Nook Digital, LLC

Mulesoft, Inc.

Barnesandnoble.com LLC

Retrevo Inc.

Salesforce Com, Inc.

Extreme Networks, Inc.

Jiang Wu

Aditya Vailaya

Manish Rathi

Kirk Chen

Ralph Rabbat

Michael Jason Welch

Nilesh Khandelwal

Ignacio Agustin Manzano

Andrew Craig Bragdon

Agustin Lopez Gabeiras

Diego Gabriel Larralde

Gaston Alberto Lodieu

Leo Wong

Paulo Gustavo Veiga

Leandro Damián Lück

Vipin Kumar Jain

Manish M Rathi

Peter S S Wang

Natalie Forood

Robert Lincoln Lewis, Jr

Jeff Ronne

A system is disclosed. The system includes a processor configured to: receive a set of data structured in a schemaless data representation; automatically translate the set of data into a relational representation by: translating an array map value in the set of data into an ordered multi-map; and converting the ordered multi-map to the relational representation. The processor is further configured to store the translated set of data in a key-value data store for a query-based retrieval.

Schemaless to relational representation conversion

Disclosed herein are system, method, and computer program product embodiments for providing anomaly feedback monitoring and detection. An embodiment operates by determining a first set of data corresponding to an anomaly indicating an undesirable data state for a first application. A subset of data from a second set of data corresponding to the undesirable data state is identified, wherein the second set of data is associated with communications between the first application and a second application. A notification identifying the anomaly is provided. Feedback associated with the anomaly is received. Data corresponding to the anomaly is updated based on the feedback.

Anomaly feedback monitoring and detection system

An application network is monitored using a plurality of agents. Adaptive event aggregation is performed to determine retaining values for an aggregation dimension. A report of the application network is generated based on the aggregation dimension.

Adaptive event aggregation

Disclosed herein are system, method, and computer program product embodiments for providing clustering and monitoring functionality. An embodiment operates by determining that an application programming interface (API) call has been made from a first application to a second application. Metric data regarding a performance of one or more computing devices responsive to the determined API call is received. The received metric data associated with the determined API call is clustered into one of a plurality of predetermined clusters associated with the performance of the one or more computing devices responsive to one or more previous API calls. A notification indicating a system state of the one or more computing devices is determined based on the clustering, and provided.

Clustering and monitoring system

A data object is received for storage in a key-value store. A partitioning token prefix is generated for the data object. A logical key for the data object is determined. A partitioning key is generated based at least in part on combining the partitioning token prefix and the logical key. Data associated with the data object is stored in the key-value store based on the partitioning key.

Balanced key range based retrieval of key-value database

A set of data is received that includes a plurality of event data objects or a plurality of meta data objects, wherein a structure of the set of data is a schemaless data representation. The set of data is automatically translated into a relational representation. The translated set of data is stored in a key-value data store for a query-based retrieval.

Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.

Systems and methods for programatically classifying text using topic classification

A method for processing a search query according to one embodiment includes receiving a search query containing terms; combining at least some consecutive terms in the search query to create biwords; looking up at least some of the terms and biwords in a search index for identifying sections of documents containing the at least some of the terms and/or biwords; generating a content score for each of the identified sections based at least in part on a number of the terms and biwords found in the sections of each document, wherein the biwords are given a higher priority than matched terms, wherein the priority affects the content score; and selecting and outputting an indicator of at least one of the sections, or portion thereof, based at least in part on the content score.

Indexing and search query processing

Systems and methods for programmatically causing a machine to classify and extract the meaning of text are discussed herein. Some embodiments may provide for a system including circuitry configured to identify topics associated with a block of text and one or more categories for each of the topics. Each unique category across the one or more categories may be further associated with one or more levels of parent and/or child categories to form an expanded category set of category nodes having parent-child relationships. Based on a number of category nodes connected to each unique category, the circuitry may be configured to determine one or more filtered categories from the unique categories and one or more filtered topics. Filtered topics or categories may be used to programmatically classify text with a more relevant data set than may be possible without the filtering.

Systems and methods for programatically classifying text using category filtration

A method for analyzing and indexing an unstructured or semistructured document according to one embodiment includes receiving an unstructured or semistructured document; converting the document to one or more text streams; analyzing the one or more text streams for identifying textual contents of the document; analyzing the one or more text streams for identifying logical sections of the document; associating the textual contents with the logical sections; indexing the textual contents and their association with the logical sections; and saving a result of the indexing in a data storage device.

Unstructured and semistructured document processing and searching

A system, method and computer program product for processing a search query includes receiving a search query containing one or more terms; processing the query to add one or more bi-words as terms to the query; searching a search index embodied on a non-transitory computer-readable storage medium having product identifiers and logical parts of the product identifiers indexed into different fields in the index; generating a score based on at least some of the terms matching the product identifiers and the individual logical parts of the product identifiers in the different fields in the index, wherein bi-words are weighted higher than the terms having only one word and individual terms are weighted higher when matching in the product identifier fields of the index; and selecting and outputting an indicator of product identifiers ranked by their scores.

Growing community of inventors

Union City, CA, United States of America

Jiang Wu