Lightbend, Inc.

Yahoo! Inc.

Enkata Technologies, Inc.

Omer Emre Velipasaoglu

Amit Sasturkar

Vishal Surana

Abhinav Amrut Vora

Arun Kejariwal

Uday K Chettiar

Aiyesha Lowe Ma

Dhruv Hemchand Jain

Mohamed A Abdelhafez

Alan Honkwan Ngai

Benoit F Dumoulin

Hinrich H Schuetze

Oliver Kempe

Stan Stukov

Chia-Hao Yu

Narayanan Arunachalam

Elizabeth F Churchill

Su-Lin Wu

Richard Kasperski

Rosemary Ellen Jones

Alpa Jain

Jiefeng Shen

Umut Ozertem

Craig David Upson

Henry Feild

Jeffrey Bardzell

Chi-Hoon Lee

Eric Crestan

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

The technology disclosed relates to learning how to efficiently display anomalies in performance data to an operator. In particular, it relates to assembling performance data for a multiplicity of metrics across a multiplicity of resources on a network and training a classifier that implements at least one circumstance-specific detector used to monitor a time series of performance data or to detect patterns in the time series. The training includes producing a time series of anomaly event candidates including corresponding event information used as input to the detectors, generating feature vectors for the anomaly event candidates, selecting a subset of the candidates as anomalous instance data, and using the feature vectors for the anomalous instance data and implicit and/or explicit feedback from users exposed to a visualization of the monitored time series annotated with visual tags for at least some of the anomalous instances data to train the classifier.

Anomaly detection using circumstance-specific detectors

The technology disclosed relates to understanding traffic patterns in a network with a multitude of processes running on numerous hosts. In particular, it relates to using at least one of rule based classifiers and machine learning based classifiers for clustering processes running on numerous hosts into local services and clustering the local services running on multiple hosts into service clusters, using the service clusters to aggregate communications among the processes running on the hosts and generating a graphic of communication patterns among the service clusters with available drill-down into details of communication links. It also relates to using predetermined command parameters to create service rules and machine learning based classifiers that identify host-specific services. In one implementation, user feedback is used to create new service rules or classifiers and/or modify existing service rules or classifiers so as to improve accuracy of the identification of the host-specific services.

Hierarchical service oriented application topology generation for a network

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 maintaining up to date software version data in a network. In particular, it relates to accessing a network topology that records node data and connection data including processes running on numerous hosts grouped into local services on the hosts, the local services running on multiple hosts grouped into service clusters and sub-clusters of service clusters, and network connections used by the service clusters to connect the hosts grouped into service connections. It further relates to collecting current software version information for the processes, updating the network topology with the current software version for particular process running on a particular host when it differs from a stored software version in the network topology, reassigning the particular host to a sub-cluster within the service cluster according to the current software version, and monitoring the updated sub-cluster within the service cluster.

Systems and methods of monitoring a network topology

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

The technology disclosed relates to sub-clustering within service clusters in real-time. In particular, it relates to accessing a network topology that records node data and connection data including processes running on numerous hosts grouped into local services on the hosts, the local services running on multiple hosts grouped into service clusters and sub-clusters of service clusters, and network connections used by the service clusters to connect the hosts grouped into service connections, wherein the node data includes software versions of the processes and process data with configuration files and clustering the multiple hosts with the service clusters into the sub-clusters based at least in part on the software versions.

Systems and methods of constructing a network topology

Disclosed is a system and method for providing search suggestions to a user based on the user's previously entered search queries. A computing device stores a global set of search suggestions. The computing device receives over a network from a user computer operated by a user one or more alphanumeric characters forming a portion of a search query. The computing device determines a search suggestion to the portion of the search query from the global set of search suggestions based on a search history of the user, the search history of the user comprising a plurality of search queries entered by the user within a predetermined period of time. The computing device transmits to the user computer the search suggestion for display by the user computer.

System and method for contextualizing query instructions using user's recent search history

Search-engine software displays a group of search results in a graphical user interface (GUI) for a search engine. The search-engine software captures positive feedback and negative feedback as to the search results from a user. Then the search-engine software determines a collective aboutness signature for the search results associated with the positive feedback and a collective aboutness signature for search results associated with the negative feedback. The search-engine software obtains a score of similarity to each of the collective aboutness signatures for a representation of each query suggestion in a group of query suggestions. Then the search-engine software separates the scored query suggestions into two or more groups, based on the similarity scores, and displays query suggestions from the groups in the GUI.

Dynamically altered search assistance

The present invention is directed towards systems and methods for providing search assistance technologies based on a user's search self-efficacy and search frustration. The method according to one embodiment of the present invention comprises receiving a search query from a user. The method then calculates the user's search self-efficacy and calculates the user's frustration with the current information task. The method then identifies a plurality of search assistance technologies based on the user's search self-efficacy and frustration. Finally, the method provides a search engine results page comprising a plurality of search results and the identified plurality of search assistance technologies.

Systems and methods for providing search assistance technologies based on user self-efficacy and search frustration

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San Francisco, CA, United States of America

Omer Emre Velipasaoglu