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Journal articles on the topic 'Data driven performance'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Cesme, Burak, Stephanie Dock, Ryan Westrom, Kevin Lee, and Jorge Andres Barrios. "Data-Driven Urban Performance Measures." Transportation Research Record: Journal of the Transportation Research Board 2605, no. 1 (2017): 45–53. http://dx.doi.org/10.3141/2605-04.

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Performance measures typically are used by transportation agencies to measure progress toward organizational goals. As cities have reoriented their transportation priorities toward people instead of cars and have put more emphasis on multimodal transportation options, relatively few studies have identified measures that capture the urban context and are sensitive to the multimodal nature of urban transportation systems. Moreover, some studies have focused only on the measures without fully considering the available resources needed to capture these measures and the limitations in data. This la

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2

Ake, Anuoluwa. "Enhancing US Energy Sector Performance Through Advanced Data-Driven Analytical Frameworks." International Journal of Research Publication and Reviews 5, no. 12 (2024): 3336–56. https://doi.org/10.55248/gengpi.5.1224.250111.

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Singh, Arjun. "The HR Data Landscape: Transforming HR with Data-Driven Insights." Engineering and Applied Sciences Journal 2, no. 1 (2025): 01–02. https://doi.org/10.64030/3067-8005.02.01.06.

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In today’s rapidly evolving corporate world, the role of data in shaping HR practices and decisions cannot be overstated. Human Resources (HR) departments in large corporations are increasingly relying on data-driven approaches to drive their strategies, improve existing processes, and motivate employees to succeed in their roles. Tracking HR data allows organizations to gain valuable insights into employee feedback, performance, and engagement, ultimately leading to a healthier and more productive workplace.

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Bilen, Umran, and Sebnem Helvacioglu. "DATA DRIVEN PERFORMANCE EVALUATION IN SHIPBUILDING." Brodogradnja 71, no. 4 (2020): 39–51. http://dx.doi.org/10.21278/brod71403.

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Rapid development in data science keeps paving the way for use of data for many purposes in shipbuilding, both for product development and production, such as Industry 4.0 have been developing many industries. Similar to other industries the evaluation of performance in shipbuilding is the key to success which is closely connected to productivity and lowered costs. Data mining and analysis techniques are used to create effective algorithms to evaluate the performance, also by means of cost estimation based on parametric methods. However, it is usually not very clear how data are collected, org

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Dang, Ngoc Trung, and Phuong Nam Dao. "Data-Driven Reinforcement Learning Control for Quadrotor Systems." International Journal of Mechanical Engineering and Robotics Research 13, no. 5 (2024): 495–501. http://dx.doi.org/10.18178/ijmerr.13.5.495-501.

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This paper aims to solve the tracking problem and optimality effectiveness of an Unmanned Aerial Vehicle (UAV) by model-free data Reinforcement Learning (RL) algorithms in both sub-systems of attitude and position. First, a cascade UAV model structure is given to establish the control system diagram with two corresponding attitude and position control loops. Second, based on the computation of the time derivative of the Bellman function by two different methods, the combination of the Bellman function and the optimal control is adopted to maintain the control signal as time converges to infini

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Swapna, Nadakuditi, Agrawal Shobhit, and Kumar Bhargava. "Data Analytics and Business Analysis: How Business Analysts Can Drive Data-Driven Decision-Making in Organizations." European Journal of Advances in Engineering and Technology 8, no. 1 (2021): 71–75. https://doi.org/10.5281/zenodo.12737472.

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With the rise of Information systems there is an explosion of data produced every minute in the organizations across various sectors. The systems help gather data, analyze complex information, and help manage outcomes and decrease costs, thereby improving the customer experience. Healthcare data is complex with much unstructured data like clinical notes and images. With the increased use of electronic health data and growing needs for information sharing across domains, good understanding of the systems including integrations and data both structured and unstructured is crucial for effective d

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Siva, Sankar Das. "Optimizing Employee Performance through Data-Driven Management Practices." European Journal of Advances in Engineering and Technology 7, no. 1 (2020): 76–81. https://doi.org/10.5281/zenodo.15606857.

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This research focused on the use of data and analytics in employee performance, which is a major trend in helping organizations improve their performance. This research examines the practical way based on which the data systems are set up, their positive impact on team members, and the ethical problems that arise. In practice, relying on data analysis tools to make better decisions requires based on that must ethical approach to be used. The team is ready for digital approaches, and the organization’s culture matches the goals. The research helps to clarify that approaches based on data

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Rashid, Umair, Muhammad Asim Abbasi, Abdul Qayyum Khan, Muhammad Irfan, Muhammad Abid, and Grzegorz Nowakowski. "Robust Data-Driven Design for Fault Diagnosis of Industrial Drives." Electronics 11, no. 23 (2022): 3858. http://dx.doi.org/10.3390/electronics11233858.

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Due to the presence of actuator disturbances and sensor noise, increased false alarm rate and decreased fault detection rate in fault diagnosis systems have become major concerns. Various performance indexes are proposed to deal with such problems with certain limitations. This paper proposes a robust performance-index based fault diagnosis methodology using input–output data. That data is used to construct robust parity space using the subspace identification method and proposed performance index. Generated residual shows enhanced sensitivity towards faults and robustness against unknown dist

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9

Hengki, Tamando Sihotang, Efendi Syahril, Zarlis Muhammad, and Mawengkang Herman. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497~1504. https://doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical e

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10

Badhan, Istiaque Ahmed, Moohtasim Haque Neeroj, and Irfan Chowdhury. "THE EFFECT OF AI-DRIVEN INVENTORY MANAGEMENT SYSTEMS ON HEALTHCARE OUTCOMES AND SUPPLY CHAIN PERFORMANCE: A DATA-DRIVEN ANALYSIS." Frontline Marketing, Management and Economics Journal 4, no. 11 (2024): 15–52. http://dx.doi.org/10.37547/marketing-fmmej-04-11-03.

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There has been much focus toward increasing integration of AI in healthcare generally, with a specific focus on inventory management systems in particular. As more hospitals in the United States public and private side face increasing costs and concerns over cost and length of operation all systems supporting inventory must be made most efficient and AI system offers the US hospitals the opportunity to maintain accurate inventory and hence improve the quality of patient care. But the literature review presents a small number of studies that focus on the effects of these systems as the means to

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11

Nagaveni B Biradar, Santhamma. "Performance Monitoring in Virtual Organization Using Domain Driven Data Mining and Opinion Mining." International Journal of Scientific Engineering and Research 2, no. 6 (2014): 10–14. https://doi.org/10.70729/2061402.

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12

Boto, Fernando, Maialen Murua, Teresa Gutierrez, Sara Casado, Ana Carrillo, and Asier Arteaga. "Data Driven Performance Prediction in Steel Making." Metals 12, no. 2 (2022): 172. http://dx.doi.org/10.3390/met12020172.

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This work presents three data-driven models based on process data, to estimate different indicators related to process performance in a steel production process. The generated models allow the optimization of the process parameters to achieve optimal performance and quality levels. A new approach based on ensembles has been developed with feature selection methods and four state-of-the-art regression approximations (random forest, gradient boosting, xgboost and neural networks). The results show that the proposed approach makes the prediction more stable reducing the variance for all cases, ev

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Pugna, Irina Bogdana, Adriana Dutescu, and Georgiana Oana Stanila. "Performance management in the data-driven oragnisation." Proceedings of the International Conference on Business Excellence 12, no. 1 (2018): 816–28. http://dx.doi.org/10.2478/picbe-2018-0073.

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Abstract This paper investigates the challenges raised by the “datafication” of the business environment and its role in reshaping future managerial behavior. These challenges arise specifically from new drivers of performance improvement and strategic development, such as cloud computing, big data, and data analytics. We analyze the factors that significantly change the potential influence that information and information asymmetries (“insight”) - resulting from analyzing huge volumes of data - have on organizational competitive advantage. This paper develops a framework to strengthen the val

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14

Matheou, George, and Paraskevas Evripidou. "Data-Driven Concurrency for High Performance Computing." ACM Transactions on Architecture and Code Optimization 14, no. 4 (2017): 1–26. http://dx.doi.org/10.1145/3162014.

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15

Zumoffen, David, Lautaro Braccia, and Patricio Luppi. "Data-Driven Plant-Wide Control Performance Monitoring." Industrial & Engineering Chemistry Research 58, no. 16 (2019): 6576–91. http://dx.doi.org/10.1021/acs.iecr.8b06293.

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16

Nikula, Riku-Pekka, Mika Ruusunen, and Kauko Leiviskä. "Data-driven framework for boiler performance monitoring." Applied Energy 183 (December 2016): 1374–88. http://dx.doi.org/10.1016/j.apenergy.2016.09.072.

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17

Hu, Guang-Zhong, Xin-Jian Xu, Shou-Ne Xiao, Guang-Wu Yang, and Fan Pu. "Product Data Model for Performance-driven Design." Chinese Journal of Mechanical Engineering 30, no. 5 (2017): 1112–22. http://dx.doi.org/10.1007/s10033-017-0173-6.

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18

Caicedo, David, and Ashish Pandharipande. "Sensor Data-Driven Lighting Energy Performance Prediction." IEEE Sensors Journal 16, no. 16 (2016): 6397–405. http://dx.doi.org/10.1109/jsen.2016.2579663.

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19

Oluwatosin Abdul-Azeez, Alexsandra Ogadimma Ihechere, and Courage Idemudia. "Enhancing business performance: The role of data-driven analytics in strategic decision-making." International Journal of Management & Entrepreneurship Research 6, no. 7 (2024): 2066–81. http://dx.doi.org/10.51594/ijmer.v6i7.1257.

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In today’s highly competitive business landscape, organizations are increasingly turning to data-driven analytics to enhance performance and inform strategic decision-making. This approach leverages vast amounts of data, transforming it into actionable insights that drive efficiency, innovation, and growth. The role of data-driven analytics is multifaceted, encompassing predictive, prescriptive, and descriptive analytics, each contributing uniquely to the decision-making process. Predictive analytics forecasts future trends and behaviors, enabling proactive strategies. Prescriptive analytics p

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20

Yang, Cheng, Jia-Rui Lin, Ke-Xiao Yan, Yi-Chuan Deng, Zhen-Zhong Hu, and Cheng Liu. "Data-Driven Quantitative Performance Evaluation of Construction Supervisors." Buildings 13, no. 5 (2023): 1264. http://dx.doi.org/10.3390/buildings13051264.

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The performances of construction supervisors are essential for the monitoring, control, and coordination of the construction process of a project in order to adhere to a predefined schedule, cost, quality and other factors. However, it is challenging to evaluate their performance due to limitations such as data deficiency, human error, etc. Thus, this paper proposes an approach to data-driven quantitative performance evaluation of construction supervisors by integrating an analytic hierarchy process (AHP) and activity tracking. The proposed approach contains three parts, namely, index extracti

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21

Sihotang, Hengki Tamando, Syahril Efendi, Muhammad Zarlis, and Herman Mawengkang. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497–504. http://dx.doi.org/10.11591/eei.v11i3.3660.

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Abstract:

Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical examp

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22

Hassoubah, Mohammed, and Ganesh Sistu. "Data Driven 3D-Lane Detection Using Parallelism Loss Function." Journal of Image and Graphics 12, no. 1 (2024): 16–22. http://dx.doi.org/10.18178/joig.12.1.16-22.

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Accurate lane position prediction is crucial in autonomous driving for safe vehicle maneuvering. Monocular cameras, aided by AI advancements, have proven to be effective in this task. However, 2D image space predictions overlook lane height, causing poor results in uphill or downhill scenarios that affect action judgments, such as in the planning and control module. Previous 3D-lane detection approaches relied solely on applying Inverse Perspective Mapping (IPM) on the encoded camera feature map, which may not be ordered according to the perspective principle leading to sub-optimal prediction

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23

Okunuga, Aishat. "Improving healthcare financial performance through data-driven forecasting, cost modeling, and reimbursement optimization tools." International Journal of Research Publication and Reviews 6, no. 4 (2025): 331–54. https://doi.org/10.55248/gengpi.6.0425.1673.

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24

Martin, Charles Patrick, and Jim Torresen. "Data-Driven Analysis of Tiny Touchscreen Performance with MicroJam." Computer Music Journal 43, no. 4 (2020): 41–57. http://dx.doi.org/10.1162/comj_a_00536.

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The widespread adoption of mobile devices, such as smartphones and tablets, has made touchscreens a common interface for musical performance. Although new mobile music instruments have been investigated from design and user experience perspectives, there has been little examination of the performers' musical output. In this work, we introduce a constrained touchscreen performance app, MicroJam, designed to enable collaboration between performers, and engage in a data-driven analysis of more than 1,600 performances using the app. MicroJam constrains performances to five seconds, and emphasizes

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25

Frery, Alejandro, Heitor S. Ramos, José Alencar-Neto, Eduardo Nakamura, and Antonio A. F. Loureiro. "Data Driven Performance Evaluation of Wireless Sensor Networks." Sensors 10, no. 3 (2010): 2150–68. http://dx.doi.org/10.3390/s100302150.

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26

Freestone, Dean R., Kelvin J. Layton, Levin Kuhlmann, and Mark J. Cook. "Statistical Performance Analysis of Data-Driven Neural Models." International Journal of Neural Systems 27, no. 01 (2016): 1650045. http://dx.doi.org/10.1142/s0129065716500453.

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Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass models. Neural mass models describe the mean firing rates and mean membrane potentials of populations of neurons. Various neural mass models exist with different levels of complexity and realism. An ideal data-driven model-based analysis framework will incorporate the most realistic model possible, enabling accurate imaging of the physiological variabl

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27

Sabeeh, Sarah. "Enhancing Robotic Grasping Performance through Data-Driven Analysis." Misan Journal of Engineering Sciences 3, no. 1 (2024): 134–56. http://dx.doi.org/10.61263/mjes.v3i1.78.

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In automation, reliability in robotic grasping in dynamic environment is still a problem encountered. Further, there is the need to consider deep learning methods, as traditional approaches are not easily flexible in dealing with different objects and situations. In this work, we aim to analyze how well deep neural network models perform in predicting grasp strength based on data collected from the Smart Grasping Sandbox simulation trials. Hence, the proposed approach for analyzing the joint positions, velocities and efforts led to the design of a deep neural network for improving robot grasp

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28

Novicoff, Wendy M. "Data-Driven Performance Improvement in Designing Healthcare Spaces." HERD: Health Environments Research & Design Journal 7, no. 1 (2013): 79–84. http://dx.doi.org/10.1177/193758671300700107.

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29

Long, Huan, Long Wang, Zijun Zhang, Zhe Song, and Jia Xu. "Data-Driven Wind Turbine Power Generation Performance Monitoring." IEEE Transactions on Industrial Electronics 62, no. 10 (2015): 6627–35. http://dx.doi.org/10.1109/tie.2015.2447508.

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30

Zhong, Ray Y. "RFID Data Driven Performance Evaluation in Production Systems." Procedia CIRP 81 (2019): 24–27. http://dx.doi.org/10.1016/j.procir.2019.03.005.

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31

Chi, Kuang-Hwei, Chien-Chao Tseng, Chih-Zong Lin, and Wen-Kuang Chou. "Performance Analysis of Data-Driven Pipelined Computer Architectures." International Journal of Modelling and Simulation 20, no. 3 (2000): 236–47. http://dx.doi.org/10.1080/02286203.2000.11442162.

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32

Spray, A., and S. Jones. "Performance tradeoffs in rings of data-driven elements." IEEE Transactions on Computers 42, no. 1 (1993): 113–18. http://dx.doi.org/10.1109/12.192221.

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33

Bic, Lubomir, and Robert L. Hartmann. "Simulated performance of a data-driven database machine." Journal of Parallel and Distributed Computing 3, no. 1 (1986): 1–22. http://dx.doi.org/10.1016/0743-7315(86)90025-0.

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34

Vilanova, R., M. Dominguez, J. Vicario, et al. "Data-driven tool for monitoring of students performance." IFAC-PapersOnLine 52, no. 9 (2019): 165–70. http://dx.doi.org/10.1016/j.ifacol.2019.08.188.

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35

Bolder, Joost, Stephan Kleinendorst, and Tom Oomen. "Data-driven multivariable ILC: enhanced performance by eliminatingLandQfilters." International Journal of Robust and Nonlinear Control 28, no. 12 (2016): 3728–51. http://dx.doi.org/10.1002/rnc.3611.

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36

Johnson, D., and F. Berman. "Performance of the Efficient Data-Driven Evaluation Scheme." Journal of Parallel and Distributed Computing 18, no. 3 (1993): 340–46. http://dx.doi.org/10.1006/jpdc.1993.1069.

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37

Jiang, Zhigang, Zhouyang Ding, Hua Zhang, Wei Cai, and Ying Liu. "Data-driven ecological performance evaluation for remanufacturing process." Energy Conversion and Management 198 (October 2019): 111844. http://dx.doi.org/10.1016/j.enconman.2019.111844.

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38

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

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Wu, Zhiyong, Jie Liang, Jingzhou Fu, Mingzhe Wang, and Yu Jiang. "Hulk: Exploring Data-Sensitive Performance Anomalies in DBMSs via Data-Driven Analysis." Proceedings of the ACM on Software Engineering 2, ISSTA (2025): 2181–202. https://doi.org/10.1145/3728973.

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Performance is crucial for database management systems (DBMSs), and they are always designed to handle ever-changing workloads efficiently. However, the complexity of the cost-based optimizer (CBO) and its interactions can introduce implementation errors, leading to data-sensitive performance anomalies. These anomalies may cause significant performance degradation compared to the expected design under certain datasets. To diagnose performance issues, DBMS developers often rely on intuitions or compare execution times to a baseline DBMS. These approaches overlook the impact of datasets on perfo

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Morais da Costa, B., J. Þ. Snæbjörnsson, O. A. Øiseth, J. Wang, and J. B. Jakobsen. "Data-driven prediction of mean wind turbulence from topographic data." IOP Conference Series: Materials Science and Engineering 1201, no. 1 (2021): 012005. http://dx.doi.org/10.1088/1757-899x/1201/1/012005.

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Abstract This study presents a data-driven model to predict mean turbulence intensities at desired generic locations, for all wind directions. The model, a multilayer perceptron, requires only information about the local topography and a historical dataset of wind measurements and topography at other locations. Five years of data from six different wind measurement mast locations were used. A k-fold cross-validation evaluated the model at each location, where four locations were used for the training data, another location was used for validation, and the remaining one to test the model. The m

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Wen, Tao, Xu Zhou, Xiaolong Li, and Zhiqiang Long. "Data-Driven Nonlinear Iterative Inversion Suspension Control." Actuators 12, no. 2 (2023): 68. http://dx.doi.org/10.3390/act12020068.

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The commercial operation of the maglev train has strict requirements for the reliability and safety of the suspension control system. However, due to a large number of unmodeled dynamics of the suspension system, it is difficult to obtain the precise mathematical model of the suspension system. After the suspension system has been operated for a long time with high load, the system model will change due to the wear, aging and failure of components, as well as the settlement of the line and track. The control performance is degraded. Therefore, this paper proposes a data-driven nonlinear iterat

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Olufunke, Anne Alabi, Aribidesi Ajayi Funmilayo, Ann Udeh Chioma, and Pelumi Efunniyi Christianah. "Data-driven employee engagement: A pathway to superior customer service." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 923–33. https://doi.org/10.5281/zenodo.14937275.

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This paper explores the significant correlation between employee engagement and customer service quality, emphasizing the role of data-driven strategies in enhancing organizational outcomes. The paper highlights the importance of using data analytics to understand and improve employee engagement by analyzing key theories that link engagement to customer satisfaction. It discusses the critical metrics used to measure engagement and how data-driven insights can inform HR strategies, leading to superior customer service. The paper also examines the implications of implementing these strategies, a

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Researcher. "CUBE TECHNOLOGIES - EMPOWERING DATA-DRIVEN DECISION-MAKING IN THE MODERN ENTERPRISE." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 330–36. https://doi.org/10.5281/zenodo.13982453.

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In today's data-driven landscape, the ability to extract meaningful insights from vast and complex datasets is paramount for informed decision-making. Traditional reporting technologies, while valuable, often fall short in addressing the challenges posed by modern data environments. Cube technologies, rooted in multidimensional data modeling, offer a powerful alternative that empowers users to explore, analyze, and visualize data with unprecedented flexibility and efficiency. This technical article delves into the key advantages of cube technologies over traditional reporting, highlighting the

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Olu-Ajayi, Razak, Hafiz Alaka, Hakeem Owolabi, Lukman Akanbi, and Sikiru Ganiyu. "Data-Driven Tools for Building Energy Consumption Prediction: A Review." Energies 16, no. 6 (2023): 2574. http://dx.doi.org/10.3390/en16062574.

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The development of data-driven building energy consumption prediction models has gained more attention in research due to its relevance for energy planning and conservation. However, many studies have conducted the inappropriate application of data-driven tools for energy consumption prediction in the wrong conditions. For example, employing a data-driven tool to develop a model using a small sample size, despite the recognition of the tool for producing good results in large data conditions. This study delivers a review of 63 studies with a precise focus on evaluating the performance of data-

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Kono, Takayuki, Toru Yamamoto, Takao Hinamoto, and Sirish L. Shah. "DESIGN OF A DATA-DRIVEN PERFORMANCE-ADAPTIVE PID CONTROLLER." IFAC Proceedings Volumes 40, no. 13 (2007): 69–74. http://dx.doi.org/10.3182/20070829-3-ru-4911.00010.

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46

Carpenter, Chris. "Data-Driven Analytics Provide Novel Approach to Performance Diagnosis." Journal of Petroleum Technology 71, no. 10 (2019): 62–64. http://dx.doi.org/10.2118/1019-0062-jpt.

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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 no

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48

Sun, Ke, Iñaki Esnaola, Okechukwu Okorie, Fiona Charnley, Mariale Moreno, and Ashutosh Tiwari. "Data-driven modeling and monitoring of fuel cell performance." International Journal of Hydrogen Energy 46, no. 66 (2021): 33206–17. http://dx.doi.org/10.1016/j.ijhydene.2021.05.210.

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49

Piga, Dario, Marco Forgione, Simone Formentin, and Alberto Bemporad. "Performance-Oriented Model Learning for Data-Driven MPC Design." IEEE Control Systems Letters 3, no. 3 (2019): 577–82. http://dx.doi.org/10.1109/lcsys.2019.2913347.

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50

Stefanini, Alessandro, Davide Aloini, Elisabetta Benevento, Riccardo Dulmin, and Valeria Mininno. "Performance analysis in emergency departments: a data-driven approach." Measuring Business Excellence 22, no. 2 (2018): 130–45. http://dx.doi.org/10.1108/mbe-07-2017-0040.

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PurposeThis paper aims to investigate the process performances in Emergency Departments (EDs) with a novel data-driven approach, permitting to discover the entire patient-flow, deploy the performances in term of time and resources on the activities and flows and identify process deviations and critical bottlenecks. Moreover, the use of this methodology in real time might dynamically provide a picture of the current situation inside the ED in term of waiting times, crowding, resources, etc., supporting the management of patient demand and resources in real time.Design/methodology/approachThe pr

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