LogAI is a free library for log analysis and intelligence that supports various log analysis and intelligence tasks. It is compatible with multiple log formats and has an interactive graphical user interface. LogAI provides a unified model interface for general statistical, time series, and deep learning models to facilitate benchmarking of deep learning algorithms for log anomaly detection.
Logs generated by computer systems contain important information that helps developers understand system behavior and identify problems. Log analysis is traditionally done manually, but AI-based log analysis automates tasks such as log parsing, summarization, clustering, and anomaly detection, making the process more efficient. Log analysis requirements vary by role in academia and industry. For example, machine learning researchers need to quickly benchmark their experiments against public log datasets, reproduce results from other research groups, and develop new log analysis algorithms. Industrial data scientists should run existing log analysis algorithms on log data and choose the best combination of algorithms and configurations for their log analysis solution. Unfortunately, there is no existing open source library that can meet all of these requirements. Therefore, LogAI was introduced to address these needs and better perform log analysis for various academic and industrial use cases.
The lack of comprehensive AI-based log analytics in log management platforms presents challenges for integrated analytics as it requires integrated log data models, pre-processing redundancy, and workflow management mechanisms. Experimental results are difficult to reproduce and require customized analysis tools for different log formats and schemas. Various log analysis algorithms are implemented in separate pipelines, complicating the management of experiments and benchmarks.
LogAI consists of two main components: LogAI Core Library and LogAI GUI. The LogAI GUI module allows users to connect to core library log analysis applications and interactively visualize analysis results through a graphical user interface. The LogAI core library, on the other hand, consists of four different layers.
of data layer in LogAI consists of a data loader and a unified log data model defined by OpenTelemetry. It also provides various data loaders for converting raw log data into standardized forms of LogRecordObjects.
of Pretreatment layer LogAI uses preprocessors and partitioners to clean up and split logs. Preprocessors extract entities and split records into unstructured loglines and structured log attributes, while partitioners group logs into events for machine learning models. Customized preprocessors and partitioners are available for specific open log datasets and can be extended to support other log formats.
of information extraction layer of LogAI converts log records into vectors for machine learning. It has four components: log parser, log vectorizer, categorical encoder, and feature extractor.
of Analysis layer Contains modules for performing analytical tasks with a unified interface for multiple algorithms.
LogAI uses deep learning models such as CNN, LSTM, and Transformer to detect log anomalies and can benchmark them on common log datasets. The results show that the supervised bidirectional LSTM model performs as well as or better than the deep logizer by providing the best performance.
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Niharika is a technical consulting intern at Marktechpost. She is in her third year of undergraduate studies and is currently completing her Bachelor’s degree at the Indian Institute of Technology (IIT), Kharagpur. She is a very passionate person who has a keen interest in machine learning, data her science, AI and avid reader of the latest developments in these fields.
