Nook Digital, LLC

Mulesoft, Inc.

Barnesandnoble.com LLC

Retrevo Inc.

Aditya Vailaya

Jiang Wu

Manish Rathi

Kirk Chen

Ralph Rabbat

Michael Jason Welch

Nilesh Khandelwal

Paulo Gustavo Veiga

Leo Wong

Vipin Kumar Jain

Peter S S Wang

Robert Lincoln Lewis, Jr

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

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

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.

Indexing and searching product identifiers

A method for processing a search query according to one embodiment includes receiving a search query containing terms; looking up at least some of the terms in a search index for identifying sections of documents containing the at least some of the terms; generating a content score for each of the documents based at least in part on a number of keywords found in the sections of each document; looking up at least some of the terms in the search index for attempting to match one or more of the terms to context information in the search index, the context information being associated with at least one of the documents; generating a context score based at least in part on the matching of terms to the context information; generating a document score for each of the documents based at least in part on the content score and the context score; and outputting an indicator of at least one of the documents, or portion thereof, for the at least one of the documents having a higher document score relative to other of the documents.

A system, method and computer program product for indexing a product identifier and logical parts thereof includes receiving a product identifier; splitting the product identifier into logical parts; indexing the product identifier and the individual logical parts in an index; and storing the index, wherein the index is created by encoding the product identifier and the individual logical parts of the product identifier into separate fields of a document, wherein different weights are assigned to separate fields based on the field types such as product identifier or logical parts of the product identifier field, wherein different weights are assigned to words based on the word types including bi-words, wherein the fields and weights are encoded to word positions in the document, wherein the weights affect a score generated upon performing a query using the index. A system, method and computer program product for processing a search query is also presented.

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San Jose, CA, United States of America

Aditya Vailaya