Disclosed herein are embodiments providing query validation with automated query modification. In particular, the embodiments provide a computing system that receives a query and determines the query is sensitive. The computing system iteratively modifies the query until the query is not sensitive by modifying the query to increase a scope of the query, updating estimated query results based on the query as modified, and determining whether the query as modified is sensitive based on the estimated query results as updated. Upon determining that the query as modified is not sensitive, the computing system proceeds with the query as modified. Accordingly, the computing system improves query efficiency by automatically modifying a sensitive query.

Embodiments as disclosed herein allow simple specification of searches of values within regions and efficient implementation of such searches. Specifically, embodiments as disclosed may provide a search operator that addresses the problem of complex query construction for finding objects having a particular value, including a minimum or a maximum value, in one of a set of regions, and the efficient implementation of the searches specified by such search operators.

A system described herein may provide a technique for the compression of query terms and search data against which the query terms may be evaluated. The compression may be dynamic, in that a quantity of bits used to compress the search data and query terms may be based on a quantity of unique characters included in a given query term. The compression may further include reducing the volume of search data by compressing entire words, that do not include any of the unique characters of the query term, to one particular code.

In various embodiments, methods and systems for presenting a search interface with search query history based functionality is provided. A search query history store comprising search queries is accessed. The search query history store includes search queries executed in a search computing system. A search query comprises one or more commands. A plurality of search queries retrieved from the search query history store is displayed on the search interface using a placement style. A placement style, such as an indent style, provides a structure for separating and arranging commands of a plurality of search queries displayed. The search interface further provides for receiving a selection of at least a portion of a search query from the plurality of search queries to initiate actions or execute actions based on the selection. The search interface includes a search input interface, such as a search bar, where the selection of the portion of the search query is displayed based on a selected action.

Embodiments are directed to managing visualizations of data. A provided data model may include a tree specification that declares parent-child relationships between objects in the data model. In response to a query associated with objects in the data model: employing the parent-child relationships to determine a tree that includes parent objects and child objects from the objects based on the parent-child relationships; determining a root object based on the query and the tree; traversing the tree from the root object to visit the child objects in the tree; determining partial results based on characteristics of the visited child objects such that the partial results are stored in an intermediate table; and providing a response to the query that includes values based on the intermediate table and the partial results.

A safety case discovery system includes a scenario framework and safety protocols for edge cases. The safety case discovery system receives sensor data generated by at least one sensor during operation of a vehicle and stores the sensor data in a data warehouse. The data warehouse can be queried based on a predefined scenario description to produce a subset of records which are ranked based on a relevancy of the records to the predefined scenario description. The safety case discovery system deduplicates the ranked results to produce edge cases and updates the safety framework. The safety case discovery system can train a machine learning model vehicle based on the edge cases.

Systems and methods are disclosed for receiving, at a data intake and query system, a query that includes an indication to process data managed by a third-party data storage and processing system that supports a different query language than the data intake and query system. The data intake and query system identifies a third-party data storage and processing system that manages the data to be processed and generates a subquery for execution by the third-party data storage and processing system, generates instructions for one or more worker nodes to receive and process results of the subquery from the third-party data storage and processing system, and instructs the worker nodes to provide results of the processing to the data intake and query system.

Knowledge graph systems are disclosed for implementing multiple approaches, including stand alone or combined approaches, for knowledge graph pruning. The approaches are based on graph sampling work such as, for example, information gain theory. The approaches are applied by a knowledge graph system to perform schema pruning, automatic graph pruning, and query correlation for improving query performance.

Resource management includes storing, for multiple workload pools of a data intake and query system, a workload pool hierarchy arranged in multiple workload pool layers. After storing a processing request is assigned a selected subset of workload pools in a second layer of the workload pool hierarchy based on a type of processing request. The processing request is then assigned to an individual workload pool in the selected subset to obtain a selected workload pool. Execution of the processing request is initiated on the selected workload pool.

To provide search results to be presented in accordance with changes in the state of search performed by a user. A search device includes: a search means that searches a storage means storing records for records that match an input search condition to not less than a set relevance level and outputs search results; and a specificity inference means that, when a user operation is input after the search means has output search results corresponding to a first search condition, infers whether or not specificity of the search results is acceptable on the basis of the user operation, and, when the specificity is inferred to be acceptable, raises a relevance level that the search means uses.