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 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: 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.

Abstract: 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.

Abstract: 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.

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: A system and method for detecting events based on input data from a plurality of sources. The system may receive input from a plurality of sources containing information about possible events. A method for event detection involves pre-processing and normalizing a data input from a plurality of sources, extracting and disambiguating events and entities, associate event and entities, correlate events and entities associated from a data input to results from a different data sources to determine if an event has occurred, and store the detected events in a data storage.

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: 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: 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.