Raw Logs Research Articles

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation.

Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care. An examination of EHR interactions using EHR audit logs could provide a scalable, unobtrusive way to quantify the nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex; however, simple analytical methods cannot discover complex temporal patterns, requiring use of state-of-the-art temporal data-mining approaches. To effectively use these approaches, it is necessary to structure the raw audit logs into a consistent and scalable logical data model that can be consumed by machine learning (ML) algorithms. We aimed to conceptualize a logical data model for nurse-EHR interactions that would support the future development of temporal ML models based on EHR audit log data. We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from the literature and our previous experience studying temporal patterns in biomedical data, we formulated a logical data model that can describe nurse-EHR interactions, the nurse-intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to the nursing workload in a scalable and extensible manner. We describe the data structure and concepts from EHR audit log data associated with nursing workload as a logical data model named RNteract. We conceptually demonstrate how using this logical data model could support temporal unsupervised ML and state-of-the-art artificial intelligence (AI) methods for predictive modeling. The RNteract logical data model appears capable of supporting a variety of AI-based systems and should be generalizable to any type of EHR system or health care setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support the nursing documentation workload and address nurse burnout.

Log Analyzer and Anomaly Reporting Framework for Proactive System Monitoring.

Abstract: In an era where organizations increasingly rely on intricate software applications, cloud services, and interconnected networks, the significance of analyses cannot be overstated. These tools serve as vigilant custodians of digital footprints, meticulously dissecting the voluminous records encapsulated in log files to extract valuable insights and detect anomalies. As such, log analyses emerge as linchpins in deciphering the meaning behind recorded events, enabling organizations to obtain a more comprehensive understanding of their digital infrastructures and enhance their security posture. This comprehensive exploration aims to untangle the core attributes of log analyses, bringing clarity to their parsing capabilities, the art of information extraction, and the nuanced algorithms that facilitate the conversion of raw logs into actionable insights. Furthermore, against the backdrop of a dynamically evolving cyber threat landscape, the role of log analyses extends beyond conventional diagnostics. These tools have become instrumental in the proactively orchestrated defense against cyber adversaries, empowering organizations to detect and mitigate threats in real-time. Through an in-depth analysis of log analyses and their evolving functionalities, this paper seeks to provide a comprehensive understanding of their Integral role in modern cybersecurity and system management. By elucidating the significance and impact of log analyses, organizations can leverage these tools to fortify their defenses, mitigate risks, and make informed, data-driven decisions in an increasingly complex digital environment

The new approach to establish a better match between obtained electrofacies and hydraulic flow units for oligo-Miocene reservoir rocks, North of Dezful Embayment, SW Iran

Routine core analysis data (porosity and permeability)—used in various methods for hydraulic flow unit (HFU) determination of reservoir rocks—are unavailable in all drilled wells. On the other hand, raw petrophysical wireline logs—applied to determine reservoir electrofacies (EF)—are usually available in all wells. Since cores provide accurate data on reservoir characteristics, the lack of cores has always interested petroleum geologists and engineers. Therefore, introducing a new method to give almost accurate data about reservoir rocks in uncored wells has always interested petroleum geologists and engineers. As the type of input data that was used to determine HFUs and reservoir EFs are fundamentally different from each other, providing an approach that can create a better match between the results of these two rock typing methods is always one of significant interest for researchers. In this research, capillary pressure (Pc) test results are vital in obtaining reservoir EFs compatible with HFUs for the Oligo-Miocene Asmari Formation in Qale Nar Oilfield. So that only EFs that are compatible with Pc test results are approved. Flow zone indicator (FZI) method was applied to determine five HFU including A (Log FZI > − 0.05, average of core porosity and permeability are 5.8% and 0.37 mD) to E (Log FZI < − 0.65, average of core porosity and permeability are 0.07% and 0.03 mD). Furthermore, based on raw petrophysical wireline logs and MRGC algorithm in Geolog software, five electrofacies (EF) were indicated containing EF 1 (average of core porosity and permeability are 5.91% and 0.38 mD) to EF 5 (average of core porosity and permeability are 0.08% and 0.02 mD). The correlation between HFUs and EFs shows that HFU A to HFU E is compatible with EF 1 to EF 5. Also, examining the obtained electrofacies in the modified Lorenz plot indicates that EF 1 and 2 perfectly match intervals with a high fluid flow regime. By this method, it is possible to provide an almost accurate estimation of hydraulic flow unit distribution for wells and intervals without cores.

Reservoir Petrofacies Predicted Using Logs Data: A Study of Shale Oil from Seven Members of the Upper Triassic Yanchang Formation, Ordos Basin, China

The identification and prediction of petrofacies plays a crucial role in the study of shale oil and gas “sweet spots”. However, the petrofacies identified through core and core test data are not available for all wells. Therefore, it is essential to establish a petrofacies identification model using conventional well logging data. In this study, we determined the petrofacies of shale oil reservoirs in the Upper Triassic Yanchang Formation, Ordos Basin, China, based on scanning electron microscopy, core porosity and total organic carbon (TOC), and brittleness index calculations from X-ray diffraction (XRD) experiments conducted on seven members of the formation. Furthermore, we compared the interpreted logs with the raw well logs data clustered into electrofacies in order to assess their compliance with the petrofacies, using the Multi-Resolution Graph-Based Clustering (MRGC) method. Through an analysis of pore structure type, core porosity, TOC, and brittleness index, we identified four types of lithofacies with varying reservoir quality: PF A &gt; PF B &gt; PF C &gt; PF D. The compliance of the clustered electrofacies with the petrofacies obtained from the interpreted logs was found to be 85.42%. However, the compliance between the clustered electrofacies and the petrofacies obtained from the raw well logs was only 47.92%. Hence, the interpreted logs exhibit a stronger correlation with petrofacies characterization, and their utilization as input data is more beneficial in accurately predicting petrofacies through machine learning algorithms.

The Accuracy of CT Scanning in the Assessment of the Internal and External Qualitative Features of Wood Logs.

The qualitative evaluation of harvested raw logs and sawlogs is mainly based on the quantitative and qualitative evaluation of the visible macroscopic features of the wood. Modern methods allow for the analysis of whole logs by means of computed tomography. These devices can analyze the internal qualitative features of wood that are not visible on the external structures of the logs. The aim of this work was to evaluate the detection accuracy of a CT-scanning device intended for scanning logs on the internal qualitative features of wood using model trunks. Two logs of beech and oak with a length of 4 m were selected for the analysis, based on availability. Qualitative features were identified through computed tomography scanning, visually identified on cut sections, and then manually measured in accordance with applicable legislation. Relatively good agreement was demonstrated for the detected features in terms of identifying their location (dimension in millimeters from the end of the log). For this parameter, the average differences were 0.90% on the beech log and only 1.21% on the oak log. Relatively high accuracy was shown via CT detection of qualitative features in the beech section (with average differences in dimensions of only 3.5%). In the case of the oak log, the dimensions of the quality features were significantly overestimated. These results indicate that CT scanning technology may have a problem with some hardwood species. It was primarily developed for coniferous tree species, and software algorithms are, therefore, not yet fully adapted to the precise detection of the dimensions of individual quality features. Despite the detected differences, it was confirmed that the CT technology of scanning harvested wood can have a fundamental impact on optimization procedures in the recovery and processing of wood. Renting a scanning line for a certain capacity of wood volume appears to be a deployment option for forestry operations and smaller wood processing operations. Thus, this technology can become an important factor in improving the economic evaluation of the final production of wood.

PVE: A log parsing method based on VAE using embedding vectors

Log parsing is a critical task that converts unstructured raw logs into structured data for downstream tasks. Existing methods often rely on manual string-matching rules to extract template tokens, leading to lower adaptability on different log datasets. To address this issue, we propose an automated log parsing method, PVE, which leverages Variational Auto-Encoder (VAE) to build a semi-supervised model for categorizing log tokens. Inspired by the observation that log template tokens often consist of words, we choose common words and their combinations to serve as training data to enhance the diversity of structure features of template tokens. Specifically, PVE constructs two types of embedding vectors, the sum embedding and the n-gram embedding, for each word and word combination. The structure features of template tokens can be learned by training VAE on these embeddings. PVE categorizes a token as a template token if it is similar to the training data when log parsing. To improve efficiency, we use the average similarity between token embedding and VAE samples to determine the token type, rather than the reconstruction error. Evaluations on 16 real-world log datasets demonstrate that our method has an average accuracy of 0.878, which outperforms comparison methods in terms of parsing accuracy and adaptability.

Conservation by corruption: The hidden yet regulated economy in Nepal's community forest timber sector

Through the case of commercial timber production in Nepal's community forests, we uncover and explain how effective anti-corruption and harvest regulation have produced a kind of ‘allowed’ corruption that promotes forest conservation. An ethnographic study in four community forests and in-depth interviews of nearly 200 actors along the Sal (Shorea robusta) timber commodity chain showed that all actors participated in a highly organised form of collusive corruption. Anti-corruption officials call this practice “corruption without illegality” because it does not involve unauthorised harvest in community forests. Instead, it suppresses producer prices through legally required but rigged timber auctions that generate windfall profits, which sawmill owners share with upstream actors to ensure a steady supply of raw logs. Local-level timber brokers connect community forest user groups to sawmill owners. They also operate as stealth conveyers of unofficial payments to forestry officials and other upstream actors because they can camouflage such cash flows as transaction costs. Anti-corruption authorities enforce formal timber harvesting rules, which deters forestry officials from getting involved in overharvesting schemes. However, these same rules, plus some legal posturing, allow forestry officials to extract rents from legally harvested logs at minimum risk. This is hardly a coincidence because it enables the central administration to regulate difficult-to-control field-level forestry officials' behaviour without curtailing their access to informal incomes. Forest user groups lose out, but they could increase their timber income substantially by exercising their powers to decide whether or not to harvest timber. Elevating timber auction floor prices through state intervention is also feasible.

Classifying clinical work settings using EHR audit logs: a machine learning approach.

We used electronic health record (EHR)-based raw audit logs to classify the work settings of anesthesiology physicians providing care in both surgical intensive care units (ICUs) and operating rooms. Observational study. Attending anesthesiologists who worked at least 1 shift in 1 of 4 surgical ICUs in calendar year 2019 were included. Time-stamped EHR-based audit log events for each week were used to create event frequencies and represented as a term frequency-inverse document frequency matrix. Primary classification outcome of interest was a physician's clinical work setting. Performance of multiple supervised machine learning classifiers were evaluated. A total of 24 attending physicians were included; physicians performed a median (IQR) of 2545 (906-5071) EHR-based actions per week and worked a median (IQR) of 5 (3-7) weeks in a surgical ICU. A random forest classifier yielded the best discriminative performance (mean [SD] area under receiver operating characteristic curve, 0.88 [0.05]; mean [SD] area under precision-recall curve, 0.72 [0.13]). Model explanations illustrated that clinical activities related to signing of clinical notes, printing handoff data, and updating diagnosis information were associated with the positive prediction of working in a surgical ICU setting. A random forest classifier using a frequency-based feature engineering approach successfully predicted work settings of physicians with multiple clinical responsibilities with high accuracy. These findings highlight opportunities for using audit logs for automated assessment of clinician activities and their work settings, thereby affording the ability to accurately assess context-specific work characteristics (eg, workload).

System Log Parsing: A Survey

Modern information and communication systems have become increasingly challenging to manage. The ubiquitous system logs contain plentiful information and are thus widely exploited as an alternative source for system management. As log files usually encompass large amounts of raw data, manually analyzing them is laborious and error-prone. Consequently, many research endeavors have been devoted to automatic log analysis. However, these works typically expect structured input and struggle with the heterogeneous nature of raw system logs. Log parsing closes this gap by converting the unstructured system logs to structured records. Many parsers were proposed during the last decades to accommodate various log analysis applications. However, due to the ample solution space and lack of systematic evaluation, it is not easy for practitioners to find ready-made solutions that fit their needs. This paper aims to provide a comprehensive survey on log parsing. We begin with an exhaustive taxonomy of existing log parsers. Then we empirically analyze the critical performance and operational features for 17 open-source solutions both quantitatively and qualitatively, and whenever applicable discuss the merits of alternative approaches. We also elaborate on future challenges and discuss the relevant research directions. We envision this survey as a helpful resource for system administrators and domain experts to choose the most desirable open-source solution or implement new ones based on application-specific requirements.

Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

System logs are essential for detecting anomalies, querying faults, and tracing attacks. Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection, it cannot meet the actual needs. The implementation of automated log anomaly detection is a topic that demands urgent research. However, the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data. Meanwhile, there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection. This paper proposes a novel and practical detection model named LCC-HGLog, the core of which is the conversion of log anomaly detection into a graph classification problem. Semantic temporal graphs (STG) are constructed by extracting the raw logs’ execution sequences and template semantics. Then a unique graph classifier is used to better comprehend each STG’s semantic, sequential, and structural features. The classification model is trained jointly by graph classification loss and label contrastive loss. While achieving discriminability at the class-level, it increases the fine-grained identification at the instance-level, thus achieving detection performance even with a small amount of labeled data. We have conducted numerous experiments on real log datasets, showing that the proposed model outperforms the baseline methods and obtains the best all-around performance. Moreover, the detection performance degrades to less than 1% when only 10% of the labeled data is used. With 200 labeled samples, we can achieve the same or better detection results than the baseline methods.

An Efficient Way to Parse Logs Automatically for Multiline Events

In order to obtain information or discover knowledge from system logs, the first step is to perform log parsing, whereby unstructured raw logs can be transformed into a sequence of structured events. Although comprehensive studies on log parsing have been conducted in recent years, most assume that one event object corresponds to a single-line message. However, in a growing number of scenarios, one event object spans multiple lines in the log, for which parsing methods toward single-line events are not applicable. In order to address this problem, this paper proposes an automated <bold>l</bold>og <bold>p</bold>arsing method for <bold>m</bold>ultiline <bold>e</bold>vents (LPME). LPME finds multiline event objects via iterative scanning, driven by a set of heuristic rules derived from practice. The advantage of LPME is that it proposes a cohesion-based evaluation method for multiline events and a bottom-up search approach that eliminates the process of enumerating all combinations. We analyze the algorithmic complexity of LPME and validate it on four datasets from different backgrounds. Evaluations show that the actual time complexity of LPME parsing for multiline events is close to the constant time, which enables it to handle large-scale sample inputs. On the experimental datasets, the performance of LPME achieves 1.0 for recall, and the precision is generally higher than 0.9, which demonstrates the effectiveness of the proposed LPME.

Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach.

Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15767634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.

A Decade of Surgery Aboard the U.S. Naval Ship COMFORT (T-AH 20).

The U.S. Naval Ship COMFORT has performed six humanitarian assistance and disaster relief mission since 2007. This paper describes the surgical volume per surgical specialty for five missions spanning 19 countries. Raw surgical case logs were analyzed for total case volume, total operating days, unanticipated return to operating room, and percentage of pediatric cases (<18 years old) for each country visited. Total surgical volume for the five missions was 5,142. The countries most frequently visited were Columbia and Haiti with seven and five visits, respectively. General surgery, ophthalmology, and plastic surgery have had consistent volume over time. Orthopedic surgery volume has steadily decreased with the exception of the 2018 mission. As volume in military treatment facilities declines, alternative sources of surgical volume for military surgeons are being examined. This paper highlights the historical volume which can inform future personnel planning requirements of U.S. Naval Ship COMFORT missions. With the exception of orthopedic surgery, surgical volume has remained consistent over the last decade. For future best practice, historical case data should be used to determine staffing needs on hospital ships and case logs and operating procedures and follow-up protocols should be standardized.

Study of Scientific Production on Production of Wooden Doors

The forest sector has a significant role for society reaching economic, social and environmental spheres. There is a range of products from reforestation, the most important are raw wood or logs, sawn wood, cellulose, wood panels, biomass and doors. The world door export market alone reached 3.84 billion dollars, considering an interval of ten years, it achieved an average growth of +3,3% per year. With the growing demand and the demand for quality from the external environment makes door manufacturers invest in innovation and studies on the subject. The present work used a bibliometric method in four databases (Scopus, ScienceDirect, Scielo and Mendeley) and articles and publications were searched in conference proceedings until 1996, with the theme of wooden door production, in order to understand what is being disseminated in the academic environment regarding this theme. Data were compiled on the year, authors, journals, countries and database that obtained the most publications. In general, we deduce the low explanation of this content, as few articles were found, the non-appearance of an author who stands out in this theme, in addition to the lack of publications between the years 1999 to 2010 and the lack of studies from China. which in turn is more exporter of this product.

Informer: Irregular traffic detection for containerized microservices RPC in the real world

Containerized microservices have been widely deployed in the industry. Meanwhile, security issues also arise. Many security enhancement mechanisms for containerized microservices require predefined rules and policies. However, it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data. Hence, automatic policy generation becomes indispensable. In this paper, we focus on the automatic solution for the security problem: irregular traffic detection for RPCs. We propose Informer, a two-phase machine learning framework to track the traffic of each RPC and automatically report anomalous points. We first identify RPC chain patterns using density-based clustering techniques and build a graph for each critical pattern. Next, we solve the irregular RPC traffic detection problem as a prediction problem for attributed graphs with time series by leveraging spatial-temporal graph convolution networks. Since the framework builds multiple models and makes individual predictions for each RPC chain pattern, it can be efficiently updated upon legitimate changes in any graphs. In evaluations, we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from a large Kubernetes system for two weeks. We provide two case studies of detected real-world threats. As a result, our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario, which demonstrates the effectiveness of Informer. Furthermore, we extensively evaluated the risk of adversarial attacks for our prediction model under different reality constraints and showed that the model is robust to such attacks in most real-world scenarios.

Driving Utah's Rivers: Working Water in the West

Driving Utah's Rivers: Working Water in the West

Multi-level process mining methodology for exploring disease-specific care processes

BackgroundPublic healthcare is a complex domain with many actors and highly variable protocols, which makes traditional process mining tools less effective and calls for specialized methods. AimThe objective of the work was to develop a generally applicable process mining methodology to explore care processes related to diseases. MethodsThe proposed methodology called Process Mining Methodology for Exploring Disease-specific Care Processes (MEDCP) is based on a systematic, step-wise refinement of the raw event logs by using such a multi-level expert taxonomy of events that encapsulates the professional concepts of the analysis. A treatment process is defined according to domain-specific rules to identify the starting (index) and closing events. Concepts from various levels of the taxonomy support the final process definition for an analysis that can deliver meaningful conclusions for domain experts. ResultsThe applicability of the methodology was demonstrated on two case studies in the cardiological and oncological care domains, in the public health care system in Hungary over a period of ten years. Thanks to the multi-level taxonomy, these studies successfully identified the most important high-level event sequence patterns and some key anomalies in the national care system, such as the significantly different behavior of low-volume vs. high volume care providers in the oncology study or the geographically connected, homogeneous clusters of providers with similar care spectra in the cardiology study. DiscussionAs the case studies showed, the proposed methodology can improve the efficiency of standard process mining methods, and deliver high level conclusions that are easy to interpret by domain experts. System-level insight into health care processes can serve as a basis for the optimisation and long-term planning of the whole care system.

Costs of semi-mechanized forest harvest of Tectona grandis L. F. in the brazilian Amazon

The objective of this study was to identify the harvesting system applied for Tectona grandis and its adaptations to describe the harvesting activities, characterization of the machines used and costing. The costs raised in this work were divided by activity and cost component. The work resulted in the description of the forest felling, this being carried out using three chainsaws of the Stihl brand, model MS 660 with the chainsaw teams adjusted to the scheme (1+1), an operator and a helper. As an adaptation of the system, the extraction was performed from inside the field to the storage yard, and executed with a Massey Ferguson tractor, model MF 275, of medium size with high axle, 4x2 rear-wheel drive, 75 horsepower and torque of approximately 28 kgfm adapted with a winch and use of drag chain, a tractor driver and two assistants. Labor was the most expensive cost, representing 80.82% of the total harvest cost. Gross profit per hectare was R$ 15,525.00, of which 30.76% was consumed by operating costs, being labor the most significant. Selling raw logs, as it was performed in this work, significantly decreases the price of the cubic meter.

Information support of the dynamics of system connectivity of wheel harvester operations

The article discusses the initial system dynamics of the connectivity of the work of the harvester head and the basic machine for the production of logs from the position of forming by processing the dynamics of work in a single functional space of time. On the basis of a deeper analysis of the dynamics of the production cycle of secondary raw materials and logs of the harvester head and the base machine in combination from the position of system analysis, a mathematical model for calculating the productivity of wood materials obtained from trees in a multi-operational sequential representation was built, performed by one harvester head. The total functional time of production of a unit of labor by the performed suboperations and its storage for all trees in the cycle becomes the main connecting parameter that allows us to represent the dynamics of the process. A solution is proposed to reduce the functional time for the operator of the harvester to make a decision on the production of logs of one or another quality and dimensional characteristics by using program for dynamic calculation and graphical representation of the output of finished products from a separate part of the tree trunk «STMLOGIC».

Studi Rendemen Finir dan Besarnya Limbah Pada Pengupasan Finir dengan Mesin Rotary Lathe Tipe Spindleless

In general, the log-block peeling yield on a rotary lathe machine ranges from 70 - 80% of the log volume in the form of veneer, and the remaining 20-30% is waste. This variation depends on the quality and type (species) of the log of the raw material. The veneer peeling usually consist of the face and back veneer (continuous veneer) ranging from 50% to 70% and core veneer as poly piece veneer ranging from 20 to 30%. Under certain conditions, the waste can reach up to 50% including losses due to rounding, shrinkage of the veneer, cracks, spurs, and pith (log-core).&#x0D; From the description above and several other observations, the research was carried out regarding this matter. There is a way to reduce the amount of waste in log peeling using rotary machines and at the same time increasing the recovery peeling. Thus, it will reduce production costs and increase profits for the company.&#x0D; One of the contributors to a large amount of veneer peeling waste is the use of rotary lathe veneer peeling machines with large spindles which are still widely used in veneer and plywood factories (plymill), especially in Indonesia. So far, on average half of raw logs turn into waste, and 25 percent is contributed by a rotary lathe machine. Meanwhile, on the other hand, the need for plywood products will continue to increase day by day. This is because plywood is one of the most environmentally friendly building materials and comes from renewable natural resources. Along with the development of the plywood industry, the increase of raw material needs is inevitable. So, waste products will also continue to increase too.&#x0D; In this regard, there must be fundamental changes in this industry, including the use of rotary lathe without spindles as the substitute from the conventional machine. In relevance to this situation, this research was directed with the aim of knowing the yield and amount of veneer peeled by a spindle-less rotary lathe machine and at the same time to find out the waste it produces. So, this information can later become a consideration for stakeholders to replace the old rotary lathe with a type spindle-less without a doubt.&#x0D; The results show that veneer peeling using a spindle-less rotary lathe machine increases the yield of the veneer and reduce the amount of waste. Most of the peeling results are in the form of a veneer consisting of 58.83% continuous/endless veneer for the face/back, and 22.83% of poly piece core (odd veneer) from the core veneer. The total veneer yield is 81.67%. The remaining were 1.32%, 0.93%, and 16.08% for log-core waste, cut edge finish, and round-up veneer, respectively. The spindle-less rotary lathe machine has provided increased yields and significantly reduced the amount of waste.&#x0D; &#x0D; Keywords : Waste, rotary lathe, spindle-less, veneer.