Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates to in-memory databases or search engines using a dependency manager or configuration manager for maintaining configuration in the database system. The system may include a supervisor that may request and receive data from dependency manager, where the supervisor may be linked to other components in the system. The dependency manager may be used as a container for data metadata, and software components, which may be used in the system configuration. The configuration may be developed through a dependency system, where the dependency manager may keep an entire dependency tree for all software and data in the system. Similarly, dependency manager may create a deployable package to guarantee deployment integrity and to ensure a successful execution of any suitable software and data in the system.

Abstract: The present disclosure relates to a method of alerting users regarding newly disambiguated features. More specifically, a newly disambiguated feature may pass through different filters/restrictions, such as, the known knowledge base. The disclosed known knowledge base may filter the newly disambiguated feature, comparing the newly disambiguated features to the existing features to discover a new feature of interest. Particularly, the disclosed new feature of interest may include a new person, a new phone number, a new place, a new company, among others. Finally, if there is a new feature that did not match with the existing disambiguated features in the known knowledge base, then an alert may be emitted to a user.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates to in-memory databases or search engines using a dependency manager or configuration manager for maintaining configuration in the database system. The system may include a supervisor that may request and receive data from dependency manager, where the supervisor may be linked to other components in the system. The dependency manager may be used as a container for data metadata, and software components, which may be used in the system configuration. The configuration may be developed through a dependency system, where the dependency manager may keep an entire dependency tree for all software and data in the system. Similarly, dependency manager may create a deployable package to guarantee deployment integrity and to ensure a successful execution of any suitable software and data in the system.

Abstract: Disclosed here are distributed computing system connection configurations having multiple connection bandwidth and latency tiers. Also disclosed are connection configurations including a suitable number of network segments, where network segments may be connected to external servers and clusters including search managers, analytics agents, search conductors, dependency managers, supervisors, and partitioners, amongst others. In one or more embodiments, modules may be connected to the network segments using a desired bandwidth and latency tier. Disclosed here are hardware components suitable for running one or more types of modules on one or more suitable nodes. One or more suitable hardware components included in said clusters include CPUs, Memory, and Hard Disk, amongst others.

Abstract: A method for entity-driven alerts based on disambiguated features, is disclosed. According to an embodiment, disclosed method may refer to entity-driven alerts based on trending or new knowledge of a disambiguated feature. The alerts may be sent to a user when new knowledge is discovered about the disambiguated feature, a new association (such as new features, facts, quotations, or topic IDs related, among others) with the feature of interest, and/or new trending changes are emerging about the feature of interest. According to various embodiments, method for entity-driven alerts based on disambiguated features may reduce the number of false positives resulting in a normal search query. Which in turn, may increase the efficiency of monitoring, allowing for broadened universe of alerts.

Abstract: A method for disambiguating features in unstructured text is provided. The disclosed method may not require pre-existing links to be present. The method for disambiguating features in unstructured text may use co-occurring features derived from both the source document and a large document corpus. The disclosed method may include multiple modules, including a linking module for linking the derived features from the source document to the co-occurring features of an existing knowledge base. The disclosed method for disambiguating features may allow identifying unique entities from a knowledge base that includes entities with a unique set of co-occurring features, which in turn may allow for increased precision in knowledge discovery and search results, employing advanced analytical methods over a massive corpus, employing a combination of entities, co-occurring entities, topic IDs, and other derived features.

Abstract: The present disclosure relates to a method of alerting users regarding newly disambiguated features. More specifically, a newly disambiguated feature may pass through different filters/restrictions, such as, the known knowledge base. The disclosed known knowledge base may filter the newly disambiguated feature, comparing the newly disambiguated features to the existing features to discover a new feature of interest. Particularly, the disclosed new feature of interest may include a new person, a new phone number, a new place, a new company, among others. Finally, if there is a new feature that did not match with the existing disambiguated features in the known knowledge base, then an alert may be emitted to a user.

Abstract: 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.

Abstract: 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.

Abstract: A method for obtaining and providing search suggestions using entity co-occurrence is disclosed. The method may be employed in any search system that may include at least one search engine, one or more databases including entity co-occurrence knowledge and trends co-occurrence knowledge. The method may extract and disambiguate entities from search queries by using an entity and trends co-occurrence knowledge in one or more database. Subsequently, a list of search suggestion may be provided by each database, then by comparing the score of each search suggestion, a new list of suggestion may be built based on the individual and/or overall score of each search suggestion. Based on the user's selection of the suggestions, the trends co-occurrence knowledgebase can be updated, providing a means of on-the-fly learning, which improves the search relevancy and accuracy.

Abstract: 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.

Abstract: 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.

Abstract: A method for disambiguating features in unstructured text is provided. The disclosed method may not require pre-existing links to be present. The method for disambiguating features in unstructured text may use co-occurring features derived from both the source document and a large document corpus. The disclosed method may include multiple modules, including a linking module for linking the derived features from the source document to the co-occurring features of an existing knowledge base. The disclosed method for disambiguating features may allow identifying unique entities from a knowledge base that includes entities with a unique set of co-occurring features, which in turn may allow for increased precision in knowledge discovery and search results, employing advanced analytical methods over a massive corpus, employing a combination of entities, co-occurring entities, topic IDs, and other derived features.

Abstract: 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.

Abstract: Disclosed here are distributed computing system connection configurations having multiple connection bandwidth and latency tiers. Also disclosed are connection configurations including a suitable number of network segments, where network segments may be connected to external servers and clusters including search managers, analytics agents, search conductors, dependency managers, supervisors, and partitioners, amongst others. In one or more embodiments, modules may be connected to the network segments using a desired bandwidth and latency tier. Disclosed here are hardware components suitable for running one or more types of modules on one or more suitable nodes. One or more suitable hardware components included in said clusters include CPUs, Memory, and Hard Disk, amongst others.