Systems and methods are configured to generate a set of related document objects for a predictive entity and/or to generate an optimal document sequence for a set of related document objects. In one embodiment, a set of related document objects for a predictive entity is generated by processing entity metadata features associated with the predictive entity using an entity-document correlation machine learning model, and an optimal document sequence is generated for the set of related document objects by processing the set of related document objects using a document sequence optimization machine learning model.

A method is provided that stores, in a second memory, an index structure including, for each given word from among words included in documents, a group of document IDs of documents including the given word. The method stores an index structure subset in a main memory which is faster than secondary memory. The method acquires a keyword and identifies any documents including the keyword. The method finds top-K frequent words among the words included in the identified documents by: identifying, for each given group in descending order of the number of the documents IDs therein, the number of documents IDs of the identified documents in the given group, from the subset when the number of document IDs in the given group is within the range, and from the index structure otherwise; and presenting words of top-K groups with a largest amount of the document IDs identified.

Methods, systems and computer-readable media enable various techniques related to semantic navigation. One aspect is a technique for displaying semantically derived facets in the search engine interface. Each of the facets comprises faceted search results. Each of the faceted search results is displayed in association with user interface elements for including or excluding the faceted search result as additional search terms to subsequently refine the search query. Another aspect automatically infers new metadata from the content and from existing metadata and then automatically annotates the content with the new metadata to improve recall and navigation. Another aspect identifies semantic annotations by determining semantic connections between the semantic annotations and then dynamically generating a topic page based on the semantic connections.

A computer-implemented method is provided. The method includes: acquiring, by one or more computers, a first input including a first text and a question set associated with the first text, wherein the first input includes a first separation identifier for separating a plurality of questions in the question set; determining, by one or more computers, a question index for indicating a position of the first separation identifier in the first input, and a question mask for the question set, wherein the question mask is configured to screen the question set in the first input; and based on the question index, the question mask and a reading comprehension model, determining, by one or more computers, a first output corresponding to the first input for generating a plurality of answers corresponding to the plurality of questions respectively.

Aspects of the current patent document include systems and methods to perform search in an index system. In one embodiment, an index system may be implemented in an object storage. A distributed database index is used in conjunction with the object storage. In some cases, data stored in the distributed database may be encrypted and moved to object storage. The object storage stores a plurality of blocks containing words. Each block can contain a large number of words, such as one million words.

Methods and apparatus consistent with the invention provide the ability to organize and build understandings of machine data generated by a variety of information-processing environments. Machine data is a product of information-processing systems (e.g., activity logs, configuration files, messages, database records) and represents the evidence of particular events that have taken place and been recorded in raw data format. In one embodiment, machine data is turned into a machine data web by organizing machine data into events and then linking events together.

A computing server may receive master data, transaction data, and one or more existing process models of a domain. The computing server may aggregate, based on domain knowledge ontology of the domain, the master data and the transaction data to generate a fact table. For example, entries in the fact table may be identified as relevant to the target process model and include attributes and facts that are extracted from master data or transaction data. The computing server may convert the entries in the fact table into vectors. The computing server inputting vectors into one or more machine learning algorithms to generate one or more algorithm outputs. One or more algorithm outputs may correspond to one or more improved process models that are optimized compared to the existing process models. The computing server may provide the improved process model to the domain to replace one of the existing process models.

A mobile apparatus receives a route response comprising an encoded route and one or more delay encoding data structures. The delay encoding data structures are probabilistic data structures configured to not provide false negatives. The mobile apparatus determines a decoded route based on the encoded route and a mobile version of a digital map; determines an expected traffic delay for at least one adjacent traversable map element (TME) of the decoded route based on the one or more delay encoding data structures; and performs one or more navigation functions based at least on the expected traffic delay for the at least one adjacent segment of the decoded route. An adjacent TME is a TME of the digital map that intersects the decoded route and is not a TME of the decoded route.

A network apparatus determines a route from an origin traversable map element (TME) to a target TME. The route comprises a list of route TMEs to be traveled from the starting location to the target location. The network apparatus identifies adjacent TMEs to the route, wherein an adjacent TME is a TME of the digital map that intersects the route and is not a route TME; determines an expected traffic delay for each adjacent TME based on traffic data; separates the adjacent TMEs into a plurality of delay groups based on the corresponding expected traffic delays; generates delay encoding data structures; and provides the delay encoding data structures and information identifying the route. Each delay encoding data structure encodes a map version agnostic identifier for the adjacent TMEs of one of the delay groups and is a probabilistic data structure configured to not provide false negatives.

A network apparatus determines a route from an origin TME to a target TME based on map data of a network version of a digital map. The route includes a list of route TMEs to be traveled from the origin TME to the target TME. The network apparatus accesses map version agnostic information identifying each TME of the list of route TMEs from the network version of the digital map; generates a map version agnostic identifier for each route TME of the list of route TMEs based on the accessed information; evaluates coding functions based at least on the map version agnostic identifier for each route TME to generate a coded identifier for each route TME; generates an encoding data structure based on the coded identifiers for the route TMEs; and provides the encoding data structure. The encoding data structure is a probabilistic data structure configured to not provide false negatives.