Relevant bibliographies by topics / Log data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Log data'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Log data"

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Louangrath, P. "New Log Likelihood Estimation Function." Inter. J. Res. Methodol. Soc. Sci 1, no. 2 (2015): 36–47. https://doi.org/10.5281/zenodo.1320774.

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This paper provides a New Log-Likelihood Estimator (NLLE) function as a tool for value approximation. We improved the accuracy of the log MLE in two steps (i) determine the log likelihood of a random variable <em>X</em>, and (ii) adjust the estimate by a factor of . In-Sample testing was accomplished by using daily SET100 indices over a period of 60 days. Out-of-sample data were used for confirmatory verification; out-of-sample data came from 5 major stock markets: NASDAQ, DOW, SP500, DAX, and CAC40. Relevant tests used to compare the results of the proposed NLLE include Cramer-Rao Lower Bound
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Klose, Maria, Diana Steger, Julian Fick, and Cordula Artelt. "Decrypting Log Data." Zeitschrift für Psychologie 230, no. 1 (2022): 3–15. http://dx.doi.org/10.1027/2151-2604/a000484.

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Abstract. Analyzing log data from digital learning environments provides information about online learning. However, it remains unclear how this information can be transferred to psychologically meaningful variables or how it is linked to learning outcomes. The present study summarizes findings on correlations between general online activity and learning outcomes in university settings. The course format, instructions to engage in online discussions, requirements, operationalization of general online activity, and publication year are considered moderators. A multi-source search provided 41 st
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Sedgwick, P. "Log transformation of data." BMJ 345, oct08 1 (2012): e6727-e6727. http://dx.doi.org/10.1136/bmj.e6727.

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Aakash, Aluwala. "Logs Data Reduction - Remove Unnecessary Data to Save Costs." Journal of Scientific and Engineering Research 8, no. 6 (2021): 172–78. https://doi.org/10.5281/zenodo.13338020.

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This paper aims to deal with the reduction of log data by removing unnecessary data to save costs. Since logs are continuing to grow, developers and system administrators are becoming aware of automating analysis through data mining techniques to reduce unnecessary data. The paper concentrates on ways of reducing log data and aims at transforming natural language log data into organized log events using log parsers and Principal Component Analysis (PCA) techniques to reduce unnecessary data and save costs. In addition, these methods eliminate unnecessary data, thereby saving costs in managing
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He, Pinjia, Jieming Zhu, Shilin He, Jian Li, and Michael R. Lyu. "Towards Automated Log Parsing for Large-Scale Log Data Analysis." IEEE Transactions on Dependable and Secure Computing 15, no. 6 (2018): 931–44. http://dx.doi.org/10.1109/tdsc.2017.2762673.

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Oluyede, Broderick, Susan Foya, Gayan Warahena-Liyanage, and Shujiao Huang. "The Log-logistic Weibull Distribution with Applications to Lifetime Data." Austrian Journal of Statistics 45, no. 3 (2016): 43–69. http://dx.doi.org/10.17713/ajs.v45i3.107.

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In this paper, a new generalized distribution called the log-logisticWeibull (LLoGW) distribution is developed and presented. This dis-tribution contain the log-logistic Rayleigh (LLoGR), log-logistic expo-nential (LLoGE) and log-logistic (LLoG) distributions as special cases.The structural properties of the distribution including the hazard func-tion, reverse hazard function, quantile function, probability weightedmoments, moments, conditional moments, mean deviations, Bonferroniand Lorenz curves, distribution of order statistics, L-moments and Renyientropy are derived. Method of maximum like
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Denney, Dennis. "Geosteering With Sonic-Log Data." Journal of Petroleum Technology 64, no. 12 (2012): 130–32. http://dx.doi.org/10.2118/1212-0130-jpt.

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AQIBAH, MAHMUDATUL, NI LUH PUTU SUCIPTAWATI, and I. WAYAN SUMARJAYA. "MODEL DINAMIS AUTOREGRESSIVE DISTRIBUTED LAG (STUDI KASUS: PENGARUH KURS DOLAR AMERIKA DAN INFLASI TERHADAP HARGA SAHAM TAHUN 2014-2018)." E-Jurnal Matematika 9, no. 4 (2020): 240. http://dx.doi.org/10.24843/mtk.2020.v09.i04.p304.

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The aim of this research is to determine the dynamic model equation of autoregressive distributed lag by using koyck method, to find out the effect of log US dollar exchange rate and log inflation on log stock price in 20142018, and to forecast value of log stock price on January 2019August 2019. The data used in 20142018. The data was transformed into logarithm format. Time series plot of log US dollar exchange rate, log inflation, and log stock price suggest that the fluctuation in the data, for instance, both upward and downward trends, during the period. We obtained that the Koyck transfor
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Bader, Sean, Xinming Wu, and Sergey Fomel. "Missing log data interpolation and semiautomatic seismic well ties using data matching techniques." Interpretation 7, no. 2 (2019): T347—T361. http://dx.doi.org/10.1190/int-2018-0044.1.

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Relating well-log data, measured in depth, to seismic data, measured in time, typically requires estimating well-log impedance and a time-to-depth relationship using available sonic and density logs. When sonic and density logs are not available, it is challenging to incorporate wells into integrated reservoir studies because the wells cannot be tied to seismic. We have developed a workflow to estimate missing well-log information, automatically tie wells to seismic data, and generate a global well-log property volume using data matching techniques. We first used the local similarity scan to a
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Calafiore, Giuseppe C., Stephane Gaubert, and Corrado Possieri. "Log-Sum-Exp Neural Networks and Posynomial Models for Convex and Log-Log-Convex Data." IEEE Transactions on Neural Networks and Learning Systems 31, no. 3 (2020): 827–38. http://dx.doi.org/10.1109/tnnls.2019.2910417.

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Dissertations / Theses on the topic "Log data"

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Olsson, Jakob, and Viktor Yberg. "Log data filtering in embedded sensor devices." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175367.

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Data filtering is the disposal of unnecessary data in a data set, to save resources such as server capacity and bandwidth. The method is used to reduce the amount of stored data and thereby prevent valuable resources from processing insignificant information.The purpose of this thesis is to find algorithms for data filtering and to find out which algorithm gives the best effect in embedded devices with resource limitations. This means that the algorithm needs to be resource efficient in terms of memory usage and performance, while saving enough data points to avoid modification or loss of info
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Kurtz, Aaron D. "Determining Mineralogy from Traditional Well Log Data." Marietta College Honors Theses / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=marhonors1366902854.

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Eriksson, Håkan. "Clustering Generic Log Files Under Limited Data Assumptions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189642.

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Complex computer systems are often prone to anomalous or erroneous behavior, which can lead to costly downtime as the systems are diagnosed and repaired. One source of information for diagnosing the errors and anomalies are log files, which are often generated in vast and diverse amounts. However, the log files' size and semi-structured nature makes manual analysis of log files generally infeasible. Some automation is desirable to sift through the log files to find the source of the anomalies or errors. This project aimed to develop a generic algorithm that could cluster diverse log files in a
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Grandi, Karam Samantha 1973. "Effects of borehole stability on well log data." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/30130.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2003.<br>Includes bibliographical references (leaves 145-150).<br>In this thesis we analyze the effects of borehole irregularities on well logs and develop methods to obtain reliable formation properties from such logs. Data from a well in eastern Venezuela are analysed. Borehole irregularities in this well consist mainly of borehole enlargements perpendicular to the borehole axis due to rock failure under high horizontal stresses. They are observed in three forms: as small scale irregula
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Winn, Matthew F. "Analysis of Red Oak Timber Defects and Associated Internal Defect Area for the Generation of Simulated Logs." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/36181.

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Log sawing simulation computer programs can be a valuable tool for training sawyers as well as for testing different sawing patterns. Most available simulation programs rely on databases from which to draw logs and can be very costly and time-consuming to develop. In this study, a computer program was developed that can accurately generate random, artificial logs and serve as an alternative to using a log database. One major advantage of using such a program is that every log generated is unique, whereas a database is finite.<p> Real log and external defect data was obtained from the Forest
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Selig, Henny. "Continuous Event Log Extraction for Process Mining." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210710.

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Process mining is the application of data science technologies on transactional business data to identify or monitor processes within an organization. The analyzed data often originates from process-unaware enterprise software, e.g. Enterprise Resource Planning (ERP) systems. The differences in data management between ERP and process mining systems result in a large fraction of ambiguous cases, affected by convergence and divergence. The consequence is a chasm between the process as interpreted by process mining, and the process as executed in the ERP system. In this thesis, a purchasing proce
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Wang, Xiaohan. "Designing and Evaluating a Visualization System for Log Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279586.

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In the engineering field, log data analysis has been conducted by most companies as it has become a significant step for discovering problems and obtaining insights into the system. Visualization which brings better comprehension of data could be used as an effective and intuitive method for data analysis. This study aims at applying a participatory design approach to develop a visualization system of log data, employed with design activities including interviews, prototyping, usability testing and questionnaires in the research process, along with a comparative study on the impacts of using n
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Radley, Johannes Jurgens. "Pseudo-random access compressed archive for security log data." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1020019.

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We are surrounded by an increasing number of devices and applications that produce a huge quantity of machine generated data. Almost all the machine data contains some element of security information that can be used to discover, monitor and investigate security events.The work proposes a pseudo-random access compressed storage method for log data to be used with an information retrieval system that in turn provides the ability to search and correlate log data and the corresponding events. We explain the method for converting log files into distinct events and storing the events in a compresse
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Lövenvald, Frans-Lukas. "FINDING ANOMALOUS TIME FRAMES IN REAL-WORLD LOG DATA." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163311.

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Anomaly detection is a huge fi€eld of research focused on the task of €finding weird or outlying points in data. Th‘is task is useful in all fi€elds that handle large amounts of data and is therefore a big topic of research. Th‘e focus of research often lies in fi€nding novel approaches for €finding anomalies in already labeled and well-understood data. ‘This thesis will not focus on a novel algorithm but instead display and discuss the power of an anomaly detection process that focuses on feature engineering and feature exploration. Th‘e thesis will also compare two unsupervised anomaly class
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Granlund, Oskar. "Unsupervised anomaly detection on log-based time series data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265534.

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Due to a constant increase in the number of connected devices and there is an increased demand for confidentiality, availability, and integrity on applications. This thesis was focused on unsupervised anomaly detection in data centers. It evaluates how suitable open source state-of-the-art solutions are at finding abnormal trends and patterns in log-based data streams. The methods used in this work are Principal component analysis (PCA), LogCluster, and Hierarchical temporal memory (HTM). They were evaluated using F-score on a real data set from an Apache access log. The data set was carefully
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Books on the topic "Log data"

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Skopik, Florian, Markus Wurzenberger, and Max Landauer. Smart Log Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74450-2.

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Theys, Philippe P. Log data acquisition and quality control. 2nd ed. Technip, 1999.

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Yours, Amazing. Shooting Data Log: Shooters Log. Independently Published, 2018.

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Dee, Loi. Shooting Data Log: Shooters Log. Independently Published, 2018.

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Dee, Loi. Shooters Data Log: Shooters Log. Independently Published, 2018.

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Staff, Journals for All. Shooting Data Log: Shooters Log. Independently Published, 2017.

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Dee, Loi. Shooting Data Log: Shooters Log. Independently Published, 2018.

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Dee, Loi. Reloading Data Log: Shooters Log. Independently Published, 2018.

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Dee, Loi. Shooters Data Log: Shooters Log. Independently Published, 2018.

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Yours, Amazing. Shooters Data Log: Shooters Log. Independently Published, 2018.

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Book chapters on the topic "Log data"

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Zhu, Minpeng, Khalid Mahmood, and Tore Risch. "Scalable Queries Over Log Database Collections." In Data Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20424-6_17.

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Skopik, Florian, Markus Wurzenberger, and Max Landauer. "Survey on Log Clustering Approaches." In Smart Log Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74450-2_2.

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Azen, Razia, and Cindy M. Walker. "Log-Linear Models." In Categorical Data Analysis for the Behavioral and Social Sciences, 2nd ed. Routledge, 2021. http://dx.doi.org/10.4324/9780429330308-7.

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Anderson, Carolyn J., Maria Kateri, and Irini Moustaki. "Log-Linear and Log-Multiplicative Association Models for Categorical Data." In Trends and Challenges in Categorical Data Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31186-4_1.

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Skopik, Florian, Markus Wurzenberger, and Max Landauer. "Generating Character-Based Templates for Log Data." In Smart Log Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74450-2_4.

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Skopik, Florian, Markus Wurzenberger, and Max Landauer. "Incremental Log Data Clustering for Processing Large Amounts of Data Online." In Smart Log Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74450-2_3.

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Skopik, Florian, Markus Wurzenberger, and Max Landauer. "Introduction." In Smart Log Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74450-2_1.

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Skopik, Florian, Markus Wurzenberger, and Max Landauer. "AECID: A Light-Weight Log Analysis Approach for Online Anomaly Detection." In Smart Log Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74450-2_6.

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Skopik, Florian, Markus Wurzenberger, and Max Landauer. "A Concept for a Tree-Based Log Parser Generator." In Smart Log Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74450-2_7.

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Skopik, Florian, Markus Wurzenberger, and Max Landauer. "Variable Type Detector for Statistical Analysis of Log Tokens." In Smart Log Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74450-2_8.

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Conference papers on the topic "Log data"

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Kulkarni, Aditya, Prathit Mehta, Ravi Kumar Singh, et al. "Log Sculptor: Making Logs Great Again." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825361.

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Nipa, Nadira, Nizar Bouguila, and Zachary Patterson. "A Comparative Study of Log-Based Anomaly Detection Methods in Real-World System Logs." In 10th International Conference on Internet of Things, Big Data and Security. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013367000003944.

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Saygılı, Mert İnan, Saltuk Buğra Özelgül, İlkay Samet Öztürk, Kevser Özdem Karaca, A. Orkun Gedik, and M. Ali Akcayol. "Anomaly Detection on Servers Using Log Analysis." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10710799.

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Christensen, Robert, and Feifei Li. "Adaptive log compression for massive log data." In the 2013 international conference. ACM Press, 2013. http://dx.doi.org/10.1145/2463676.2465341.

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Huang, Jiansen, Hui Li, and Jiyang Zhang. "Blockchain Based Log System." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622204.

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Zhang, Xu, Yong Xu, Qingwei Lin, et al. "Robust log-based anomaly detection on unstable log data." In ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 2019. http://dx.doi.org/10.1145/3338906.3338931.

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Meinig, Michael, Peter Tröger, and Christoph Meinel. "Rough Logs: A Data Reduction Approach for Log Files." In 21st International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007735102950302.

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Velis, Danilo R. "Segmentation of well-log data." In 9th International Congress of the Brazilian Geophysical Society & EXPOGEF, Salvador, Bahia, Brazil, 11-14 September 2005. Society of Exploration Geophysicists and Brazilian Geophysical Society, 2005. http://dx.doi.org/10.1190/sbgf2005-297.

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Mehringer, Susan, Christopher R. Myers, Jennifer Houchins, and Lorna Rivera. "Mining Online Training Log Data." In PEARC '19: Practice and Experience in Advanced Research Computing. ACM, 2019. http://dx.doi.org/10.1145/3332186.3332235.

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R. Velis, Danilo. "Segmentation of well-log data." In 9th International Congress of the Brazilian Geophysical Society. European Association of Geoscientists & Engineers, 2005. http://dx.doi.org/10.3997/2214-4609-pdb.160.sbgf298.

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Reports on the topic "Log data"

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Chu, Pinghan. Microtron Data Log. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2568544.

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Borgwardt, Stefan, Ismail Ilkan Ceylan, and Thomas Lukasiewicz. Ontology-Mediated Query Answering over Log-Linear Probabilistic Data. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.221.

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Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated
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McKay, R. B. Solid Waste Information and Tracking System (SWITS) data change request log. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10180561.

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LARRIESTRA, Claudio. Geochemical Well Logging by Geostatistical Integration of Cutting and Well Log Data. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0187.

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Chandrasekar, S., J. Coble, S. Yoder, et al. Data for Investigating the effect of metal powder recycling in Electron beam Powder Bed Fusion using process log data - Data for Investigating the effect of metal powder recycling in Electron beam Powder Bed Fusion using process log data. University of Tennessee, Knoxville, 2019. http://dx.doi.org/10.7290/kt6k64lji.

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Chandrasekar, S., J. Coble, S. Yoder, et al. Data for Investigating the effect of metal powder recycling in Electron beam Powder Bed Fusion using process log data - Data for Investigating the effect of metal powder recycling in Electron beam Powder Bed Fusion using process log data. University of Tennessee, Knoxville, 2019. http://dx.doi.org/10.7290/n8lx7b8.

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Acosta, Jaime C., and Yadira Jacquez. An Analysis Platform for Mobile Ad Hoc Network (MANET) Scenario Execution Log Data. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1001060.

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Hu, K., and D. R. Issler. A comparison of core petrophysical data with well log parameters, Beaufort-Mackenzie Basin. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2009. http://dx.doi.org/10.4095/247449.

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Amini, A., N. Benoit, and H. A. J. Russell. Evaluation of automatic borehole-log identification, selection, and data capture from PDF files. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332258.

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Rodger, Iain, and Sebastian Hörning. Technical note: a statistical approach to identifying geological intervals in wireline log data. The University of Queensland, 2023. http://dx.doi.org/10.14264/2bf1407.

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Journal articles Dissertations / Theses Books
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