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Artículos de revistas sobre el tema "Data-driven techniques"

Autor: Grafiati

Publicado: 30 de noviembre de 2024

Última modificación: 5 de julio de 2025

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1

Kumar, Sandeep. "Enhancing Data Privacy in SAP Finance with Artificial Intelligence Driven Masking Techniques." International Journal of Science and Research (IJSR) 13, no. 5 (2024): 1819–24. http://dx.doi.org/10.21275/sr24518072929.

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Bossé, Michael J. "Data-Driven Mathematics Investigations on Curved Data." Mathematics Teacher 99, no. 1 (2005): 46–54. http://dx.doi.org/10.5951/mt.99.1.0046.

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Investigations of real–world data begin in elementary school. Students often produce scatter plots, leading to trend lines. In the middle grades, lines of best fit are often investigated through median–median lines and double–centroid lines (Shawer et al. 2002). In the secondary grades, linear regression is produced by the least squares line. While these techniques are adequate for data that is more or less linear, teachers and students often encounter data that produce a “curved” scatter plot. In these cases additional techniques are required. This article demonstrates three techniques to determine the equation of a polynomial function through two or more points that model the graph of “good fit” for a set of data. Using these techniques, students can develop functions through which they can evaluate mathematical behavior and make predictions. Secondary mathematics teachers will find these techniques particularly valuable. Each technique can be applied within various secondary mathematics courses such as algebra 2, statistics, or precalculus.

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3

Azkune, Gorka, Aitor Almeida, Diego López-de-Ipiña, and Liming Chen. "Extending knowledge-driven activity models through data-driven learning techniques." Expert Systems with Applications 42, no. 6 (2015): 3115–28. http://dx.doi.org/10.1016/j.eswa.2014.11.063.

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4

Zhong, Jinghui, Dongrui Li, Zhixing Huang, Chengyu Lu, and Wentong Cai. "Data-driven Crowd Modeling Techniques: A Survey." ACM Transactions on Modeling and Computer Simulation 32, no. 1 (2022): 1–33. http://dx.doi.org/10.1145/3481299.

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Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.

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5

Li, Tao, Ning Xie, Chunqiu Zeng, et al. "Data-Driven Techniques in Disaster Information Management." ACM Computing Surveys 50, no. 1 (2017): 1–45. http://dx.doi.org/10.1145/3017678.

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6

Arunkumar, R., and V. Jothiprakash. "Reservoir Evaporation Prediction Using Data-Driven Techniques." Journal of Hydrologic Engineering 18, no. 1 (2013): 40–49. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000597.

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7

Li, Tao, Chunqiu Zeng, Yexi Jiang, et al. "Data-Driven Techniques in Computing System Management." ACM Computing Surveys 50, no. 3 (2017): 1–43. http://dx.doi.org/10.1145/3092697.

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8

I., V. "Data Engineering: using Data Analysis Techniques in Producing Data Driven Products." International Journal of Computer Applications 161, no. 1 (2017): 13–16. http://dx.doi.org/10.5120/ijca2017912712.

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9

Meliboev, Azizjon. "ANALYZING HOTEL DATA-DRIVEN SYSTEM BY USING DATA SCIENCE TECHNIQUES." QO‘QON UNIVERSITETI XABARNOMASI 11 (June 30, 2024): 108–11. http://dx.doi.org/10.54613/ku.v11i11.971.

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In the past few years, both the City Hotel and Resort Hotel have experienced significant increases in their cancellation rates. As a result, both hotels are currently facing a range of challenges, such as reduced revenue and underutilized hotel rooms. Therefore, the top priority for both hotels is to reduce their cancellation rates, which will enhance their efficiency in generating revenue. This report focuses on the analysis of hotel booking cancellations and other factors that do not directly impact their business and annual revenue generation.

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10

Poonia, Ramesh Chandra, and Santosh R. Durugkar. "Sampling Techniques Used in Big-Data Driven Applications." Journal of Intelligent Systems and Computing 2, no. 1 (2021): 17–20. http://dx.doi.org/10.51682/jiscom.00201004.2021.

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Data-driven systems process the data from various sources in multiple applications. Data retrieved from heterogeneous sources need to be available in an aggregate and unique format. This requirement gives rise to the process of the Big-data and proposed next-generation big-data processing systems. There are many applications based on contextual data useful for identifying the traffic intensity, changing users per application, weather conditions etc., and serve as next- generation business-specific systems. In such systems data abstraction and representation are the important tasks & granularity can be applied in the data processing. Granularity will process the data from low granularity to high granularity. Sampling plays an important role in the data processing.

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11

Londhe, Shreenivas, and Gauri Panse-Aglave. "Modelling Stage–Discharge Relationship using Data-Driven Techniques." ISH Journal of Hydraulic Engineering 21, no. 2 (2015): 207–15. http://dx.doi.org/10.1080/09715010.2015.1007092.

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12

Garg, Vaibhav, and V. Jothiprakash. "Evaluation of reservoir sedimentation using data driven techniques." Applied Soft Computing 13, no. 8 (2013): 3567–81. http://dx.doi.org/10.1016/j.asoc.2013.04.019.

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13

Kouskoulis, George, Ioanna Spyropoulou, and Constantinos Antoniou. "Pedestrian simulation: Theoretical models vs. data driven techniques." International Journal of Transportation Science and Technology 7, no. 4 (2018): 241–53. http://dx.doi.org/10.1016/j.ijtst.2018.09.001.

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14

Arpasat, Poohridate, and Wichian Premchaiswadi. "Data-Driven Business Process Improvement." Progress in Applied Science and Technology 14, no. 3 (2024): 11–21. https://doi.org/10.60101/past.2024.256277.

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This research presents an analytical method to improve organizational workflow efficiency by utilizing data from the organization's information system, which was recorded as event logs from a hospital's outpatient department. Through the application of Process Mining techniques using the Disco tool and Fuzzy Miner algorithm, we created a process model for efficiency analysis. The research results demonstrated the effectiveness of the proposed method in analyzing outpatient service processes involving 12,836 patients, which revealed 4,293 distinct process variants. This diversity reflects the complexity of medical service delivery. Through frequency and time analysis, our research demonstrates how organizations can optimize resource allocation, establish SLAs, and develop effective staff training plans. The study confirms that Process Mining techniques provide accurate and effective means for improving work processes through the analysis of existing organizational data.

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15

Li, Yuliang, Xiaolan Wang, Zhengjie Miao, and Wang-Chiew Tan. "Data augmentation for ML-driven data preparation and integration." Proceedings of the VLDB Endowment 14, no. 12 (2021): 3182–85. http://dx.doi.org/10.14778/3476311.3476403.

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In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to surveying task-specific DA operators that leverage rules, transformations, and external knowledge for creating additional training data, we also explore the advanced DA techniques such as interpolation, conditional generation, and DA policy learning. Finally, we describe the connection between DA and other machine learning paradigms such as active learning, pre-training, and weakly-supervised learning. We hope that this discussion can shed light on future research directions for a holistic data augmentation framework for high-quality dataset creation.

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16

Dethlefs, Nina. "Context-Sensitive Natural Language Generation: From Knowledge-Driven to Data-Driven Techniques." Language and Linguistics Compass 8, no. 3 (2014): 99–115. http://dx.doi.org/10.1111/lnc3.12067.

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17

Dong, Yachao, Ting Yang, Yafeng Xing, Jian Du, and Qingwei Meng. "Data-Driven Modeling Methods and Techniques for Pharmaceutical Processes." Processes 11, no. 7 (2023): 2096. http://dx.doi.org/10.3390/pr11072096.

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As one of the most influential industries in public health and the global economy, the pharmaceutical industry is facing multiple challenges in drug research, development and manufacturing. With recent developments in artificial intelligence and machine learning, data-driven modeling methods and techniques have enabled fast and accurate modeling for drug molecular design, retrosynthetic analysis, chemical reaction outcome prediction, manufacturing process optimization, and many other aspects in the pharmaceutical industry. This article provides a review of data-driven methods applied in pharmaceutical processes, based on the mathematical and algorithmic principles behind the modeling methods. Different statistical tools, such as multivariate tools, Bayesian inferences, and machine learning approaches, i.e., unsupervised learning, supervised learning (including deep learning) and reinforcement learning, are presented. Various applications in the pharmaceutical processes, as well as the connections from statistics and machine learning methods, are discussed in the narrative procedures of introducing different types of data-driven models. Afterwards, two case studies, including dynamic reaction data modeling and catalyst-kinetics prediction of cross-coupling reactions, are presented to illustrate the power and advantages of different data-driven models. We also discussed current challenges and future perspectives of data-driven modeling methods, emphasizing the integration of data-driven and mechanistic models, as well as multi-scale modeling.

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18

Carpenter, Chris. "Machine-Learning Techniques Assist Data-Driven Well-Performance Optimization." Journal of Petroleum Technology 73, no. 10 (2021): 63–64. http://dx.doi.org/10.2118/1021-0063-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201696, “Robust Data-Driven Well-Performance Optimization Assisted by Machine-Learning Techniques for Natural-Flowing and Gas-Lift Wells in Abu Dhabi,” by Iman Al Selaiti, Carlos Mata, SPE, and Luigi Saputelli, SPE, ADNOC, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. Despite being proven to be a cost-effective surveillance initiative, remote monitoring is still not adopted in more than 60% of oil and gas fields around the world. Understanding the value of data through machine-learning (ML) techniques is the basis for establishing a robust surveillance strategy. In the complete paper, the authors develop a data-driven approach, enabled by artificial-intelligence methodologies including ML, to find an optimal operating envelope for gas-lift wells. Real-Time Well-Performance Optimization Wellsite Measurement and Control. - Flow Tests. - Past tests include sporadic measurement of multiphase rates and the associated flowing pressure and temperature, collected at various points of the production system, from bottomhole to separator conditions. Flow tests are also known as well tests; however, the authors use the term “flow test” in this paper to avoid confusion with well testing as used in pressure transient tests, including temporary shut-in pressure buildups (for producers) and pressure falloff tests (for injectors). Normally, a well would have limited data points from the past well tests (i.e., less than 50 valid flow tests in a period of 5–10 years). This data is the basis of creating ML models. Continuous Monitoring. - Every well should have adequate instrumentation, and its supporting infrastructure should include reliable power supply, minimum latency telemetry, and desktop access to production parameters. Alignment among real-time data and relational databases is required. Remote Control and Automated Actuation. - In addition to controllable valves, every well should be enabled with actuators to control the process variables. Remote control allows the operator to make changes to the current well-site configuration. Regulatory and Supervisory Control. - The value of automated closed-loop regulatory and supervisory control is to sustain optimal production while providing high well availability. Real-Time Production Optimization. - Continuous production optimization means that expected performance is challenged frequently by updating an optimal forecast with upper-level targets and current asset status. This is achieved by applying actions that close the gap between actual and expected performance. Faster surveillance loops compare actual vs. expected performance to determine minute, hourly, and daily gaps. A slower surveillance loop updates the asset’s expected performance. Well-Management Guidelines. - These are established, known limits to address and honor restrictions such as concession-contract obligations and legal limits, optimal reservoir management, flow assurance, economics, safety, and integrity.

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19

Ferreira, Anselmo, Luca Bondi, Luca Baroffio, et al. "Data-Driven Feature Characterization Techniques for Laser Printer Attribution." IEEE Transactions on Information Forensics and Security 12, no. 8 (2017): 1860–73. http://dx.doi.org/10.1109/tifs.2017.2692722.

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20

Üneş, Fatih, Mustafa Demirci, Bestami Taşar, Yunus Kaya, and Hakan Varçin. "Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques." Polish Journal of Environmental Studies 28, no. 5 (2019): 3451–62. http://dx.doi.org/10.15244/pjoes/93923.

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21

Vehmas, Risto, Juha Jylha, Minna Vaila, Juho Vihonen, and Ari Visa. "Data-Driven Motion Compensation Techniques for Noncooperative ISAR Imaging." IEEE Transactions on Aerospace and Electronic Systems 54, no. 1 (2018): 295–314. http://dx.doi.org/10.1109/taes.2017.2756518.

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22

Bertolissi, Edy, Mauro Birattari, Gianluca Bontempi, Antoine Duchâteau, and Hugues Bersini. "Data-Driven Techniques for Divide and Conquer Adaptive Control." IFAC Proceedings Volumes 33, no. 16 (2000): 59–64. http://dx.doi.org/10.1016/s1474-6670(17)39603-9.

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23

Matwin, Stan, Luca Tesei, and Roberto Trasarti. "Computational modelling and data-driven techniques for systems analysis." Journal of Intelligent Information Systems 52, no. 3 (2019): 473–75. http://dx.doi.org/10.1007/s10844-019-00554-z.

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24

Hardy, Hilda, Alan Biermann, R. Bryce Inouye, et al. "The Amitiés system: Data-driven techniques for automated dialogue." Speech Communication 48, no. 3-4 (2006): 354–73. http://dx.doi.org/10.1016/j.specom.2005.07.006.

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25

Kaur, Harpreet, and V. Jothiprakash. "Daily precipitation mapping and forecasting using data driven techniques." International Journal of Hydrology Science and Technology 3, no. 4 (2013): 364. http://dx.doi.org/10.1504/ijhst.2013.060337.

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26

Kisi, Ozgur, Alireza Moghaddam Nia, Mohsen Ghafari Gosheh, Mohammad Reza Jamalizadeh Tajabadi, and Azadeh Ahmadi. "Intermittent Streamflow Forecasting by Using Several Data Driven Techniques." Water Resources Management 26, no. 2 (2011): 457–74. http://dx.doi.org/10.1007/s11269-011-9926-7.

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27

Velasco, D., L. Guzman, B. Puruncajas, C. Tutiven, and Y. Vidal. "Wind turbine blade damage detection using data-driven techniques." Renewable Energy and Power Quality Journal 21, no. 1 (2023): 462–66. http://dx.doi.org/10.24084/repqj21.357.

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This work presents a simple damage detection strategy for wind turbine blades. In particular, a vibration analysis-based damage detection methodology is proposed that requires only healthy data and detects damage in different locations of the blade. The stated structural health monitoring strategy is based on the extraction of characteristics using statistical metrics as a technique for the recognition and differentiation of healthy test experiments from damaged test experiments with simulated faults created by added mass. In this manner, several metrics are approached to find those that show better classification in processing the data provided by the sensors. Finally, an evaluation process is performed to detect blade damage. The results show that the proposed RMSE metric performs at an ideal level, making it a promising strategy for the detection of blade damage.

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28

Tarika, Verma, and S. Gill Nasib. "Machine Learning Techniques for Better Data Driven Decisions Revisited." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 460–64. https://doi.org/10.35940/ijeat.D6766.049420.

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The main goal of machine learning is to accurately predict the decisions to the problems without human expert intervention. These decisions depend upon patterns found and facts learnt during training tenure. However, prior incorporation of human knowledge is necessary for better prediction of the test data. The main aim is to make machines self-reliant for decision making. Providing machine with this vision makes it useful in every modern field. This makes the stepping stone to make computers behave as the humans do. Enhancing its speed and accuracy are the next step in this field. This paper presents a stock of techniques used to train the machines to respond to patterns present in the data sets so that useful information may be extracted for its potential use.

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29

Mendez, Gonzalo, Xavier Ochoa, Katherine Chiluiza, and Bram De Wever. "Curricular Design Analysis: A Data-Driven Perspective." Journal of Learning Analytics 1, no. 3 (2014): 84–119. http://dx.doi.org/10.18608/jla.2014.13.6.

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Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of Learning Analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, course impact on the overall academic performance of students, curriculum coherence, dropout paths and load/performance graph. The usefulness of these techniques is validated through their application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.

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T., Aditya Sai Srinivas, Sravanthi Y., Vinod Kumar Y., and Dwaraka Srihith I.V. "Data Standardization: Key to Effective Data Integration." Advanced Innovations in Computer Programming Languages 6, no. 1 (2023): 1–4. https://doi.org/10.5281/zenodo.10060920.

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<i>Data standardization is a critical step in data preprocessing and analysis. This process involves transforming data to have a consistent scale, enabling meaningful comparisons and effective modeling. In this digital age, where data fuels decision-making across industries, understanding and implementing data standardization techniques is essential. This abstract introduces the concept of data standardization, emphasizing its importance in enhancing data quality, supporting data integration efforts, and facilitating data-driven decision-making. We explore various methods and tools for standardizing data in Python, a widely used programming language for data analysis and machine learning. By mastering data standardization, organizations can unlock the full potential of their data, ensuring accuracy, reliability, and compatibility in an increasingly data-driven world.</i> 

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31

Yu, Hong, and Mark Riedl. "Data-Driven Personalized Drama Management." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 1 (2021): 191–97. http://dx.doi.org/10.1609/aiide.v9i1.12665.

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A drama manager is an omniscient background agent responsible for guiding players through the story space and delivering an enjoyable and coherent experience. Most previous drama managers only consider the designer's intent. We present a drama manager that uses data-driven techniques to model players and provides personalized guidance in the story space without removing player agency. In order to guide players' experiences, our drama manager manipulates the story space to maximize the probability of the players making choices intended by the drama manager. Our system is evaluated on an interactive storytelling game. Results show that our drama manager can significantly increase the likelihood of the drama manager's desired story continuation.

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32

Venkataramana, Jaladurgam. "LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS." ICTACT Journal on Soft Computing 15, no. 2 (2024): 3515–22. http://dx.doi.org/10.21917/ijsc.2024.0490.

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The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.

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Pandey, P. K., Topi Nyori, and Vanita Pandey. "Estimation of reference evapotranspiration using data driven techniques under limited data conditions." Modeling Earth Systems and Environment 3, no. 4 (2017): 1449–61. http://dx.doi.org/10.1007/s40808-017-0367-z.

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Haland, Christoffer, and Anders Granmo. "Machine Learning for Anomaly Detection: Insights into Data-Driven Applications." International journal of data science and machine learning 05, no. 01 (2025): 36–41. https://doi.org/10.55640/ijdsml-05-01-07.

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Anomaly detection plays a pivotal role in data-driven machine learning applications, enabling the identification of rare or unexpected patterns that deviate from the norm. These anomalies, which can indicate critical events such as fraud, security breaches, equipment failures, or medical conditions, are invaluable in a variety of fields. This paper provides an in-depth review of anomaly analytics, focusing on the various techniques used in machine learning to detect anomalies in complex, high-dimensional data. We explore statistical methods, machine learning-based approaches, and hybrid models, analyzing their strengths and weaknesses across multiple domains including cybersecurity, finance, healthcare, and manufacturing. The paper also discusses key evaluation metrics for anomaly detection and highlights the challenges of scalability, noise handling, and model interpretability. Finally, we examine emerging trends in anomaly detection, including real-time processing and explainability, and suggest future research directions to improve the robustness and efficiency of anomaly detection systems in large-scale, dynamic environments. This work serves as a comprehensive guide for understanding the role of anomaly analytics in modern machine learning applications, offering insights into current methodologies and future advancements.

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35

Hossain, Qaium, Fahmida Yasmin, Tapos Ranjan Biswas, and Nurtaz Begum Asha. "Data-Driven Business Strategies: A Comparative Analysis of Data Science Techniques in Decision-Making." Scholars Journal of Economics, Business and Management 11, no. 09 (2024): 257–63. http://dx.doi.org/10.36347/sjebm.2024.v11i09.002.

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In an era characterized by rapid technological advancements and an explosion of data, businesses are increasingly turning to data-driven strategies to gain a competitive edge. Understanding the effectiveness of such strategies is paramount. This study investigates the impact of data-driven decision-making on business performance in the context of a diverse set of industries. The primary objective of this research is to assess the extent to which data-driven strategies influence business performance. Specifically, we aim to quantify the correlation between the adoption of data-driven approaches and key performance indicators (KPIs) such as revenue growth, cost reduction, and customer satisfaction. A comprehensive mixed-methods approach was employed. Qualitative data was collected through interviews with executives from 15 companies across different sectors. Quantitative data was obtained through surveys distributed to 25 organizations. Statistical analysis, including correlation and regression analysis, was conducted to identify patterns and relationships. Our analysis reveals a strong positive correlation between the adoption of data-driven strategies and business performance metrics. On average, companies that embraced data-driven decision-making experienced a 20% increase in revenue, a 15% reduction in operational costs, and a 10% improvement in customer satisfaction compared to those that did not. This study underscores the transformative potential of data-driven strategies in contemporary business environments. Organizations that leverage data effectively not only enhance their financial performance but also better meet customer expectations. We conclude that data-driven decision-making is no longer a luxury but a strategic imperative for businesses looking to thrive in the digital age.

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36

Omoruyi, Nosakhare. "Advanced computational methods for financial planning and analysis risk assessment using data science-driven model validation techniques." International Journal of Research Publication and Reviews 6, no. 4 (2025): 3904–18. https://doi.org/10.55248/gengpi.6.0425.1449.

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Beckley, Jessica. "Advanced Risk Assessment Techniques: Merging Data-Driven Analytics with Expert Insights to Navigate Uncertain Decision-Making Processes." International Journal of Research Publication and Reviews 6, no. 3 (2025): 1454–71. https://doi.org/10.55248/gengpi.6.0325.1148.

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38

Bala, Vignesh Charllo. "Efficient Data Harmonization in Distributed Systems: A Scalable Approach for Multi-Site Analytics." Journal of Scientific and Engineering Research 5, no. 7 (2018): 443–49. https://doi.org/10.5281/zenodo.13752722.

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Data harmonization in distributed systems, particularly within distributed environments, presents considerable challenges due to the heterogeneity and geographical dispersion of data sources. This research introduces a scalable and efficient framework designed to integrate, standardize, and cleanse data across diverse sources, thereby ensuring consistency, accuracy, and reliability. The framework employs a multi-phase process that includes advanced data integration and cleaning techniques, combined with the power of distributed computing and parallel processing to efficiently handle large-scale datasets. In a practical simulation involving a multi-site manufacturing operation, the application of this framework led to a significant reduction in data integration errors and a notable improvement in processing speed. These enhancements resulted in more reliable analytics, facilitating better-informed decision-making within the manufacturing process. The study not only underscores the framework's practical value in the manufacturing sector but also highlights its adaptability across various industries dealing with distributed data sources. This research offers a robust foundation for future studies, with the potential to significantly impact the efficiency and effectiveness of data-driven operations across different sectors.

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Akgülgil Mutlu, Nadide Gizem. "The future of film-making: Data-driven movie-making techniques." Global Journal of Arts Education 10, no. 2 (2020): 167–74. http://dx.doi.org/10.18844/gjae.v10i2.4735.

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Since the term ‘big data’ came to the scene, it has left almost no industry unaffected. Even the art world has taken advantage of the benefits of big data. One of the latest art forms, cinema, eventually started using analytics to predict their audience and their tastes through data mining. In addition to online platforms like Netflix, Amazon Prime and many more, which act on a different basis, the industry itself evolved to a new phase that uses AI in pre-production, production, post-production and distribution phases. This paper researches software, such as Cinelytic, ScriptBook and LargoAI, and their working strategies to understand the role of directors and producers in the age of the digital era in film-making. The research aims to find answers to the capabilities of data-driven movie-making techniques and, accordingly, it makes a number of predictions about the role of human beings in the production of an artwork and analyses the role of the software. The research also investigates the pros and cons of using big data in the film-making industry. Keywords: Artificial intelligence, cinema, data mining, film-making.

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Deb, C., and A. Schlueter. "Review of data-driven energy modelling techniques for building retrofit." Renewable and Sustainable Energy Reviews 144 (July 2021): 110990. http://dx.doi.org/10.1016/j.rser.2021.110990.

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Wan Qi, Woo, Ng Lik Yin, Umaganeswaran Sivaneswaran, and Nishanth G. Chemmangattuvalappil. "A Novel Methodology for Molecular Design via Data Driven Techniques." Journal of Physical Science 28, Suppl. 1 (2017): 1–24. http://dx.doi.org/10.21315/jps2017.28.s1.1.

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Kubiak, Patrick, and Stefan Rass. "An Overview of Data-Driven Techniques for IT-Service-Management." IEEE Access 6 (2018): 63664–88. http://dx.doi.org/10.1109/access.2018.2875975.

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Reddy Kethireddy, Rajashekhar. "AI-Driven Encryption Techniques for Data Security in Cloud Computing." JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING 9, no. 1 (2021): 27–38. http://dx.doi.org/10.70589/jrtcse.2021.1.3.

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Londhe, Shreenivas, and Shrikant Charhate. "Comparison of data-driven modelling techniques for river flow forecasting." Hydrological Sciences Journal 55, no. 7 (2010): 1163–74. http://dx.doi.org/10.1080/02626667.2010.512867.

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45

Ylioinas, Juha, Norman Poh, Jukka Holappa, and Matti Pietikäinen. "Data-driven techniques for smoothing histograms of local binary patterns." Pattern Recognition 60 (December 2016): 734–47. http://dx.doi.org/10.1016/j.patcog.2016.06.029.

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46

Kazemi, Pezhman, Christophe Bengoa, Jean-Philippe Steyer, and Jaume Giralt. "Data-driven techniques for fault detection in anaerobic digestion process." Process Safety and Environmental Protection 146 (February 2021): 905–15. http://dx.doi.org/10.1016/j.psep.2020.12.016.

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Shirmohammadi, Bagher, Mehdi Vafakhah, Vahid Moosavi, and Alireza Moghaddamnia. "Application of Several Data-Driven Techniques for Predicting Groundwater Level." Water Resources Management 27, no. 2 (2012): 419–32. http://dx.doi.org/10.1007/s11269-012-0194-y.

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Fu, Daixin, Lingyi Wang, Guanlin Lv, Zhengyu Shen, Hao Zhu, and W. D. Zhu. "Advances in dynamic load identification based on data-driven techniques." Engineering Applications of Artificial Intelligence 126 (November 2023): 106871. http://dx.doi.org/10.1016/j.engappai.2023.106871.

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Gaborit, Mathieu, and Luc Jaouen. "Using data-driven techniques to provide feedback during material characterisation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 5 (2023): 2305–9. http://dx.doi.org/10.3397/in_2022_0330.

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The aim of the work is to study the feasibility of using machine learning techniques to design a decision helper to assist the characterisation of acoustic materials (porous media for instance). The tool is intended to alert the human operator about specific physical phenomena occurring during the measurements or common mistakes in handling the characterization rig or its parameters. Examples of classical issues include leakage around the samples, unintentional compression during the sample mounting, errors in input parameters such as the static pressure or temperature, etc. The proposed helper relies on a physical analysis and a k-nearest neighbours classifier using the Fréchet distance to score the measurements. This approach allows to measure the similarity between curves, independently from sampling. The training phase is performed on a labelled dataset created from actual impedance tube measurements and possibly some computer generated results to bridge gaps. The inputs are frequency-dependent quantities including normal sound absorption curves, surface impedance, dynamic mass density and dynamic bulk modulus.

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Myakala, Praveen Kumar, Anil Kumar Jonnalagadda, and Prudhvi Naayini. "Revolutionizing Big Data with AI-Driven Hybrid Soft Computing Techniques." Machine Learning and Applications: An International Journal 12, no. 1 (2025): 01–13. https://doi.org/10.5121/mlaij.2025.12101.

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The ever-growing complexity and scale of Big Data have rendered traditional computational approaches insufficient, driving the need for innovative AI-driven solutions. This paper presents an advanced framework that integrates artificial intelligence (AI) and machine learning (ML) with hybrid soft computing techniques, including fuzzy logic, deep neural networks, evolutionary algorithms, and swarm intelligence. These methods collectively address challenges such as high dimensionality, uncertainty, realtime processing, and scalability, thereby achieving enhanced accuracy, interpretability, and adaptability. Cutting-edge strategies, such as adaptive neuro-fuzzy systems and deep neuro-evolution, enable transformative improvements across diverse domains, including healthcare, IoT, and social media. Experimental evaluations on real-world datasets demonstrate significant advancements, including up to 20% faster processing speeds and a 15% improvement in predictive accuracy compared to traditional method s. This research underscores the pivotal role of AI-augmented soft computing in shaping the future of Big Data analytics, offering robust and scalable solutions to meet evolving industrial demands. Furthermore, it lays the foundation for developing next-generation systems capable of addressing emerging challenges in data-driven decision-making across industries.

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