Relevant bibliographies by topics / KNIME Analytics Platform

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Academic literature on the topic 'KNIME Analytics Platform'

Author: Grafiati
Published: 4 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "KNIME Analytics Platform"

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Andrey Vladimirovich, Kulev, and Kulev Maxim Vladimirovich. "ANALYSISOFTHELEVELOFTRAININGOF STUDENTS OF TRANSPORT DIRECTIONS IN THE KNIME ANALYTICS PLATFORM SOFTWARE ENVIRONMENT." World of transport and technological machines 1(79), no. 4 (2022): 119–24. http://dx.doi.org/10.33979/2073-7432-2022-1(79)-4-119-124.

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The aim of the work is to analyze the data and visualize the results of compliance with the requirements of employers and students' knowledge of transport areas at OSU named after I.S. Turgenev in the KNIME Analytics Platform software environment. To achieve the goal in the article, a working project was developed in the KNIME Analytics Platform; the main requirements of employers to applicants for the position of a logistician were identified; the analysis of the content of the work program of the discipline «Transport logistics» was carried out; a conclusion was made on the conformity of the level of training of students in transport areas at the Oryol State University named after I.S. Turgenev to the requirements of employers.
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Магамедова, Д. М., А. И. Джангаров та Д. Ш. Калхиташвили. "Структура платформы Knime Analytics Platform и ее возможности". ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ 80, № 2 (2021): 125–27. http://dx.doi.org/10.18411/trnio-12-2021-92.

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Стремительное развитие технологий, постоянный обмен информацией и значительное увеличение передаваемых данных привело к появлению множества алгоритмов по их обработке, платформ для реализации самих алгоритмов и к прочному закреплению понятия «большие данные (Big data)» в современном обществе. Рынок труда пополнился новыми профессиями, такими как: специалист по обработке данных, анализу данных, архитектор данных и др. В статье рассматривается одна из платформ, обладающей современными программными инструментами для анализа данных.
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Egorova, Daria K., and Roman V. Denisov. "Visual Scenario Development Platform Used to Model Real Estate Market Processes." Ogarev-online 13, no. 1 (2025): 53–63. https://doi.org/10.15507/2311-2468.013.202501.053-063.

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Introduction. The real estate market is a key sector of the economy with high price dynamics, dependence on macroeconomic factors and complexity of forecasting. Traditional analysis methods require a lot of time and resources, which limits their application. Using low-code platforms allows you to reduce the cost of developing models and make analysis tools accessible to specialists without advanced programming skills. The purpose of the study is to demonstrate the use of KNIME to predict the value of real estate and their classification. In addition, it is necessary to assess how accurate the models are and how useful they are in practice. Materials and Methods. Real estate market processes were modeled using KNIME Analytics Platform for visual scenario development. Real estate data is collected using the Cyanparser Python library, and regression analysis and data visualization methods are implemented in KNIME Analytics Platform. Results. Linear and polynomial regression of real estate prices according to specified parameters is constructed, clusterization of real estate objects and visualization of the results are performed. Clustering revealed three groups of objects correlating with location and infrastructure. Discussion and Conclusion. KNIME has confirmed its effectiveness as a low-code tool for analyzing the real estate market. The materials of the article can be useful for understanding the dynamics of the real estate market and forecasting its future trends.
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BUNYAKINA, EKATERINA V., and MAXIM I. GALCHENKO. "APPLICATION OF H2O FRAMEWORK IN TIME SERIES PROCESSING." H&ES Research 12, no. 4 (2020): 56–64. http://dx.doi.org/10.36724/2409-5419-2020-12-4-56-64.

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Currently, one of the most popular advanced analytics area is time series analysis. Stored data of measuring instruments, detectors of various kinds can serve as examples of generating this kind of data. There are quite a few integrated platforms proposals on the market for building deep learning models. In this situation, the H2O platform (framework) is a unique offer, due to its free, comprehensive, low entry threshold and scalability. Particular interest in the framework is also dictated by the fact that the developers have provided the opportunity to access the platform algorithms in R and Python using libraries, as well as the availability of the Sparkling Water application for Apache Spark. In the KNIME Analytics Platform 4.X, the KNIME Labs — Deep Learning node group has well-tuned nodes that can invoke the corresponding H2O algorithms. Currently, not all H2O algorithms have been implemented yet, but the KNIME development process allows us to expect that everything remaining will be implemented soon. The article provides general information, as well as an example of using the platform for the task of time series forecasting. The order of the functions in the code, the basic settings, as well as the call features that occur when working on a single PC while working in R are shown. The process of interaction with the web interface and the implementation of functions in the statistical programming language R is described. The use of simulation results, namely, relative importance, is demon strated. predictors in H2O to simplify the data set and increase the rate of convergence of the algorithm.
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Mazanetz, Michael P., Charlotte H. F. Goode, and Ewa I. Chudyk. "Ligand- and Structure-Based Drug Design and Optimization using KNIME." Current Medicinal Chemistry 27, no. 38 (2020): 6458–79. http://dx.doi.org/10.2174/0929867326666190409141016.

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In recent years there has been a paradigm shift in how data is being used to progress early drug discovery campaigns from hit identification to candidate selection. Significant developments in data mining methods and the accessibility of tools for research scientists have been instrumental in reducing drug discovery timelines and in increasing the likelihood of a chemical entity achieving drug development milestones. KNIME, the Konstanz Information Miner, is a leading open source data analytics platform and has supported drug discovery endeavours for over a decade. KNIME provides a rich palette of tools supported by an extensive community of contributors to enable ligandand structure-based drug design. This review will examine recent developments within the KNIME platform to support small-molecule drug design and provide a perspective on the challenges and future developments within this field.
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Liawrungrueang, Wongthawat, Sung Tan Cho, Vit Kotheeranurak, Alvin Pun, Khanathip Jitpakdee, and Peem Sarasombath. "Artificial neural networks for the detection of odontoid fractures using the Konstanz Information Miner Analytics Platform." Asian Spine Journal 18, no. 3 (2024): 407–14. http://dx.doi.org/10.31616/asj.2023.0259.

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Study Design: An experimental study.Purpose: This study aimed to investigate the potential use of artificial neural networks (ANNs) in the detection of odontoid fractures using the Konstanz Information Miner (KNIME) Analytics Platform that provides a technique for computer-assisted diagnosis using radiographic X-ray imaging.Overview of Literature: In medical image processing, computer-assisted diagnosis with ANNs from radiographic X-ray imaging is becoming increasingly popular. Odontoid fractures are a common fracture of the axis and account for 10%–15% of all cervical fractures. However, a literature review of computer-assisted diagnosis with ANNs has not been made.Methods: This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME’s graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation.Results: The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model’s accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures.Conclusions: The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.
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Maraza-Quispe, Benjamin, Ricardo Carlos Quispe-Figueroa, Manuel Alejandro Valderrama-Solis, and Benjamin Maraza-Quispe. "Dashboard proposal implemented according to an analysis developed in the KNIME platform." World Journal on Educational Technology: Current Issues 13, no. 4 (2021): 617–34. http://dx.doi.org/10.18844/wjet.v13i4.6233.

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The objective of the research is to develop a methodology to analyze a set of data extracted from a Learning Management System (LMS), in order to implement a Dashboard, which can be used by teachers to make timely and relevant decisions to improve the teaching-learning processes. The methodology used consisted of the analysis of 9,257 records extracted through simple random sampling from a population of 100,000 records. The indicators analyzed were: number of accesses, course grades, time spent, number of courses enrolled and number of activities developed. The results show the data analysis in the KNIME data mining analysis platform, the model was implemented in five phases: Requirements definition, model design, development, implementation and evaluation of results. The results are taken as a recommendation to design and implement a customized Dashboard for teachers to identify observable behavioral patterns that allow them to make decisions to improve the teaching-learning processes of students.
 Keywords: Analytics, dashboard, KNIME Learning, personalized, teaching.
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Maraza-Quispe, Benjamin, Ricardo Carlos Quispe-Figueroa, Manuel Alejandro Valderrama-Solis, and Benjamin Maraza-Quispe. "Dashboard proposal implemented according to an analysis developed on the KNIME platform." World Journal on Educational Technology: Current Issues 13, no. 4 (2021): 816–37. http://dx.doi.org/10.18844/wjet.v13i4.6267.

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The objective of the research is to develop a methodology to analyse a set of data extracted from a learning management system, in order to implement a dashboard, which can be used by teachers to make timely and relevant decisions to improve the teaching–learning processes. The methodology used consisted of analysing 9,257 records extracted through simple random sampling from a population of 100,000 records. The indicators analysed were number of accesses, course grades, time spent, number of courses enrolled and number of activities developed. The results show that the data analysis was carried out on the (o Konstanz Information Miner (KNIME) data mining analysis platform, and the model was implemented in five phases: requirements definition, model design, development, implementation and evaluation of results. The results are taken as a recommendation to design and implement a customised dashboard for teachers to identify observable behavioural patterns that allow them to make decisions to improve the teaching–learning processes of students.
 Keywords: Analytics, dashboard, KNIME Learning, personalised, teaching
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R, Chitra A., and Dr Arjun B. C. "Performance Analysis of Regression Algorithms for Used Car Price Prediction: KNIME Analytics Platform." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 1324–31. http://dx.doi.org/10.22214/ijraset.2023.49180.

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Abstract: In the recent years people’s willingness towards used car has increased. This has reflected in selling and buying of such cars. With the advance in technology online portal for marketing of used cars has come into effect. Many online portals focus to connect available used cars with user needs, present trends and various selection criteria. Using Machine Learning Algorithms such as Linear Regression, Tree Ensemble (Regression), Random forest (Regression), Gradient Boosted Tree(Regression), Simple Regression tree provided by KNIME Analytics Platform used car price predicted is performed. Analysis shows that Gradient Boosted Tree(Regression) prediction is closest to the target.
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Marlinda, Linda, Evita Fitri, Siti Nurhasanah Nugraha, Faruq Aziz, and Santoso Setiawan. "Decision Tree Algorithm to Measure Employee Performance Discipline." Sinkron 7, no. 4 (2022): 2223–30. http://dx.doi.org/10.33395/sinkron.v7i4.11796.

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Performance appraisal is done to measure the performance of an employee on the work done. The company conducts performance appraisals on employees at least every six months, involving all employees. This study uses the Absenteeism_at_work dataset. The purpose of this research is to analyze the performance of the Decision Tree algorithm in the classification process. Classification will be grouped into two, namely: disciplined and undisciplined The classification process will be carried out using K-Nime. Algorithm performance measurement using Knime Analytics Platform is open-source software for creating data science models. Knime builds data understanding and designs data science workflows and reusable components using accuracy, recall, and precision parameters. From the research conducted, the results of the Decision Tree algorithm have an accuracy rate of 94.6% while the label No. 5.4%. Based on the nineteen attributes proposed, it can be concluded that the Decision Tree algorithm has better performance.
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Books on the topic "KNIME Analytics Platform"

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Đokić, Kristian. Strojno učenje u ekStrojno učenje u ekonomiji i turizmu (s primjerima u programu KNIME Analytics Platform)onomiji i turizmu (s primjerima u programu KNIME Analytics Platform). Sveučilište Josipa Jurja Strossmayera u Osijeku, Fakultet turizma i ruralnog razvoja u Požegi, 2024. http://dx.doi.org/10.62598/ftrr.002.

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Ovaj priručnik pisan je za studente i stručnjake s polja ekonomije i turizma, ali i ostalih društvenih područja, koji žele naučiti kako primijeniti strojno učenje koristeći vizualno programiranje. Iako pozadinu strojnog učenja čine kompleksni matematički i statistički modeli, ovaj pristup omogućuje primjenu metoda, ne ulazeći dublje u njihovu matematičku pozadinu. S druge strane za primjenu ovih metoda, potrebno je osnovno poznavanje načina funkcioniranja, uvjeta, ograničenja i osnovnih principa, kako bi se odabrala odgovarajuća metoda i dobili realni rezultati. U priručniku je opisano sedam osnovnih metoda, a nakon svake je naveden primjer iz područja struke. U opisu pojedinih metoda, na apstraktnoj su razini objašnjeni temeljni principi funkcioniranja istih, dok su jednostavnije metode objašnjene primjenom matematičkih izraza te bi trebale biti razumljive i široj publici
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Melcher, Kathrin, and Rosaria Silipo. Codeless Deep Learning with KNIME: Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. Packt Publishing, Limited, 2020.

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Book chapters on the topic "KNIME Analytics Platform"

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Leonis, Georgios, Georgia Melagraki, and Antreas Afantitis. "Open Source Chemoinformatics Software including KNIME Analytics Platform." In Handbook of Computational Chemistry. Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-007-6169-8_57-2.

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Salem, Makki Ben, Philipp Niklas Rosenthal, and Abdelmajid Khelil. "Qualitative Comparison of Tools for Handling Unstructured IIoT Data." In ARENA2036. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88831-1_20.

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Abstract The Industrial Internet of Things (IIoT) generates vast amounts of data, often unstructured and heterogeneous. Processing and analyzing this data to gain insights and drive industrial automation requires sophisticated tools. This paper investigates the potential of various platforms for handling unstructured IIoT data, focusing on a qualitative comparative analysis of Node-RED, a visual programming tool, against established industrial solutions such as Apache NIFI, KNIME Analytics Platform, and Microsoft Azure Logic Apps. By evaluating these tools, we highlight their respective capabilities and limitations in managing unstructured data. Through this comparative analysis, we demonstrate the distinct features and performance of each tool and discuss their applicability in IIoT data management. The study shows that Node-RED is the most effective tool for handling unstructured IIoT data. Accordingly, we illustrate its use for a specific use case, i.e., value stream analysis.
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Zeferino, Emanuel Fernando, Khumbulani Mpofu, Olasumbo Makinde, and Boitumelo Ramatsetse. "Establishment of an Appropriate Data Analytic Platform for Developing a Wisdom Manufacturing System Using Decision Techniques." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_70.

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AbstractIn today’s global business context, data has played a critical role in ensuring accurate and appropriate decision making in manufacturing organisations. Despite the huge pool of information (i.e. data) generated by consumers, repair or maintenance shops, manufacturing job shop, scientific society on various products, which could be deployed by manufacturers in eliciting vital information towards achieving sustainable product design and development, only few manufacturers are making use of this data to generate wisdom required for sustainable manufacturing. This act is caused by lack of appropriate systems capable of integrating the available data and make wise inferences that will result in a competitive advantage of a specific organisation over its competitors. In light of this, the aim of this study is to establish a suitable data analytic platform that could be used to sort, classify and integrate data required to generate wisdom vital for sustainable manufacturing. In order to achieve this, Analytical Hierarchy Process (AHP) was deployed to appraise various alternative data analytical platforms such as Python, Apache Spark, Qlik View, Power BI, Tableau, KNIME, Excel, Talend, Rapid Miner and Statistical Analysis System (SAS) using various criteria such as Data Format, Availability, Interface, Programming Intensity, Data Science Knowledge Intensity and Capabilities. The result of this decision analysis and selection exercise, revealed that KNIME data analytic platform, with the most important decision criterion; data science knowledge intensity, and a cumulative assessment score of 80.80 is the appropriate data analytic platform that manufacturers should use to generate a knowledge advisor vital for sustainable manufacturing and product development.
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Zluga Claudio, Modre-Osprian Robert, Kastner Peter, and Schreier Günter. "Continual Screening of Patients Using mHealth: The Rolling Score Concept Applied to Sleep Medicine." In Studies in Health Technology and Informatics. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-645-3-237.

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Continual monitoring of patients utilizing mHealth-based telemonitoring applications are more and more used for individual management of patients. A new approach in risk assessment called Rolling Score Concept uses standardized questionnaires for continual scoring of individuals' health state through electronic patient reported outcome (ePRO). Using self-rated questionnaires and adding a specific Time Schedule to each question result in a movement of the questionnaires' scores over time, the Rolling Score. A text-processing pipeline was implemented with KNIME analytics platform to extract a Score Mapping Rule Set for three standardized screening questionnaires in the field of sleep medicine. A feasibility study was performed in 10 healthy volunteers equipped with a mHealth application on a smartphone and a sleep tracker. Results show that the proposed Rolling Score Concept is feasible and deviations of scores are in a reasonable range (< 7%), sustaining the new approach. However, further studies are required for verification. In addition, parameter quantification could avoid incorrect subjective evaluation by substitution of questions with sensor data.
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Conference papers on the topic "KNIME Analytics Platform"

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Delen, Dursun, Stefan Helfrich, and Rosaria Silipo. "KNIME Analytics Platform for Visual Data Science and Business Analytics Teaching." In SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education. ACM, 2021. http://dx.doi.org/10.1145/3408877.3439538.

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Franciska, I., and B. Swaminathan. "Churn prediction analysis using various clustering algorithms in KNIME analytics platform." In 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS). IEEE, 2017. http://dx.doi.org/10.1109/ssps.2017.8071585.

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Akhir, Emelia Akashah P., and Nasha Ayuni. "Predictive Analytics of Machine Failure using Linear Regression on KNIME Platform." In AIVR 2021: 2021 the 5th International Conference on Artificial Intelligence and Virtual Reality. ACM, 2021. http://dx.doi.org/10.1145/3480433.3480445.

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Omanovic, Samir, Admir Midzic, Zikrija Avdagic, Damir Pozderac, and Amel Toroman. "Missing Values Interpolation in PurpleAir Sensor Data based on a Correlation with Neighboring Locations using KNIME Analytics Platform." In 2023 46th MIPRO ICT and Electronics Convention (MIPRO). IEEE, 2023. http://dx.doi.org/10.23919/mipro57284.2023.10159808.

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