LogAI is a free log intelligence and analytics library that supports many log analytics and intelligence tasks. It is compatible with multiple record formats and has an interactive graphical user interface. LogAI provides a unified prototyping interface for common statistical models, time series models, and deep learning, making it easy to measure deep learning algorithms for log anomaly detection.
The logs generated by computer systems contain key information that helps developers understand system behavior and identify problems. Traditionally, log analysis has been done manually, but AI-based log analysis automates tasks such as log analysis, summarization, clustering, and anomaly detection, making the process more efficient. Different roles in academia and industry have different requirements for log analysis. For example, machine learning researchers must quickly compare experiments to public log data sets and reproduce results from other research groups to develop new log analysis algorithms. Industrial data scientists need to run existing log analysis algorithms on their log data and choose the best algorithm combination and configuration as a log analysis solution. Unfortunately, there are no open source libraries that can meet all of these requirements. Therefore, LogAI was introduced to meet these needs and perform better log analysis for various academic and industrial use cases.
The lack of comprehensive AI-based log analysis in log management platforms creates challenges for standardized analysis due to the need for a standardized log data model, redundancy in pre-processing, and workflow management machinery. Reproducing experimental results is challenging, requiring dedicated analysis tools for various record formats and schemas. Different log analysis algorithms are implemented in separate pipelines, which increases the complexity of managing trials and benchmarks.
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LogAI consists of two main components, the LogAI core library and the LogAI GUI. The LogAI GUI module allows users to connect to log analysis applications in the core library and visualize analysis results interactively through a graphical user interface. On the other hand, the LogAI core library consists of four distinct layers:
the data layer In LogAI it consists of data loaders and a unified log data model defined by OpenTelemetry. It also provides several data loaders to convert raw log data into LogRecordObjects in a unified format.
the pre-treatment layer LogAI cleans and splits logs using preprocessors and breakers. Preprocessors extract entities and separate records into unstructured logical lines and properties of structured record while splitters aggregate records into events for machine learning models. Custom preprocessors and cutters are available for specific open-log datasets and can be extended to support other log formats.
the information extraction layer From LogAI that converts log records into vectors for machine learning. It has four components: log parser, log vector, class encoder, and feature extractor.
the analysis layer It contains modules for performing analysis 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 measure them on common log datasets. Results show that it performs equally or better than Deep Log, with a supervised two-way LSTM model providing the best performance.
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Niharika is a Technical Consultant Intern at Marktechpost. She is a third year undergraduate student and is currently pursuing a Bachelor of Technology degree from Indian Institute of Technology (IIT), Kharagpur. She is a highly motivated person with a keen interest in machine learning, data science, and artificial intelligence and an avid reader of the latest developments in these areas.
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