Adapting database queries from a common interface into conditions specific to database storage formats, optimization of a data model, and virtualization of query results from combined databases.

Embodiments may provide a cache for query results that can adapt the cache-space utilization to the popularity of the various topics represented in the query stream. For example, a method for query processing may perform receiving a plurality of queries for data and requesting data responsive to at least one query from a data cache comprising a temporal cache, wherein the temporal cache is configured to store data based on a topic associated with the data and is configured to retrieve data based on a topic, and wherein the data cache is configured to retrieve data responsive to at least one query from the computer system.

Methods, systems and computer readable media are provided for automatically creating a semantic model of a relational database for processing natural language queries. A computing device automatically extracts relational database metadata. The computing device prompts a user to enter textual labels for columns of the extracted metadata. The computing device automatically generates a schema annotation file based upon the relational database metadata and the textual labels for the columns. A natural language query is processed for the relational database using the schema annotation file.

A method includes receiving audio data corresponding to an utterance spoken by the user and captured by the user device. The utterance includes a command for a digital assistant to perform an operation. The method also includes determining, using a hotphrase detector configured to detect each trigger word in a set of trigger words associated with a hotphrase, whether any of the trigger words in the set of trigger words are detected in the audio data during the corresponding fixed-duration time window. The method also includes determining identifying, in the audio corresponding to the utterance, the hotphrase when each other trigger word in the set of trigger words was also detected in the audio data. The method also includes triggering an automated speech recognizer to perform speech recognition on the audio data when the hotphrase is identified in the audio data corresponding to the utterance.

An electronic device that includes one or more input sensor devices, one or more output devices, one or more computer processors and a memory containing computer program code that, when executed by operation of the one or more computer processors, performs an operation. The operation includes collecting information, using one or more input sensor devices, about the plurality of users within a physical environment. The operation includes analyzing the collected information to determine a present situational context for the plurality of users that are currently present within the physical environment. An action to perform is determined based on the determined present situational context. The determined action is executed using the one or more output devices.

To effectively utilize knowledge of relationship information among material property parameters the users tangibly and intangibly have in a search system that generates a graph in which material property parameters are nodes and relationships of the material property parameters are edges from a database of material property parameter pairs whose relationships are already known, and conducts a path search in the generated graph. A search system, which includes the database, a graph generator that generates the graph, and a graph searcher searches the graph, further includes a user interface and a user information storage unit corresponding to each user. The user conducts a search unique to the user by inputting relationship information between the material property parameters that he has to the user information storage unit and integrating the relationship information into the above graph. Further, by accumulating a history of searches conducted by the user in the user information storage unit and analyzing the search history, the user can be provided with new knowledge.

Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolved-request by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency database analysis system, and outputting the results data for presentation to a user.

An attribute information setting section loads information indicating whether or not access to each of a plurality of files is allowed, into a memory. A readout request receiving section receives a readout request including a file path from a program. A hash value deriving section derives a hash value of a file path included in the readout request. A file confirming section confirms whether or not the derived hash value matches with one of hash values of the files included in software. A determining section refers to the information loaded into the memory by the attribute information setting section in a case in which matching of the hash values is confirmed, to thereby determine whether or not a process on the file which has been subjected to the readout request is executable.

An approach for identifying privileged access to a database is provided. A processor receives a query plan to search the database. A processor determines the query plan includes a request that accesses privileged data. A processor generates an updated query plan with an indication of the request that accesses privileged data. A processor sends the updated query plan for an audit of the query plan.

Methods and systems for translating a natural language user query into a graph database query are described. In some instances, the methods may comprise receiving a first input from a user comprising a natural language query regarding data in a graph database; processing the natural language query using a named entity recognition (NER) machine learning model to extract named entities from the natural language query and tag them according to an entity type; processing the tagged named entities using a semantic similarity algorithm to identify corresponding nodes and edges, and their associated properties, in the graph database; processing the natural language query using an intent classification machine learning model to determine a user intent for the natural language query; and applying a user intent-based template to the identified nodes and edges to formulate a graph database query that corresponds to the natural language query.