Abstract: A serverless data processing architecture may be a high-velocity, cost-efficient and massively scalable log ingest application and event parser that may act as the intermediary between data collection and data analysis and may handle the receipt and parsing of incoming event data generated by collector and sensor applications. The log analytics data science portion of an ingest process may determine if arbitrarily structured log data is anomalous and/or security relevant via single event or multievent algorithms. There are numerous applications for both the single and multi-event log analytics platforms. Any application involving reliably finding anomalous pieces of semi-structured or unstructured text data may provide potential use cases. These processes may be particularly widely applicable because these processes are agnostic to and require no prior knowledge of the structure of the data.

Abstract: A serverless data processing architecture may be a high-velocity, cost-efficient and massively scalable log ingest application and event parser that may act as the intermediary between data collection and data analysis and may handle the receipt and parsing of incoming event data generated by collector and sensor applications. The log analytics data science portion of an ingest process may determine if arbitrarily structured log data is anomalous and/or security relevant via single event or multievent algorithms. There are numerous applications for both the single and multi-event log analytics platforms. Any application involving reliably finding anomalous pieces of semi-structured or unstructured text data may provide potential use cases. These processes may be particularly widely applicable because these processes are agnostic to and require no prior knowledge of the structure of the data.

Abstract: A serverless data processing architecture may be a high-velocity, cost-efficient and massively scalable log ingest application and event parser that may act as the intermediary between data collection and data analysis and may handle the receipt and parsing of incoming event data generated by collector and sensor applications. The log analytics data science portion of an ingest process may determine if arbitrarily structured log data is anomalous and/or security relevant via single event or multievent algorithms. There are numerous applications for both the single and multi-event log analytics platforms. Any application involving reliably finding anomalous pieces of semi-structured or unstructured text data may provide potential use cases. These processes may be particularly widely applicable because these processes are agnostic to and require no prior knowledge of the structure of the data.

Abstract: A serverless data processing architecture may be a high-velocity, cost-efficient and massively scalable log ingest application and event parser that may act as the intermediary between data collection and data analysis and may handle the receipt and parsing of incoming event data generated by collector and sensor applications. The log analytics data science portion of an ingest process may determine if arbitrarily structured log data is anomalous and/or security relevant via single event or multievent algorithms. There are numerous applications for both the single and multi-event log analytics platforms. Any application involving reliably finding anomalous pieces of semi-structured or unstructured text data may provide potential use cases. These processes may be particularly widely applicable because these processes are agnostic to and require no prior knowledge of the structure of the data.