Lightbend, Inc.

Cisco Technology, Inc.

Acante, Inc.

Dhruv Hemchand Jain

Omer Emre Velipasaoglu

Amit Sasturkar

Vishal Surana

Arun Kejariwal

Uday K Chettiar

Mohamed A Abdelhafez

Sanchay Harneja

Udayakumar Srinivasan

Sanjay Sundaresan

Chien-Ju Lo

Abhishek Das

Anusha Kasa

Sarangan Rangachari

Advait Abhay Dixit

A validating service of a plurality of services that compose an application receives a security token that identifies an entity that has submitted a transaction to the application, the security token indicating that the entity is authorized to submit the transaction to the application. The validating service obtains a transaction identifier that uniquely identifies the transaction. The validating service sends, to a collector service, the transaction identifier and data derived from the security token that identifies the entity. A downstream service receives input data associated with the transaction, the input data including the transaction identifier. The downstream service accesses an information source to obtain information. The downstream service sends, to the collector service, the transaction identifier and metadata about the information.

Sensitive data classification for micro-service applications

The technology disclosed relates to building ensemble analytic rules for reusable operators and tuning an operations monitoring system. In particular, it relates to analyzing a metric stream by applying an ensemble analytical rule. After analysis of the metric stream by applying the ensemble analytical rule, quantized results are fed back for expert analysis. Then, one or more type I or type II errors are identified in the quantized results, and one or more of the parameters of the operators are automatically adjusted to correct the identified errors. The metric stream is further analyzed by applying the ensemble analytical rule with the automatically adjusted parameters.

Tuning context-aware rule engine for anomaly detection

Systems, methods, and computer-readable media for identifying and categorizing epoch events between a first epoch and a second epoch. Epoch event data for a first epoch and a second epoch is retrieved. The retrieved epoch event data is categorized to determine event category, specific event, and respective object identification. The categorized first and second epoch event data is then labeled to identify new, resolved, and persistent epoch events over multiple epochs.

Epoch comparison for network events

The technology disclosed relates to detecting anomalous behavior of network components in a complex network setting. In particular, it relates to processing a historical stream of latency sample points by applying a wide aperture temporal aggregator that summarizes historical stream in a reduced number of summary points, based on a moving window, with a metric date-time indicator associated with each summary point, processing a current stream of latency sample points, including, applying a narrow aperture temporal aggregator that summarizes the current stream in a reduced number of summary points, based on a moving window, comparing summary points from the current stream to summary points from the historical stream at a date-time indicator with a predetermined periodic-temporal relationship to a particular current stream summary point, and quantizing the comparison to at least three comparison values of low-range, normal and high-range and recording at least low-range and high-range quantized comparisons for analysis.

Context-aware rule engine for anomaly detection

Growing community of inventors

Santa Clara, CA, United States of America

Dhruv Hemchand Jain