In this paper, we propose a new distribution, named unit log-log distribution, defined on the bounded (0,1) interval. Basic distributional properties such as model shapes, stochastic ordering, ...quantile function, moments, and order statistics of the newly defined unit distribution are studied. The maximum likelihood estimation method has been pointed out to estimate its model parameters. The new quantile regression model based on the proposed distribution is introduced and it has been derived estimations of its model parameters also. The Monte Carlo simulation studies have been given to see the performance of the estimation method based on the new unit distribution and its regression modeling. Applications of the newly defined distribution and its quantile regression model to real data sets show that the proposed models have better modeling abilities than competitive models. The proposed unit quantile regression model has targeted to explain linear relation between educational measurements of both OECD (Organization for Economic Co-operation and Development) countries and some non-members of OECD countries, and their Better Life Index. The existence of the significant covariates has been seen on the real data applications for the unit median response.
Logs are semi-structured text generated by logging statements in software source code. In recent decades, software logs have become imperative in the reliability assurance mechanism of many software ...systems, because they are often the only data available that record software runtime information. As modern software is evolving into a large scale, the volume of logs has increased rapidly. To enable effective and efficient usage of modern software logs in reliability engineering, a number of studies have been conducted on automated log analysis. This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs, how to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis. Additionally, we survey work that releases open-source toolkits and datasets. Based on the discussion of the recent advances, we present several promising future directions toward real-world and next-generation automated log analysis.
Context: Logs are often the first and only information available to software engineers to understand and debug their systems. Automated log-analysis techniques help software engineers gain insights ...into large log data. These techniques have several steps, among which log abstraction is the most important because it transforms raw log-data into high-level information. Thus, log abstraction allows software engineers to perform further analyses. Existing log-abstraction techniques vary significantly in their designs and performances. To the best of our knowledge, there is no study that examines the performances of these techniques with respect to the following seven quality aspects concurrently: mode, coverage, delimiter independence, efficiency,scalability, system knowledge independence, and parameter tuning effort.
Objectives: We want (1) to build a quality model for evaluating automated log-abstraction techniques and (2) to evaluate and recommend existing automated log-abstraction techniques using this quality model.
Method: We perform a systematic literature review (SLR) of automated log-abstraction techniques. We review 89 research papers out of 2,864 initial papers.
Results: Through this SLR, we (1) identify 17 automated log-abstraction techniques, (2) build a quality model composed of seven desirable aspects: mode, coverage, delimiter independence, efficiency, scalability, system knowledge independence, and parameter tuning effort, and (3) make recommendations for researchers on future research directions.
Conclusion: Our quality model and recommendations help researchers learn about the state-of-the-art automated log-abstraction techniques, identify research gaps to enhance existing techniques, and develop new ones. We also support software engineers in understanding the advantages and limitations of existing techniques and in choosing the suitable technique to their unique use cases.
As the information technology industry advances, the demand for log anomaly detection, based solely on printed log text, is growing. However, identifying anomalies in rapidly accumulating logs ...remains a challenging task. Traditional anomaly detection models require dataset-specific training, leading to corresponding delays. Notably, most methods only focus on sequence-level log information, complicating the detection of subtle anomalies, and often involve inference processes that are difficult to utilize in real-time. We introduce a new retrieval-based log anomaly detection model, capitalizing on the inherent features of log data for real-time anomaly detection. Our model treats logs as natural language, extracting representations with pre-trained language models. Categorizing logs based on system context, we implement a retrieval-based reformulation to contrast test logs with the most similar normal logs. This strategy not only obviates the need for log-specific training but also incorporates token-level information, ensuring refined detection, particularly for unseen logs. We also propose the core set technique, reducing computational costs for comparison. In our experiments on three representative benchmarks, we obtained an average f1-score of 0.9738, demonstrating that our model performs competitively with existing models without training on log data. Through various research questions, we verified real-world usability, including real-time detection.
The Baxter numbers \(B_n\) enumerate a lot of algebraic and combinatorial objects such as the bases for subalgebras of the Malvenuto-Reutenauer Hopf algebra and the pairs of twin binary trees on ...\(n\) nodes.
The Turán inequalities and higher order Turán inequalities are related to the Laguerre-Pólya (\(\mathcal{L}\)-\(\mathcal{P}\)) class of real entire functions, and the \(\mathcal{L}\)-\(\mathcal{P}\) class has a close relation with the Riemann hypothesis. The Turán type inequalities have received much attention.
In this paper, we are mainly concerned with Turán type inequalities, or more precisely, the log-behavior, and the higher order Turán inequalities associated with the Baxter numbers. We prove the Turán inequalities (or equivalently, the log-concavity) of the sequences \(\{B_{n+1}/B_n\}_{n\geqslant 0}\) and \(\{\hspace{-2.5pt}\sqrtn{B_n}\}_{n\geqslant 1}\).
Monotonicity of the sequence \(\{\hspace{-2.5pt}\sqrtn{B_n}\}_{n\geqslant 1}\) is also obtained. Finally, we prove that the sequences \(\{B_n/n!\}_{n\geqslant 2}\) and \(\{B_{n+1}B_n^{-1}/n!\}_{n\geqslant 2}\) satisfy the higher order Turán inequalities.
A feature-based log parsing method is presented for extracting log events from unstructured free-text logs. It is a data-driven log analytic solution with no training data needed and suitable for ...various types of log parsing tasks. Experiments show that the proposed method can achieve higher accuracy and lower time complexity in large-scale log data than existing log parsing methods.
Globally F-regular and log Fano varieties Schwede, Karl; Smith, Karen E.
Advances in mathematics (New York. 1965),
06/2010, Letnik:
224, Številka:
3
Journal Article
Recenzirano
Odprti dostop
We prove that every globally
F-regular variety is log Fano. In other words, if a prime characteristic variety
X is globally
F-regular, then it admits an effective
Q
-divisor Δ such that
−
K
X
−
Δ
is ...ample and
(
X
,
Δ
)
has controlled (Kawamata log terminal, in fact globally
F-regular) singularities. A weak form of this result can be viewed as a prime characteristic analog of de Fernex and Hacon's new point of view on Kawamata log terminal singularities in the non-
Q
-Gorenstein case. We also prove a converse statement in characteristic zero: every log Fano variety has globally
F-regular type. Our techniques apply also to
F-split varieties, which we show to satisfy a “log Calabi–Yau” condition. We also prove a Kawamata–Viehweg vanishing theorem for globally
F-regular pairs.
Logs happen every day. They record all kinds of events of current network and its management facilities. Communication dispatchers can use it to aware network or facility failures. In the purpose of ...improving awareness of communication network and its management facilities, this research is focusing on a log management system for different types of communication devices and network management servers. From log system architecture, to actual situations of log collection. Then, the distribution of system, from bare-metal servers to virtual machines. Most a virtualized log management system is deployed and tested in a dispatching center of power grid.
•Innovative use of autoencoders to reconstruct missing values in event logs.•Focus on anomalous and missing information at the level of event log attributes.•Methods tested on real life and ...artificial event logs.•Qualitative evaluation of impact on process discovery is also presented.
Low quality of business process event logs, as determined by anomalous and missing values, is often unavoidable in practical contexts. The output of process analysis that uses event logs with missing and anomalous values is also likely to be of low quality, thus decreasing the quality of any decisions based on it. While previous work has focused on reconstructing missing events in an event log or removing anomalous traces, in this paper we focus on detecting anomalous values and reconstructing missing values at the level of attributes in event logs. We propose methods based on autoencoders, which are a class of neural networks that can reconstruct their own input and are particularly suitable to learn a model of the complex relationships among attribute values in an event log. These methods do not rely on any a-priori knowledge about the business process that generated an event log and are evaluated using real world and artificially-generated event logs. The paper also discusses a qualitative analysis of the impact of event log cleaning and reconstruction on the output of process discovery. The proposed approach shows remarkable performance regarding activity labels and timestamps in artificial event logs. The performance in the case of real world event logs, in particular timestamp anomaly detection, is lower, which may be due to high variability of attribute values in the chosen event logs. Process models discovered from reconstructed event logs are characterised by lower variability of allowed behaviour and, therefore, are more usable in practice.
System event logs have been frequently used as a valuable resource in data-driven approaches to enhance system health and stability. A typical procedure in system log analytics is to first parse ...unstructured logs to structured data, and then apply data mining and machine learning techniques and/or build workflow models from the resulting structured data. Previous work on parsing system event logs focused on offline, batch processing of raw log files. But increasingly, applications demand online monitoring and processing. As a result, a streaming method to parse unstructured logs is needed. We propose an online streaming method Spell, which utilizes a longest common subsequence based approach, to parse system event logs. We show how to dynamically extract log patterns from incoming logs and how to maintain a set of discovered message types in streaming fashion. An enhancement to find more accurate message types is also proposed. We also propose and evaluate a method to automatically discover semantic meanings for parameter fields identified by Spell. We compare Spell against state-of-the-art methods to extract patterns from system event logs on large real data. The results demonstrate that, compared with other log parsing alternatives, Spell shows its superiority in terms of both efficiency and effectiveness.
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