Lightbend, Inc.

Cognant LLC

Satori Worldwide, LLC

Arun Kejariwal

Omer Emre Velipasaoglu

Uday K Chettiar

Amit Sasturkar

Vishal Surana

Dhruv Hemchand Jain

Mohamed A Abdelhafez

James Koh

Owen S Vallis

Heng Wang

Francois Orsini

Boaz Sedan

Alan Honkwan Ngai

Anton Koinov

Andrey Kushnir

Ishan Upadhyaya

Cynthia Chu

Dhruv Choudhary

Nika Kolomentseva

Andrey Kolomentsev

Sujesha Sudevalayam

Anna Kareva

Gabor Mueller-Tseng

Jared Stoffan

Mick Ryan

Renee Ya

Mikhail Kuzmiankou

Alex Taylor

Harsh Singhal

William Hatzer

Craig David Upson

A method, a system, and an article are provided for detecting and managing anomalies associated with content presentations. An example computer-implemented method can include: obtaining a stream of data including a record of clicks on a plurality of client devices; aggregating the stream of data to generate a batch click stream for each of the client devices; processing the stream of data using a real-time anomaly detector to detect a first anomaly indicating that at least one of the client devices was previously associated with fraudulent clicks; processing each batch click stream using a batch anomaly detector to detect a second anomaly indicating that at least one of the batch click streams includes fraudulent clicks; and facilitating an adjustment of content presentations on the client devices, based on at least one of the detected first anomaly and the detected second anomaly.

System and method for detecting fraudulent activity on client devices

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

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing or tracking assets in a geographical area. One method includes receiving one or more first messages from a plurality of assets on one or more first channels of a plurality of channels. The one or more first messages may indicate live geographical locations of the plurality of assets located in a geographical area. The method also includes displaying, via a graphical user interface, the live geographical locations of the plurality of assets on a map of the geographical area. The method further includes receiving on a second channel a message indicating a request from a first user device to travel to a destination. The method further includes determining one or more travel routes to the destination. The one or more travel routes may use different assets to transport the first user device to the destination. The method further includes publishing on a third channel one or more second messages indicating the one or more travel routes.

Systems and methods for managing assets in a geographical location

A method, a system, and an article are provided for detecting bot users of a software application. An example method can include: providing a client application to a plurality of users; obtaining device-based data and application-based data for each user, the device-based data including a description of at least one computer component used to run the client application, the application-based data including a history of user interactions with the client application; aggregating the data to obtain a plurality of bot signals for each user; analyzing the bot signals to detect a bot among the plurality of users; and preventing the bot from accessing the client application.

System and method for bot detection

The technology disclosed relates to differential analysis of sets of time series pairs. In particular, it relates to building estimators of magnitude of difference between two time series. After the basic estimators are built, they are combined into ensemble estimators using linear or nonlinear prediction models to improve their accuracy. In one application, the ensemble is used for estimating the magnitudes of difference over sets of metric pairs observed from distributed applications and systems running over a computer network. The metric pairs are then ranked in decreasing order of magnitude of difference to guide an operator in prioritizing his root cause analysis of faults, thereby reducing the time-to-resolution of problems.

Resource and metric ranking by differential analysis

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

Fremont, CA, United States of America

Arun Kejariwal