Qbase, LLC

Scott Lightner

Franz Weckesser

Sanjay Boddhu

Rakesh Dave

Robert Flagg

Bryan Zimmerman

Joseph Becknell

Telford S Berkey

Birali Hakizumwami

Mats Persson

A system for building a knowledge base of co-occurring features extracted from a document corpus is disclosed. The method includes a plurality of feature extraction software modules that may extract different features from each document in the corpus. The system may include a knowledge base aggregator module that may keep count of the co-occurrences of features in the different documents of a corpus and determine appropriate co-occurrences to store in a knowledge base.

Featured co-occurrence knowledge base from a corpus of documents

Methods for non-exclusionary searching within clustered in-memory databases are disclosed. The non-exclusionary search methods may allow the execution of searches where the results may include records where fields specified in the query are not populated or defined. The disclosed methods include the application of fuzzy matching and scoring algorithms, which enables the system to search, score and compare records with different schemata. This may significantly improve the recall of relevant records.

Non-exclusionary search within in-memory databases

Disclosed here is a fault tolerant architecture suitable for use with any distributed computing system. A fault tolerant architecture may include any suitable number of supervisors, dependency managers, node managers, and other modules distributed across any suitable number of nodes. In one or more embodiments, supervisors may monitor the system using any suitable number of heartbeats from any suitable number of node managers and other modules. In one or more embodiments, supervisors may automatically recover failed modules in a distributed system by moving the modules and their dependencies to other nodes in the system. In one or more embodiments, supervisors may request a configuration package from one or more dependency managers installing one or more modules on a node. In one or more embodiments, one or more modules may have any suitable number of redundant copies in the system, where redundant copies of modules in the system may be stored in separate nodes.

Fault tolerant architecture for distributed computing systems

Methods for faceted searching within clustered in-memory databases are disclosed. Faceted searching may be used to generate search suggestions. The faceted search engine may be able to use non-literal key algorithms for a partial prefix fuzzy matching and may include a feature disambiguation module. The disclosed search engine may be capable of processing large amounts of unstructured data in real time to generate search suggestions.

Method for facet searching and search suggestions

Disclosed herein are systems and methods for compressing structured or semi-structured data in a horizontal manner achieving compression ratios similar to vertical compression. Collections include structured or semi-structured data include a number of fields and are described using a schema. Fields include information having semantic similarity and are compressed using methods suitable for compressing the type of data. Data of a collection is compressed after fragmentation or may be normalized prior to compression. Data with semantic similarity is compressed using token tables and/or n-gram tables, where higher weighted, consisting of the product of frequency and length, occurring values may be stored in the lower numbered indices of the data table. Records include record descriptor bytes, field descriptor bytes, zero or more array descriptor bytes, zero or more object descriptor bytes, or bytes representing the data associated with the record. Data is indexed or compressed by a suitable module.

Data record compression with progressive and/or selective decomposition

Disclosed are pluggable, distributed computing-system architectures allowing for embedding analytics to be added or removed from nodes of a system hosting an in-memory database. The disclosed system includes an API that may be used to create customized, application specific analytics modules. The newly created analytics modules may be easily plugged into the in-memory database. Each user query submitted to the in-memory database may specify different analytics be applied with differing parameters. All analytics modules operate on the in-memory image of the data, inside the in-memory database platform. All the analytics modules, may be capable of performing on-the-fly analytics, which may allow a dynamic and comprehensive processing of search results.

Pluggable architecture for embedding analytics in clustered in-memory databases

The present disclosure relates to a method for performing automated discovery of new topics from unlimited documents related to any subject domain, employing a multi-component extension of Latent Dirichlet Allocation (MC-LDA) topic models, to discover related topics in a corpus. The resulting data may contain millions of term vectors from any subject domain identifying the most distinguished co-occurring topics that users may be interested in, for periodically building new topic ID models using new content, which may be employed to compare one by one with existing model to measure the significance of changes, using term vectors differences with no correlation with a Periodic New Model, for periodic updates of automated discovery of new topics, which may be used to build a new topic ID model in-memory database to allow query-time linking on massive data-set for automated discovery of new topics.

Method of automated discovery of new topics

A method for searching for related entities using entity co-occurrence is disclosed. Embodiments of the method may be employed in any search system that may include at least one search engine, at least one entity co-occurrence knowledge base, an entity extraction module, and at least an entity indexed corpus. The method may extract and disambiguate entities from search queries by using an entity co-occurrence knowledge base, find extracted entities in an entity indexed corpus and finally present search results as related entities of interest.

Method for searching related entities through entity co-occurrence

A method for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching is disclosed. The method may be employed in a search system that may include a client/server type architecture. The search system may include a user interface for a search engine in communication with one or more server devices over a network connection. The server device may include an entity extraction module, a fuzzy-score matching module, and an entity co-occurrence knowledge base database. In one embodiment, the search system may process a partial search query from a user and present search suggestions to complete the partial query. In another embodiment, the complete search query may be used as a new search query. The search system may process the new search query, run an entity extraction, find related entities from the entity co-occurrence knowledge base, and present said related entities in a drop down list.

Search suggestions of related entities based on co-occurrence and/or fuzzy-score matching

Methods and systems for discovering and exploring feature knowledge included in large corpora are disclosed. The described systems and methods may include the application of in-memory analytics to records, where the analytic methods applied to the records and the level of precision of the methods may be dynamically selected by a user.

Method of discovering and exploring feature knowledge

A computer system and method for automated discovery of topic relatedness are disclosed. According to an embodiment, topics within documents from a corpus may be discovered by applying multiple topic identification (ID) models, such as multi-component latent Dirichlet allocation (MC-LDA) or similar methods. Each topic model may differ in a number of topics. Discovered topics may be linked to the associated document. Relatedness between discovered topics may be determined by analyzing co-occurring topic IDs from the different models, assigning topic relatedness scores, where related topics may be used for matching/linking a feature of interest. The disclosed method may have an increased disambiguation precision, and may allow the matching and linking of documents using the discovered relationships.

Method of automated discovery of topics relatedness

A method for generating search suggestions by using fuzzy-score matching and entity co-occurrence in a knowledge base is disclosed. Embodiments of the method may be employed in any search system that may include an entity extraction computer module that may perform partial entity extractions from provided search queries, a fuzzy-score matching computer module that may generate algorithms based on the type of entity extracted and perform a search against an entity co-occurrence knowledge base. The entity co-occurrence knowledge base, which may include a repository where entities may be indexed as entities to entities, entities to topics, or entities to facts among others, may return fast and accurate suggestions to the user to complete the search query. The suggestions may include alternates to the partial query provided by the user that may enhance and save time when performing searches.

Search suggestions using fuzzy-score matching and entity co-occurrence

An in-memory database system and method for administrating a distributed in-memory database, comprising one or more nodes having modules configured to store and distribute database partitions of collections partitioned by a partitioner associated with a search conductor. Database collections are partitioned according to a schema. Partitions, collections, and records, are updated and removed when requested by a system interface, according to the schema. Supervisors determine a node status based on a heartbeat signal received from each node. Users can send queries through a system interface to search managers. Search managers apply a field processing technique, forward the search query to search conductors, and return a set of result records to the analytics agents. Analytics agents perform analytics processing on a candidate results records from a search manager. The search conductors comprising partitioners associated with a collection, search and score the records in a partition, then return a set of candidate result records after receiving a search query from a search manager.

Implementation of clustered in-memory database

Disclosed is a system and method for extending search capabilities of contentment management systems, such as SharePoint 2013&#xae;, to enable geographic and name entity based searches. Geographic and named entity searches are enabled by a content enrichment web service. The content enrichment web service calls a geotagging or a named entity tagger web service application to tag crawled managed properties as input and return geographically or entity modified managed properties as output. The system associates one or more geographically and named entity modified managed properties with content and stores this information as metadata in a SharePoint 2013&#xae; search index. Thus, the search system allows users to identify a particular geographic entity the user is interested in finding, and to receive search results directly related to that geographic entity on SharePoint 2013&#xae;.

Growing community of inventors

Leesburg, VA, United States of America

Scott Lightner