Big Data Reduction and Optimization in Sensor Monitoring Network by Bin He, Yonggang Li

“…The spatial and temporal correlation functions are introduced for the data compression and data recovery. It is verified that the proposed algorithm is applicable to WSNs in the underground tunnel.…”
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Biobank Digitalization: From Data Acquisition to Efficient Use by Anastasiia S. Bukreeva, Kristina A. Malsagova, Denis V. Petrovskiy, Tatiana V. Butkova, Valeriya I. Nakhod, Vladimir R. Rudnev, Alexander A. Izotov, Anna L. Kaysheva

“…Digital biobanks have emerged due to digitalization in this field; however, it also entails an increasing number of ethical and legal issues, in particular those related to the protection of donor data and potential commercial applications. The development of biobanks and the size of stored datasets lay the groundwork for proceeding to digital biobanks that intensely employ artificial intelligence tools. …”
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Comparative Study of Imputation Methods for Time Series Data by Daniyal Khan, Alina Lazar

“…Missing and incomplete values pose a significant challenge in analyzing tabular and time-series data. Dealing with missing values is time-consuming and tedious, especially when working with data from real-world applications. …”
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Data Science in Order and Disorder of High-Entropy Materials by Jiasheng Wang, Jianzhong Jiang, Peter K. Liaw, Guihong Geng, Yong Zhang

“…This review systematically summarizes the progress in understanding the order and disorder in HEMs, with a focus on the role of data science in this field. We introduce the basic concepts of order and disorder and the related research in HEMs, discuss the nonlinear behaviors of HEMs, and elaborate on the relevant applications of data science, including analysis by machine learning, molecular dynamics simulations, and Monte Carlo simulations. …”
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Quality Assurance in Big Data Engineering - A Metareview by Daniel Staegemann, Matthias Volk, Klaus Turowski

“…With a continuously increasing amount and complexity of data being produced and captured, traditional ways of dealing with their storing, processing, analysis and presentation are no longer sufficient, which has led to the emergence of the concept of big data. …”
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New method of intelligent data transmission for IoV by Degan ZHANG, Pengzhen ZHAO, Jinxin GAO, Ting ZHANG, Changle GONG

“…Internet of vehicle (IoV) is a hot research topic of the intelligent transportation,and the research of new method of the intelligent data transmission is one of the key technologies.In order to solve the problem of intelligent data transmission of IoV,a kind of new method which includes the important parameters such as vehicle density,vehicle speed,data transmission rate and data delay was proposed.By setting up network model of IoV and delaying function,the optimal routing method for intelligent data transmission which is based on Markovian decision theory was designed.Through comparison experiments with the relative methods show that the performance on improving the data transmission rate and shortening the delay of data transmission of IoV,it has important value for many applications of intelligent transportation with IoV,such as vehicle dynamic remote monitoring.…”
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Globalizing manifold-based reduced models for equations and data by Bálint Kaszás, George Haller

“…We illustrate the power of globalized manifold-based model reduction on several equation-driven and data-driven examples from solid mechanics and fluid mechanics.…”
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Music Genre Classification Based on Functional Data Analysis by Jiahong Shen, Guangrun Xiao

“…This paper proposes a noval method for MGC using functional data analysis (FDA) to represent music signals as smooth functions, capturing their temporal and harmonic properties more naturally. …”
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Energy-aware scheduling policy for data-intensive workflow by Peng XIAO, Zhi-gang HU, Xi-long QU

“…With the increasing scale of data centers,high energy consumption has become a critical issue in high-performance computing area.To address the issue of energy consumption optimization for data-intensive workflow applications,a set of virtual data-accessing nodes are introduced into the original workflow for quantitatively evaluating the data-accessing energy consumption,by which a novel heuristic policy called minimal energy consumption path is designed.Based on the proposed heuristic policy,two energy-aware scheduling algorithms are implemented,which are deprived from the classical HEFT and CPOP scheduling algorithms.Extensive experiments are conducted to investigate the performance of the proposed algorithms,and the results show that they can significantly reduce the data-accessing energy consumption.Also,the proposed algorithms show better adaptive when the system is in presence of large-scale workflows.…”
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Analyzing Big Data with the Hybrid Interval Regression Methods by Chia-Hui Huang, Keng-Chieh Yang, Han-Ying Kao

“…In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. …”
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Regularizing Data for Improving Execution Time of NLP Model by Thang Dang, Yasufumi Sakai, Tsuguchika Tabaru, Akihiko Kasagi

“…Our method focuses on removing unimportant data from the input data set and optimizing the padding of tokens to reduce the processing time for the NLP model. …”
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Modeling integration site data for safety assessment with MELISSA by Tsai-Yu Lin, Giacomo Ceoldo, Kimberley House, Matthew Welty, Thao Thi Dang, Denise Klatt, Christian Brendel, Michael P. Murphy, Kenneth Cornetta, Danilo Pellin

“…MELISSA offers a quantitative tool to bridge the gap between IS data and safety and efficacy evaluation, facilitating the generation of comprehensive data packages supporting Investigational New Drug (IND) and Biologics License (BLA) applications and the development of safe and effective gene and cell therapies.…”
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Data-intensive service deployment based on edge computing by Yongmei GAO, Guanjie CHENG

“…The demand is getting higher and higher for data processing due to big data volume, thus, data-intensive service shave emerged. …”
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Data-intensive service deployment based on edge computing by Yongmei GAO, Guanjie CHENG

“…The demand is getting higher and higher for data processing due to big data volume, thus, data-intensive service shave emerged. …”
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The Immune Epitope Database: How Data Are Entered and Retrieved by Ward Fleri, Kerrie Vaughan, Nima Salimi, Randi Vita, Bjoern Peters, Alessandro Sette

“…Easy access to a vast collection of experimental data on immune epitopes can greatly facilitate the development of therapeutics and vaccines. …”
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RETRACTED: Challenges and opportunities of big data analytics in healthcare by Priyanshi Goyal, Rishabha Malviya

“…This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. …”
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Estimating the collapse of Afghanistan's economy using nightlights data. by Till Raphael Saenger, Ethan B Kapstein, Ronnie Sircar

“…This study showcases the potential applicability of our methodology and the use of appropriately processed monthly nightlights data in scenarios where administrative data is limited or unreliable.…”
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Data simulation in machine olfaction with the R package chemosensors. by Andrey Ziyatdinov, Alexandre Perera-Lluna

“…In machine olfaction, the design of applications based on gas sensor arrays is highly dependent on the robustness of the signal and data processing algorithms. …”
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Using Large Languge Models for Processing Sensor Data by Maciej Hojda

“…The wide availability of sensor data stored in multiple formats makes it difficult to reuse in other applications. …”
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Enhancement over DBSCAN Satellite Spatial Data Clustering by Mohammad Subhi Al-Batah, Enas Rezeg Al-Kwaldeh, Mutaz Abdel Wahed, Mazen Alzyoud, Najah Al-Shanableh

“…This enhancement allows EDBSCAN-H to achieve improved performance across various metrics for clustering spatial data images.…”
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