1. A method for handling log data from one or more applications, sensors or instruments, comprising: receiving heterogeneous logs from arbitrary/unknown systems or applications; generating regular expression patterns from the heterogeneous log sources using machine learning and extracting a log pattern therefrom; generating models and profiles from training logs based on different conditions and updating a global model database storing all models generated over time; tokenizing raw log messages from one or more applications, sensors or instruments running a production system; transforming incoming tokenized streams are into data-objects for anomaly detection and forwarding of log messages to various anomaly detectors; and generating an anomaly alert from the one or more applications, sensors or instruments running a production system.