A Generative Adversarial Network is used to train and/or tune a model used to analyze data in a database or data stream. The Generative Adversarial Network intermittently trains or tunes the model as the database is actively ingesting data and/or while the data stream is streaming. This intermittent refreshing of the model, performed by the Generative Adversarial Network, is sometimes described as “dynamic” or “dynamical.” Analytics type software is queried in order to perform normalization and/or model training.

A method includes generating a representation of each of a plurality of dialects of structured query language (SQL) statements, and receiving a first SQL statement. The first SQL statement is in a first dialect of the plurality of dialects. The method further includes generating a first output corresponding to the first SQL statement. The first output has a plurality of first data structures arranged in a first tree structure. Each of the plurality of first data structures corresponds to a portion of the first SQL statement. The method further include receiving a second SQL statement, the second SQL statement is in a second dialect of the plurality of dialects, and generating a second output corresponding to the second SQL statement. The second output has a plurality of second data structures arranged in a second tree structure. Each of the plurality of second data structures corresponds to a portion of the second SQL statement. A first data structure of the plurality of first data structures is the same as a second data structure of the plurality of second data structures.

A method includes generating a representation of each of a plurality of dialects of structured query language (SQL) statements, and receiving a first SQL statement. The first SQL statement is in a first dialect of the plurality of dialects. The method further includes generating a first output corresponding to the first SQL statement. The first output has a plurality of first data structures arranged in a first tree structure. Each of the plurality of first data structures corresponds to a portion of the first SQL statement. The method further include receiving a second SQL statement, the second SQL statement is in a second dialect of the plurality of dialects, and generating a second output corresponding to the second SQL statement. The second output has a plurality of second data structures arranged in a second tree structure. Each of the plurality of second data structures corresponds to a portion of the second SQL statement. A first data structure of the plurality of first data structures is the same as a second data structure of the plurality of second data structures.

One implementation of the present disclosure is a building system of a building including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to receive a query from a requesting system, the query including one or more query parameters and a context, the context indicating one or more purposes for the query including the one or more query parameters. The instructions cause the one or more processors to retrieve, based on the one or more query parameters, first data of the building system from a data storage system, identify, based on the context, one or more processing operations to perform to generate a processing result with the first data, perform the one or more processing operations with the first data to generate second data, and provide a response to the requesting system with the second data.

A policy determination unit acquires a rule for a request for accessing data based on a preset access control policy, and selects whether to acquire attribute information about an attribute of each record of the data from the outside of a database in which the data is stored. As a result, when selecting acquisition of the attribute information, the attribute information is acquired and the rule based on the attribute information is evaluated, and when selecting no acquisition of the attribute information, the database is caused to execute filtering of the data based on the rule. Then, based on the evaluation result of the rule or the filtering execution result, a record of the data corresponding to the access request is acquired from the database.

A policy determination unit acquires a rule for a request for accessing data based on a preset access control policy, and selects whether to acquire attribute information about an attribute of each record of the data from the outside of a database in which the data is stored. As a result, when selecting acquisition of the attribute information, the attribute information is acquired and the rule based on the attribute information is evaluated, and when selecting no acquisition of the attribute information, the database is caused to execute filtering of the data based on the rule. Then, based on the evaluation result of the rule or the filtering execution result, a record of the data corresponding to the access request is acquired from the database.

A system, method and computer program product, which given in input a question in natural language format, delivers personalized insights related to the answer. Personalized insights are selected among candidate insights mined from the data and ranked based on closeness to (mined) user-preference, relevance to the question, and surprise factor. Two core components include: Question analysis and meaningful insight look up and Multi-dimensional insight ranking. The Question analysis and meaningful insights lookup module performs a semantic analysis of the questions and, uses techniques including “templates” to build new questions which could uncover insights from the data. The Multi-dimensional insight ranking module takes in input a list of insights returned from Question analysis and meaningful insights lookup and rank such insights based on such factors as: relevance to the query, surprise factor, and user preferences.

Embodiments provide search answer extensibility by using either one or more primary applications that is executing or residing on, or is accessible to, a first computing device that receives a natural language (NL) query and/or one or more assigned applications that is executing or residing on the first computing device or on a different second computing device to process the NL query and provide a response to the NL query.

An example method of implementing a monitoring interface for an information technology environment comprises: identifying machine data reflecting activity in the information technology environment comprising a plurality of entities providing a service; executing a search query to derive, from the machine data, a value of a key performance indicator (KPI) reflecting performance of the service; and causing display of a monitoring interface including: an identifier of the service, a color coded indication of a state of the KPI, and a visual representation of time series data associated with the service.

A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.