Data/Log analysis, monitoring, root cause – real-time, continuous, and predictive.

  • Categorize data rapidly: Logs can be seen as textual data, which means that NLP techniques can be applied to gather the same logs in an organized manner, making it possible to search for specific types of logs.
  • Automatically identify issues: One of the benefits of ML is that it detects issues and problems automatically, even if there’s a huge number of logs.
  • Alert critical information: Stream processing for logs would allow users to enable continuous time-series analysis for log data. With ML, it’s possible to be alerted when there’s something that deserves attention. Complex patterns could be detected and actions can be automated.
  • Early anomaly detection, auto root cause: In most disastrous events, there’s always an initial anomaly that wasn’t detected. Machine learning can detect this anomaly before it creates a major problem.

  • IOT/Manufacturing is all about relationships and dependencies, which makes graph technologies a perfect fit for discovering more information in a speedy manner.

  • Real-time analytics solutions based on the streaming database, support complex query and analysis operations in a continuous and predictive manner: Network monitoring, Predictive maintenance, Vehicle telematics, Better customer experience etc.