers' efficiency." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20090105_150115-82504.

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The main idea is an application of data mining technologies in order to increase distributed servers’ efficiency using data mining methods and agent’s technology. The objects of investigation are data from document based model database and its using by allocatable servers.
Disertacijoje nagrinėjamos šiuolaikiškos duomenų gavybos technologijos serverių našumui gerinti, taikant įvairius duomenų gavybos metodus ir agentines technologijas. Pagrindinis tyrimo objektas – dokumentinių duomenų bazių duomenys ir jų naudojimas išskirstytuose serveriuose.
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Rentzsch, Viola. "Human trafficking 2.0 the impact of new technologies." University of the Western Cape, 2021. http://hdl.handle.net/11394/8353.

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Magister Legum - LLM
Human history is traversed by migration. This manifold global phenomenon has shaped the world to its current state, moving people from one place to another in reaction to the changing world. The autonomous decision to permanently move locations represents only a segment of what is considered to be migration. Routes can be dangerous, reasons can be without any alternative, displacements forced, and journeys deadly. Arguably the most fatal of all long-distance global migration flows, the transatlantic slave trade has left an enduring legacy of economic patterns and persistent pain. Whilst the trade in human beings originated centuries before, with Europe’s long history of slavery, this event represents an atrocious milestone in history. In a nutshell, European colonialists traded slaves for goods from African kings, who had captured them as war prisoners.
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Jiang, Lu. "Advanced imaging and data mining technologies for medical and food safety applications." College Park, Md. : University of Maryland, 2009. http://hdl.handle.net/1903/9862.

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Thesis (Ph.D.) -- University of Maryland, College Park, 2009.
Thesis research directed by: Fischell Dept. of Bioengineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Espinoza, Sofia Elizabeth. "Data mining methods applied to healthcare problems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44903.

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Growing adoption of health information technologies is allowing healthcare providers to capture and store enormous amounts of patient data. In order to effectively use this data to improve healthcare outcomes and processes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various data mining and statistical methods that could be applied to the type of datasets that are found in healthcare research. We discuss the process of identification of appropriate measures and statistical tools, the analysis and validation of mathematical models, and the interpretation of results to improve healthcare quality and safety. We illustrate the application of statistics and data mining techniques on three real-world healthcare datasets. In the first chapter, we develop a new method to assess hydration status using breath samples. Through analysis of the more than 300 volatile organic compounds contained in human breath, we aim to identify markers of hydration. In the second chapter, we evaluate the impact of the implementation of an electronic medical record system on the rate of inpatient medication errors and adverse drug events. The objective is to understand the impact on patient safety of different information technologies in a specific environment (inpatient pediatrics) and to provide recommendations on how to correctly analyze count data with a large amount of zeros. In the last chapter, we develop a mathematical model to predict the probability of developing post-operative nausea and vomiting based on patient demographics and clinical history, and to identify the group of patients at high-risk.
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Reipas, Artūras. "Verslo analizės metodų taikymas mažų įmonių informacinėse sistemose." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070115_092850-31887.

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In current work problems and requirements for demand forecasting in commercial or manufacturing enterprises are analyzed and suitable forecasting algorithms are proposed. In enterprises with multidimensional and heterogeneous demand it is advisable to use different algorithms for different demand constituents and to readjust parameters used for forecasting. Existing forecasting packages are not practical as they are not integrated with commodities or materials supply orders management activities and business processes of enterprise. The orders management system is developed with forecasting component using adopted time series forecasting techniques such as moving average, exponential smoothing, double exponential smoothing. These techniques ensure reliable forecasting results for different time series models: random, trend and are integrated with other business management activities. It is possible to calculate deviations of forecasted demand from factual values, to select algorithms giving minimal perсentage error, and to adjust algorithms parameters to changing demand. The system can help managers to choose forecasting algorithms and to adapt their parameters in the course of time. The system is designed using UML CASE tool and implemented in Microsoft .Net environment using MS SQL Server 2005 for data storage.
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Dagnely, Pierre. "Scalable Performance Assessment of Industrial Assets: A Data Mining Approach." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/288650/5/contratPD.pdf.

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Nowadays, more and more industrial assets are continuously monitored and generate vast amount of event logs and sensor data. Data Mining is the field concerned with the exploration and exploitation of these data. Despite the fact that data mining has been researched for decades, the event log data are still underexploited in most data mining workflows although they could provide valuable insights on the asset behavior as they represent the internal processes of an asset. However, exploitation of event log data is challenging, mainly as: 1) event labels are not consistent across manufacturers, 2) assets report vast amount of data from which only a small part may be relevant, 3) textual event logs and numerical sensor data are usually processed by methods dedicated respectively to textual data or sensor data, methods combining both types of data are still missing, 4) industrial data are rarely labelled, i.e. there is no indication on the actual performance of the asset and it has to be derived from other sources, 5) the meaning of an event may vary depending on the events send after or before.Concretely, this thesis is concerned with the conception and validation of an integrated data processing framework for scalable performance assessment of industrial asset portfolios. This framework is composed of several advanced methodologies facilitating exploitation of both event logs and time series sensor data: 1) an ontology model describing photovoltaic (the validation domain) event system allowing the integration of heterogeneous event generated by various manufacturers; 2) a novel and computationally scalable methodology enabling automatic calculation of event relevancy score without any prior knowledge; 3) a semantically enriched multi-level pattern mining methodology enabling data exploration and hypothesis building across heterogeneous assets; 4) an advanced workflow extracting performance profiles by combining textual event logs and numerical sensor values; 5) a scalable methodology allowing rapid annotation of new asset runs with a known performance label only based on the event logs data.The framework has been exhaustively validated on real-world data from PV plants, provided by our industrial partner 3E. However, the framework has been designed to be domain agnostic and can be adapted to other industrial assets reporting event logs and sensor data.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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Orakzai, Faisal Moeen. "Movement Pattern Mining over Large-Scale Datasets." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/285611/4/TOC.pdf.

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Movement pattern mining involves the processing of movement data to understand the mobility behaviour of humans/animals. Movement pattern mining has numerous applications, e.g. traffic optimization, event planning, optimization of public transport and carpooling. The recent digital revolution has caused a wide-spread use of smartphones and other devices equipped with GPS. These devices produce a tremendous amount of movement data which contains valuable mobility information. Many interesting mobility patterns and algorithms to mine them have been proposed in recent years to mine different types of mobility behaviours, e.g. convoy, flock, group, swarm or platoon, etc. The drastic increase in the volumes of data being generated limits the use of these algorithms in the mining of movement patterns on real-world data sizes because of their lack of scalability.This thesis deals with three aspects of movement pattern mining, i.e. scalability, efficiency, and real-timeliness with a focus on convoy pattern mining. A convoy pattern is a group of objects moving together for a certain period. Mining of convoy pattern involves clustering of the movement dataset at each timestamp and then merging the clusters to form convoys. Clustering the whole dataset is a limiting factor in the scalability of existing algorithms. One way to solve the scalability problem is to mine convoys in parallel. Parallel mining can be done either using the existing distributed spatiotemporal data processing system like Parallel Secondo or by using a general distributed data processing system. We first test the scalability behaviour of Parallel Secondo for mining movement patterns and conclude that it is not an industrial grade system and its scalability is limited. An essential part of designing distributed data processing algorithms is the data partitioning strategy. We study three different data partitioning strategies, i.e. Object-based, spatial and temporal. We analyze their suitability to convoy pattern mining based on 5 properties, i.e. data exchange, data redundancy, partitioning cost, disk seeks and data ordering. Our study shows that the temporal partitioning strategy is best suited for convoy mining as it is easily parallelizable and less complicated. The observations in our study also apply to other movement pattern mining algorithms, e.g. flock, group or platoon, etc.Based on the temporal partitioning strategy, we propose a generic distributed shared nothing convoy mining algorithm called DCM which is linearly scalable concerning the data size, data density and the number of nodes. DCM can be implemented using any distributed data processing framework. For our experiments, we implemented the algorithm using the Hadoop MapReduce framework. It performs better than the existing sequential algorithms, i.e. CuTs family of algorithms by an order of magnitude on different computing architectures, e.g. single x86 machine, multi-core cluster with NUMA architecture and multi-node SMP clusters. Although DCM is a scalable distributed algorithm which can process huge datasets, the cost of maintaining the cluster is high. Also, the heavy computation it incurs because of the requirement of clustering the whole dataset is not resource-efficient.To solve the efficiency problem of DCM, we propose a new sequential algorithm called k/2-hop which even being a sequential algorithm can perform orders of magnitude faster than the existing state-of-the-art sequential as well as distributed algorithms. The main strength of the algorithm is its pruning capability. Our experiments show that it can prune up to 99% of the data. k/2-hop uses a notion of benchmark points which are timestamps separated by k/2 timestamps where k is the minimum length of the convoys to be mined. We prove that to be able to mine maximal convoys; we need to cluster the data belonging to the benchmark points only. For the timestamps between two consecutive benchmark points, we propose an efficient mining algorithm called the Hop Window Mining Tree (HWMT). HWMT clusters the data corresponding to only those objects that are part of a cluster in the benchmark points. k/2-hop is a batch algorithm that can mine convoys very fast, but we only get the result when the complete dataset has been processed. Also, it requires the data to be indexed for better performance and thus cannot be used in real-time scenarios. We propose a streaming variant of the k/2-hop algorithm which does not require the input dataset to be indexed and can process a stream of data. It outputs the mined convoys as and when they are discovered. The streaming k/2-hop algorithm is very memory efficient and can process data that is many times bigger than the memory made available to the algorithm. We show through experiments that if we include the data loading and indexing time in the runtime of the k/2-hop algorithm, streaming k/2-hop is the fastest convoy mining algorithm to date. Convoy pattern is part of a bigger category of co-movement patterns, and most of the observations (if not all) made in this thesis about convoy pattern mining also apply to other patterns of the category such as flock, group or platoon, etc. This applicability means that a generic batch and streaming distributed co-movement pattern mining framework can be build using the k/2 technique.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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Jiang, Haotian. "WEARABLE COMPUTING TECHNOLOGIES FOR DISTRIBUTED LEARNING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1571072941323463.

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Jafer, Yasser. "Task Oriented Privacy-preserving (TOP) Technologies Using Automatic Feature Selection." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34320.

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A large amount of digital information collected and stored in datasets creates vast opportunities for knowledge discovery and data mining. These datasets, however, may contain sensitive information about individuals and, therefore, it is imperative to ensure that their privacy is protected. Most research in the area of privacy preserving data publishing does not make any assumptions about an intended analysis task applied on the dataset. In many domains such as healthcare, finance, etc; however, it is possible to identify the analysis task beforehand. Incorporating such knowledge of the ultimate analysis task may improve the quality of the anonymized data while protecting the privacy of individuals. Furthermore, the existing research which consider the ultimate analysis task (e.g., classification) is not suitable for high-dimensional data. We show that automatic feature selection (which is a well-known dimensionality reduction technique) can be utilized in order to consider both aspects of privacy and utility simultaneously. In doing so, we show that feature selection can enhance existing privacy preserving techniques addressing k-anonymity and differential privacy and protect privacy while reducing the amount of modifications applied to the dataset; hence, in most of the cases achieving higher utility. We consider incorporating the concept of privacy-by-design within the feature selection process. We propose techniques that turn filter-based and wrapper-based feature selection into privacy-aware processes. To this end, we build a layer of privacy on top of regular feature selection process and obtain a privacy preserving feature selection that is not only guided by accuracy but also the amount of protected private information. In addition to considering privacy after feature selection we introduce a framework for a privacy-aware feature selection evaluation measure. That is, we incorporate privacy during feature selection and obtain a list of candidate privacy-aware attribute subsets that consider (and satisfy) both efficacy and privacy requirements simultaneously. Finally, we propose a multi-dimensional, privacy-aware evaluation function which incorporates efficacy, privacy, and dimensionality weights and enables the data holder to obtain a best attribute subset according to its preferences.
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Inthasone, Somsack. "Techniques d'extraction de connaissances en biodiversité." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4013/document.

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Les données sur la biodiversité sont généralement représentées et stockées dans différents formats. Cela rend difficile pour les biologistes leur agrégation et leur intégration afin d'identifier et découvrir des connaissances pertinentes dans le but, par exemple, de classer efficacement des spécimens. Nous présentons ici l'entrepôt de données BioKET issu de la consolidation de données hétérogènes de différentes sources. Actuellement, le champ d'application de BioKET concerne la botanique. Sa construction a nécessité, notamment, d'identifier et analyser les ontologies et bases botaniques existantes afin de standardiser et lier les descripteurs utilisés dans BioKET. Nous avons également développé une méthodologie pour la construction de terminologies taxonomiques, ou thésaurus, à partir d'ontologies de plantes et d'informations géo-spatiales faisant autorité. Les données de biodiversité et botanique de quatre fournisseurs majeurs et de deux systèmes d'informations géo-spatiales ont été intégrées dans BioKET. L'utilité d'un tel entrepôt de données a été démontrée par l'application de méthodes d'extraction de modèles de connaissances, basées sur les approches classiques Apriori et de la fermeture de Galois, à des ensembles de données générées à partir de BioKET. En utilisant ces méthodes, des règles d'association et des clusters conceptuels ont été extraits pour l'analyse des statuts de risque de plantes endémiques au Laos et en Asie du Sud-Est. En outre, BioKET est interfacé avec d'autres applications et ressources, tel que l'outil GeoCAT pour l'évaluation géo-spatiale des facteurs de risques, afin de fournir un outil d'analyse performant pour les données de biodiversité
Biodiversity data are generally stored in different formats. This makes it difficult for biologists to combine and integrate them in order to retrieve useful information and discover novel knowledge for the purpose of, for example, efficiently classifying specimens. In this work, we present the BioKET data warehouse which is a consolidation of heterogeneous data stored in different formats and originating from different sources. For the time being, the scope of BioKET is botanical. Its construction required, among others things, to identify and analyze existing botanical ontologies, to standardize and relate terms in BioKET. We also developed a methodology for mapping and defining taxonomic terminologies, that are controlled vocabularies with hierarchical structures from authoritative plant ontologies, Google Maps, and OpenStreetMap geospatial information system. Data from four major biodiversity and botanical data providers and from the two previously mentioned geospatial information systems were then integrated in BioKET. The usefulness of such a data warehouse was demonstrated by applying classical knowledge pattern extraction methods, based on the classical Apriori and Galois closure based approaches, to several datasets generated from BioKET extracts. Using these methods, association rules and conceptual bi-clusters were extracted to analyze the risk status of plants endemic to Laos and Southeast Asia. Besides, BioKET is interfaced with other applications and resources, like the GeoCAT Geospatial Conservation Assessment Tool, to provide a powerful analysis tool for biodiversity data
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Books on the topic "Data mining technologies"

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

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International Conference on Data Mining (10th 2009 Crete, Greece). Data mining X: Data mining, protection, detection and other security technologies. Edited by Zanasi A, Ebecken N. F. F, and Brebbia C. A. Southampton, UK: WIT Press, 2009.

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International Conference on Data Mining (10th 2009 Crete, Greece). Data mining X: Data mining, protection, detection and other security technologies. Edited by Zanasi A, Ebecken N. F. F, and Brebbia C. A. Southampton, UK: WIT Press, 2009.

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International Conference on Data Mining (10th 2009 Crete, Greece). Data mining X: Data mining, protection, detection and other security technologies. Edited by Zanasi A, Ebecken N. F. F, and Brebbia C. A. Southampton, UK: WIT Press, 2009.

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International Conference on Data Mining (9th 2008 Cadiz, Spain). Data mining IX: Data mining, protection, detection and other security technologies. Edited by Zanasi A. Southampton: WIT, 2008.

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International Conference on Data Mining (10th 2009 Crete, Greece). Data mining X: Data mining, protection, detection and other security technologies. Edited by Zanasi A, Ebecken N. F. F, and Brebbia C. A. Southampton, UK: WIT Press, 2009.

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International Conference on Data Mining (9th 2008 Cadiz, Spain). Data mining IX: Data mining, protection, detection and other security technologies. Edited by Zanasi A. Southampton: WIT, 2008.

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Data mining: Technologies, techniques, tools, and trends. Boca Raton: CRC Press, 1999.

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Hassanien, Aboul Ella, Siddhartha Bhattacharyya, Satyajit Chakrabati, Abhishek Bhattacharya, and Soumi Dutta, eds. Emerging Technologies in Data Mining and Information Security. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4367-2.

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Tavares, João Manuel R. S., Satyajit Chakrabarti, Abhishek Bhattacharya, and Sujata Ghatak, eds. Emerging Technologies in Data Mining and Information Security. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9774-9.

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Book chapters on the topic "Data mining technologies"

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Wang, Shuliang, and Tisinee Surapunt. "Spatial Data Mining." In Encyclopedia of Big Data Technologies, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_66-1.

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Wang, Shuliang, and Tisinee Surapunt. "Spatial Data Mining." In Encyclopedia of Big Data Technologies, 1546–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_66.

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Senderovich, Arik. "Queue Mining." In Encyclopedia of Big Data Technologies, 1–8. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_101-1.

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Senderovich, Arik. "Queue Mining." In Encyclopedia of Big Data Technologies, 1351–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_101.

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Fürnkranz, Johannes, Dragan Gamberger, and Nada Lavrač. "Machine Learning and Data Mining." In Cognitive Technologies, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-75197-7_1.

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Maggi, Fabrizio Maria. "Declarative Process Mining." In Encyclopedia of Big Data Technologies, 1–8. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_92-1.

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Maggi, Fabrizio Maria. "Declarative Process Mining." In Encyclopedia of Big Data Technologies, 625–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_92.

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Folino, Francesco, and Luigi Pontieri. "Business Process Deviance Mining." In Encyclopedia of Big Data Technologies, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_100-1.

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Fahland, Dirk. "Artifact-Centric Process Mining." In Encyclopedia of Big Data Technologies, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_93-1.

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Folino, Francesco, and Luigi Pontieri. "Business Process Deviance Mining." In Encyclopedia of Big Data Technologies, 389–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_100.

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Conference papers on the topic "Data mining technologies"

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Ciubancan, Mihai, G. Neculoiu, O. Grigoriu, I. Halcu, V. Sandulescu, M. Marinescu, and V. Marinescu. "Data mining processing using GRID technologies." In 2013 RoEduNet International Conference, 11th Edition: Networking in Education and Research. IEEE, 2013. http://dx.doi.org/10.1109/roedunet.2013.6511737.

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Dol, Sunita M., and Pradeep M. Jawandhiya. "Use of Data mining Tools in Educational Data Mining." In 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT). IEEE, 2022. http://dx.doi.org/10.1109/ccict56684.2022.00075.

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Larson, Ray R., Richard Marciano, Chien-Yi Hou, Shreyas, Paul Watry, John Harrison, Luis Aguilar, and Jerome Fuselier. "Integrating Data Mining and Data Management Technologies for Scholarly Inquiry." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004455.

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Dananjali, T., S. Wijesinghe, and J. Ekanayake. "Forecasting Weekly Rainfall Using Data Mining Technologies." In 2020 From Innovation to Impact (FITI). IEEE, 2020. http://dx.doi.org/10.1109/fiti52050.2020.9424877.

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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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"ARCHITECTURE-CENTRIC DATA MINING MIDDLEWARE SUPPORTING MULTIPLE DATA SOURCES AND MINING TECHNIQUES." In 2nd International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0001326102240227.

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da Silva, Victor Regis Lyra Beserra, Fabio de Albuquerque Silva, and Vanilson Buregio. "Characterizing Educational Data Mining." In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2019. http://dx.doi.org/10.23919/cisti.2019.8760815.

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Oskin, A., and D. Oskin. "About the curriculum of the course “Data Mining – Data Mining” for undergraduates of the specialty “History”." In Historical research in the context of data science: Information resources, analytical methods and digital technologies. LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1847.978-5-317-06529-4/447-453.

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The article discusses the curriculum of the course “Data Mining – Data Mining” for graduate students studying in the specialty “History”. The definition of the term “Data Mining” is given, the areas of application are listed and the importance of mastering these technologies by undergraduates of this specialty is emphasized. The content of the lecture component of the discipline, laboratory workshop is considered, a list of useful Internet resources is provided
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Pu, Wang, and Wang Jun-qing. "Intrusion detection system with the data mining technologies." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6013951.

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Zhang Qun and Huang Wen-jie. "Research on data mining technologies appling intrusion detection." In 2010 IEEE International Conference on Emergency Management and Management Sciences (ICEMMS). IEEE, 2010. http://dx.doi.org/10.1109/icemms.2010.5563461.

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Reports on the topic "Data mining technologies"

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Zelinska, Snizhana O., Albert A. Azaryan, and Volodymyr A. Azaryan. Investigation of Opportunities of the Practical Application of the Augmented Reality Technologies in the Information and Educative Environment for Mining Engineers Training in the Higher Education Establishment. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2672.

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The augmented reality technologies allow receiving the necessary data about the environment and improvement of the information perception. Application of the augmented reality technologies in the information and educative environment of the higher education establishment will allow receiving the additional instrumental means for education quality increasing. Application of the corresponding instrumental means, to which the platforms of the augmented reality Vuforia, ARToolKit, Kudan can be referred, will allow presenting the lecturers the necessary tools for making of the augmented reality academic programs.
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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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Balyk, Nadiia, Svitlana Leshchuk, and Dariia Yatsenyak. Developing a Mini Smart House model. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3741.

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The work is devoted to designing a smart home educational model. The authors analyzed the literature in the field of the Internet of Things and identified the basic requirements for the training model. It contains the following levels: command, communication, management. The authors identify the main subsystems of the training model: communication, signaling, control of lighting, temperature, filling of the garbage container, monitoring of sensor data. The proposed smart home educational model takes into account the economic indicators of resource utilization, which gives the opportunity to save on payment for their consumption. The hardware components for the implementation of the Mini Smart House were selected in the article. It uses a variety of technologies to conveniently manage it and use renewable energy to power it. The model was produced independently by students involved in the STEM project. Research includes sketching, making construction parts, sensor assembly and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Research includes sketching, making some parts, assembly sensor and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Approbation Mini Smart House researches were conducted within activity the STEM-center of Physics and Mathematics Faculty of Ternopil Volodymyr Hnatiuk National Pedagogical University, in particular during the educational process and during numerous trainings and seminars for pupils and teachers of computer science.
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Bendikov, Michael, and Thomas C. Harmon. Development of Agricultural Sensors Based on Conductive Polymers. United States Department of Agriculture, August 2006. http://dx.doi.org/10.32747/2006.7591738.bard.

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In this 1-year feasibility study, we tried polymerization of several different monomers, commercial as well as novel, specially designed and synthesized for this project in the presence of the nitrate ion to produce imprinted conductive polymers. Polymers 1 and 2 (shown below) produced a response to nitrate, but one inferior to that produced by a polypyrrole (Ppy)-based sensor (which we demonstrated prior to this study). Thus, we elected to proceed with improving the stability of the Ppy-based sensor. In order to improve stability of the Ppy-based sensor, we created a two-layer design which includes nitrate-doped Ppy as an inner layer, and nitrate-doped PEDOT as the outer layer. PEDOT is known for its high environmental stability and conductivity. This design has demonstrated promise, but is still undergoing optimization and stability testing. Previously we had failed to create nitrate-doped PEDOT in the absence of a Ppy layer. Nitrate-doped PEDOT should be very promising for sensor applications due to its high stability and exceptional sensing properties as we showed previously for sensing of perchlorate ions (by perchlorate-doped PEDOT). During this year, we have succeeded in preparing nitrate-doped PEDOT (4 below) by designing a new starting monomer (compound 3 below) for polymerization. We are currently testing this design for nitrate sensing. In parallel with the fabrication design studies, we fabricated and tested nitrate-doped Ppy sensors in a series of flow studies under laboratory and field conditions. Nitrate-doped Ppy sensors are less stable than is desirable but provide excellent nitrate sensing characteristics for the short-term experiments focusing on packaging and deployment strategies. The fabricated sensors were successfully interfaced with a commercial battery-powered self-logging (Onset Computer Hobo Datalogger) and a wireless data acquisition and transmission system (Crossbow Technologies MDA300 sensor interface and Mica2 wireless mote). In a series of flow-through experiments with water, the nitrate-doped Ppy sensors were exposed to pulses of dissolved nitrate and compared favorably with an expensive commercial sensor. In 24-hour field tests in both Merced and in Palmdale, CA agricultural soils, the sensors responded to introduced nitrate pulses, but with different dynamics relative to the larger commercial sensors. These experiments are on-going but suggest a form factor (size, shape) effect of the sensor when deployed in a porous medium such as soil. To fill the need for a miniature reference electrode, we identified and tested one commercial version (Cypress Systems, ESA Mini-reference electrode) which works well but is expensive ($190). To create an inexpensive miniature reference electrode, we are exploring the use of AgCl-coated silver wire. This electrode is not a “true” reference electrode; however, it can calibrated once versus a commercial reference electrode at the time of deployment in soil. Thus, only one commercial reference electrode would suffice to support a multiple sensor deployment.
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