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Journal articles on the topic 'Log data'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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9

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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10

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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11

Suits, L. D., T. C. Sheahan, and Retnamony G. Robinson. "Analysis of Radial Consolidation Test Data Using a log-log Method." Geotechnical Testing Journal 32, no. 2 (2009): 101034. http://dx.doi.org/10.1520/gtj101034.

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12

Fries, O., K. Mehlhorn, S. Näher, and A. Tsakalidis. "A log log n data structure for three-sided range queries." Information Processing Letters 25, no. 4 (1987): 269–73. http://dx.doi.org/10.1016/0020-0190(87)90174-8.

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13

Al-Adani, Nabil. "Data Blocking or Zoning: Well-Log-Data Application." Journal of Canadian Petroleum Technology 51, no. 01 (2012): 66–73. http://dx.doi.org/10.2118/154017-pa.

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14

Vora, Mehul Nalin. "Predicting Utilization of Server Resources from Log Data." International Journal of Computer Theory and Engineering 6, no. 2 (2014): 118–23. http://dx.doi.org/10.7763/ijcte.2014.v6.848.

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15

Jeon, Byung-Jin, Deok-Byeong Yoon, and Seung-Soo Shin. "Integrated Monitoring System using Log Data." Convergence Society for SMB 7, no. 1 (2017): 35–42. http://dx.doi.org/10.22156/cs4smb.2017.7.1.035.

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16

Ghule, Sayalee. "Log File Data Extraction or Mining." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4802–6. http://dx.doi.org/10.22214/ijraset.2021.35833.

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Log records contain data generally Client Title, IP Address, Time Stamp, Get to Ask, number of Bytes Exchanged, Result Status, URL that Intimated, and Client Chairman. The log records are kept up by the internet servers. By analysing these log records gives a flawless thought to the client. The wide Web may be a solid store of web pages that gives the Net clients piles of information. With the change in the number and complexity of Websites, the degree of the net has gotten to be massively wide. Web Utilization Mining may be a division of web mining that consolidates the application of mining

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17

Mohaghegh, Shahab, Bogdan Balan, and Samuel Ameri. "Permeability Determination From Well Log Data." SPE Formation Evaluation 12, no. 03 (1997): 170–74. http://dx.doi.org/10.2118/30978-pa.

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18

KOK, MUSTAFA VERSAN, BAHADIR GOKCAL, and GIZEM ERSOY. "Reservoir Analysis by Well Log Data." Energy Sources 27, no. 5 (2005): 399–404. http://dx.doi.org/10.1080/00908310490441791.

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19

O’Hara, Robert B., and D. Johan Kotze. "Do not log-transform count data." Methods in Ecology and Evolution 1, no. 2 (2010): 118–22. http://dx.doi.org/10.1111/j.2041-210x.2010.00021.x.

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20

Buoro, Alvaro Bueno, and João B. C. Silva. "Ambiguity analysis of well‐log data." GEOPHYSICS 59, no. 3 (1994): 336–44. http://dx.doi.org/10.1190/1.1443595.

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Ambiguity in a geophysical interpretation problem is the possibility of accepting more than one solution caused either by solution nonuniqueness or instability. Nonuniqueness is related to the existence of more than one solution regardless of the precision of observations. On the other hand, instability is related to the acceptance of different solutions producing data fittings within the expected observational errors. We studied the ambiguity in the inversion of well‐logging data using a method based on the analysis of a finite number of acceptable solutions, which are ordered, in the solutio

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21

Velis, Danilo R. "Statistical Segmentation of Geophysical Log Data." Mathematical Geology 39, no. 4 (2007): 409–17. http://dx.doi.org/10.1007/s11004-007-9103-y.

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22

Holste, James C., Kenneth R. Hall, and Gustavo A. Iglesias-Silva. "Log-linear plots for data representation." AIChE Journal 42, no. 1 (1996): 296–97. http://dx.doi.org/10.1002/aic.690420129.

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23

Cui, Hongyuan, Jiajun Yang, Ying Liu, Zheng Zheng, and Kaichao Wu. "Data Mining-based DNS Log Analysis." Annals of Data Science 1, no. 3-4 (2014): 311–23. http://dx.doi.org/10.1007/s40745-014-0023-7.

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24

Liu, Ruiyang. "Data Security Threats of Log Aggregation." SHS Web of Conferences 170 (2023): 01007. http://dx.doi.org/10.1051/shsconf/202317001007.

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The development of cloud computing and distributed technologies has shifted log management operations to an aggregated form. Diverse information devices as well as information systems perform data collection and status analysis through a unified log aggregation management system. However, with the increasingly diverse business forms, the threats confronted by log aggregation systems are also mounting, among which data leakage, data tampering, data loss, and security vulnerabilities dominate the main positions. Data leakage imposes a severe challenge to the data privacy of enterprises and users

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25

Koprawi, Muhammad, Wahyu Sukestyastama Putra, Wahid Miftahul Ashari, Jeki Kuswanto, and Anggit Ferdita Nugraha. "LOGAN: Log Analyzer Simulation Sebagai Media Simulasi Analisis Log Berbasis Data Generator." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 10, no. 1 (2024): 171. http://dx.doi.org/10.26418/jp.v10i1.61446.

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Aktivitas pengunjung sebuah website akan tersimpan di server pada file yang bernama access log. Access log menyimpan berbagai informasi penting seperti IP address pengunjung sampai user agent yang digunakan (browser, sistem operasi, dan sebagainya). Untuk mendapatkan file access log setidaknya harus memiliki akses ke server sehingga file tersebut dapat diambil kemudian dianalisis. Pada penelitian ini, dibuat sebuah sistem yang digunakan sebagai alat untuk membuat file access log secara otomatis dan dirancang mirip seperti format file access log yang ada pada server. Selain sebagi alat untuk me

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26

Nandram, Balgobin. "Discrimination between the complementary log-log and logistic model for ordinal data." Communications in Statistics - Theory and Methods 18, no. 6 (1989): 2155–64. http://dx.doi.org/10.1080/03610928908830028.

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27

Lundgren, Christina. "Predicting Log Type and Knot Size Category Using External Log Shape Data from a 3D Log Scanner." Scandinavian Journal of Forest Research 15, no. 1 (2000): 119–26. http://dx.doi.org/10.1080/02827580050160556.

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28

Setyaningrum, Dwi Ayu, and Timbang Sirait. "PEMODELAN LOGIT, PROBIT, DAN COMPLEMENTARY LOG-LOG." Seminar Nasional Official Statistics 2020, no. 1 (2021): 429–38. http://dx.doi.org/10.34123/semnasoffstat.v2020i1.383.

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Pemodelan data yang tepat dapat digunakan dalam pengambilan keputusan yang bersifat strategis. Salah satu bentuk pemodelan yang umum digunakan yaitu analisis regresi. Dalam pemodelan regresi harus dilihat tipe data pada variabel respon terlebih dahulu agar dapat menentukan pemodelan yang dapat dilakukan sesuai tipe data. Tujuan dari penelitian ini adalah untuk mengetahui gambaran umum dan karakteristik partisipasi angkatan kerja perempuan dalam perekonomian di Kalimantan Timur tahun 2019, melakukan pemodelan data dengan menggunakan regresi Logistik Biner, Probit, dan Complementary Log-log, ser

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29

Wagito, Wagito, and Dison Librado. "ANALISIS DATA AKSES SITUS BERDASAR TEKNOLOGI LOG SERVER." Technologia : Jurnal Ilmiah 13, no. 1 (2022): 22. http://dx.doi.org/10.31602/tji.v13i1.6113.

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Selama dipublikasikan, situs suatu institusi harus dianalisis tentang data penggunaan dan akses kunjungan pengguna. Analisis dapat dilakukan terhadap halaman yang paling banyak dikunjungi, bagaimana cara pengunjung mengakses web, apakah akses pengguna memakai desktop atau smartphone dan sebagainya. Data tersebut sangat penting bagi pengelola situs atau institusi dalam menetapkan kebijakan. Tujuan penelitian yang dilakukan adalah untuk menyusun metode penggunaan data catatan log server untuk analisis akses situs serta analisis data akses berdasarkan catatan log yang dihasilkan oleh server. Tekn

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., Jamaluddin, Septian Tri Nugraha, Maria Maria, and Emi Prasetyawati Umar. "PREDIKSI TOTAL ORGANIC CARBON (TOC) MENGGUNAKAN REGRESI MULTILINEAR DENGAN PENDEKATAN DATA WELL LOG." JURNAL GEOCELEBES 2, no. 1 (2018): 1. http://dx.doi.org/10.20956/geocelebes.v2i1.3568.

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Penelitian ini bertujuan untuk menentukan potensi batuan induk di Formasi Talangakar dengan melihat parameter total organik carbon (TOC). TOC dimodelkan dengan melakukan crossplot antara TOC measured dengan log gamma ray, log densitas, log neutron-porosity, log resistivity, dan log P wave (multi linier regresi). Formasi Talangakar yang menjadi target pada studi ini terletak pada kedalaman 2280 m-2885 m, memiliki material organik yang kaya dengan TOC berkisar antara 1.09 %-1.29 %. Pemodelan TOC didapatkan regresi sebesar 0.1432 terhadap parameter log (log gamma ray, log density, log Neutron-Por

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Ilham Nugraha, Yunanta, Giva Andriana Mutiara, and Rini Handayani. "Log Data Structure for Illegal Logging Tracking System." International Journal of Engineering & Technology 7, no. 4.44 (2018): 147. http://dx.doi.org/10.14419/ijet.v7i4.44.26972.

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Forest is a natural ecosystem that plays an important role in human life. Unfortunately, due to the exploitation of the forest wealth, the forest ecosystem becomes increasingly diminished. One of the most significant forest damages caused by illegal logging. In some cases, sometimes the information about log harvest that obtained from the logging area always has a discrepancy. To reduce the cunning which is done by the perpetrators, however, a system that can track and monitor the log position from a remote area is needed. This system using APC220 as a WSN using Star topology, embed with RFID

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32

Huntington, Paul, David Nicholas, Hamid R. Jamali, and Anthony Watkinson. "Obtaining subject data from log files using deep log analysis: case study OhioLINK." Journal of Information Science 32, no. 4 (2006): 299–308. http://dx.doi.org/10.1177/0165551506065782.

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33

Ang, Jin Sheng, Mohd Jami Jastini, Nizal Mohd Shaharanee Izwan, and Fadli Zolkipli Mohamad. "Enabling efficient business process mining using flatten sequential structure model." Enabling efficient business process mining using flatten sequential structure model 31, no. 1 (2023): 531–41. https://doi.org/10.11591/ijeecs.v31.i1.pp531-541.

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The volume of extensible markup language (XML) format documents is increasing every day due to the development of internet and the use of XML format in business process log file. Storing business process log data in XML format is preferable due to the ability of extensible and storing data irrespective of how it will be represented. However, mining XML format data poses challenges due to its complex data structure and dimensions. This paper proposes a method to convert XML format document into a structured format without ignoring the structural information. Converting semi-structured business

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34

Swati, B. Patil, and Kuruva Arjun. "Analysis of User Session Data using the Map Reduce Classification with Big Data." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 2008–11. https://doi.org/10.31142/ijtsrd18221.

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Enormous information frameworks are unpredictable, comprising of numerous connecting tools and encoding segments, for example, dispersed registering hubs, databases, and middleware. Some of these segments be able to come up short. Judgment the failures major drivers are to a great degree relentless. Examination of BDS formed logs be able to speed up this process. The logs be able to similarly assist improve test form, recognize safety rupture, alter functioning profile, and assist through a number of previous activities require runtime information test. Be that as it may, commonsense difficult

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35

Robert, Freedman, and Gerald N. Minerbo. "Maximum Entropy Inversion of Induction-Log Data." SPE Formation Evaluation 6, no. 02 (1991): 259–68. http://dx.doi.org/10.2118/19608-pa.

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36

Brandt, Sebastian, Elem Güzel Kalaycı, Vladislav Ryzhikov, Guohui Xiao, and Michael Zakharyaschev. "Querying Log Data with Metric Temporal Logic." Journal of Artificial Intelligence Research 62 (August 31, 2018): 829–77. http://dx.doi.org/10.1613/jair.1.11229.

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   We propose a novel framework for ontology-based access to temporal log data using a datalog extension datalogMTL of the Horn fragment of the metric temporal logic MTL. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case full MTL is known to be undecidable. We also prove that nonrecursive datalogMTL is PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate

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37

Pelmegov, R. V. "METHOD FOR AUTOMATED QUALITY CONTROL LOG DATA." Oil and Gas Business, no. 4 (August 2014): 76–88. http://dx.doi.org/10.17122/ogbus-2014-4-76-88.

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38

Elliott, Bruce, and John H. Palmer. "Emancipation from surgical log-book data entry." British Journal of Plastic Surgery 55, no. 3 (2002): 267. http://dx.doi.org/10.1054/bjps.2002.3806.

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39

Bae, Joonsoo. "Diagnosis Analysis of Patient Process Log Data." Journal of Society of Korea Industrial and Systems Engineering 42, no. 4 (2019): 126–34. http://dx.doi.org/10.11627/jkise.2019.42.4.126.

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40

Miller, G. "Statistical modelling of Poisson/log normal data." Radiation Protection Dosimetry 124, no. 2 (2007): 155–63. http://dx.doi.org/10.1093/rpd/ncl544.

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41

Ørmen, Jacob, and Anne Mette Thorhauge. "Smartphone log data in a qualitative perspective." Mobile Media & Communication 3, no. 3 (2015): 335–50. http://dx.doi.org/10.1177/2050157914565845.

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42

Fidler, V. "Log–Linear Mixed Models for Categorical Data." Statistica Neerlandica 41, no. 1 (1987): 45–57. http://dx.doi.org/10.1111/j.1467-9574.1987.tb01170.x.

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43

Inukai, Yusuke, Yuki Fuchigami, and Naoto Matsumura. "Analysis of profitability using log shipment data." Japanese Journal of Forest Planning 53, no. 1 (2019): 15–19. http://dx.doi.org/10.20659/jjfp.53.1_15.

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44

King, D. E. "Incorporating geological data in well log interpretation." Geological Society, London, Special Publications 48, no. 1 (1990): 45–55. http://dx.doi.org/10.1144/gsl.sp.1990.048.01.06.

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45

Veletsianos, George, Justin Reich, and Laura A. Pasquini. "The Life Between Big Data Log Events." AERA Open 2, no. 3 (2016): 233285841665700. http://dx.doi.org/10.1177/2332858416657002.

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46

Wu, Xuanzhi, and Edo Nyland. "Automated stratigraphic interpretation of well‐log data." GEOPHYSICS 52, no. 12 (1987): 1665–76. http://dx.doi.org/10.1190/1.1442283.

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A computerized well‐log stratigraphic interpretation system based on artificial intelligence can be seen as two steps, contact recognition and interval identification. Unlike previous approaches to mathematical zonation which are essentially statistical, consideration of the geologic environment is included for effective interpretation. Following the logic of human experts, the system first determines the log signature of the contacts. An algorithm incorporating linear regression with variable breakpoints is used to describe the structure of log curves. Log features are mapped to the assigned

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47

Bell, Theodore S., Donald D. Dirks, Harry Levitt, and Judy R. Dubno. "Log‐linear modeling of consonant confusion data." Journal of the Acoustical Society of America 79, no. 2 (1986): 518–25. http://dx.doi.org/10.1121/1.393539.

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48

Worthington, Paul F. "Effective integration of core and log data." Marine and Petroleum Geology 11, no. 4 (1994): 457–66. http://dx.doi.org/10.1016/0264-8172(94)90079-5.

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49

Olson, David L. "Ethical aspects of web log data mining." International Journal of Information Technology and Management 7, no. 2 (2008): 190. http://dx.doi.org/10.1504/ijitm.2008.016605.

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50

Roverato, A., M. Lupparelli, and L. La Rocca. "Log-mean linear models for binary data." Biometrika 100, no. 2 (2013): 485–94. http://dx.doi.org/10.1093/biomet/ass080.

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