ThoughtSpot, Inc.

First images that are screenshots from a first version of a software component are obtained. Second images that are screenshots from a second version are obtained. A collection of image deviations that includes pair-wise image deviations between pairs of images are identified. A pair of images includes a first image from the first images and a corresponding second image from the second images. An image deviation indicates a portion of the second image identified as differing from a spatially corresponding portion of the first image. The image deviations are grouped into deviation groups. At least some of the second images are associated with at least some of the deviation groups. A subset of the second images corresponding to a deviation group is output responsive to a selection of an indication of the deviation group.

Systems and methods for query generation based on a logical data model with one-to-one joins are described. For example, methods may include accessing a join graph representing tables in a database; receiving a first query; selecting a connected subgraph of the join graph that includes the two or more tables referenced in the first query; accessing an indication that a directed edge of the connected subgraph corresponds to a one-to-one join; modifying the connected subgraph based on the indication to obtain a modified subgraph; generating one or more leaf queries based on the modified subgraph; generating a query graph that specifies joining of results from queries based on the one or more leaf queries; invoking a transformed query on the database that is based on the query graph and the queries based on the one or more leaf queries.

Automatic data modeling in a low-latency data access and analysis system includes identifying an analytical-object in response to first data expressing usage intent, generating an analytical model generation data-query for the analytical-object, obtaining a trained analytical model generated in accordance with the analytical model generation query and trained using results data obtained in accordance with the analytical-object, generating a resolved-request representing second data expressing usage intent and indicating a request for results data obtained using the trained analytical model, generating an analytical model results data-query for obtaining the results data in accordance with the trained analytical model and the analytical-object, and outputting data for presenting a visualization of the results data obtained by executing the analytical model results data-query, wherein a first portion of the results data corresponds with the analytical-object and a second portion of the results data corresponds with the trained analytical model.

A current set of context features for a database query that is associated with a string is identified. The database query includes a sequence of tokens of a database syntax, and the current set of context features includes words from the string and tokens from the database query. An inference record is selected from an inference store based on a comparison of the current set of context features to context features of inference records in the inference store. The database query is modified using a resolution of the inference record to obtain an inferred database query. The resolution includes one or more tokens of the database syntax. A search of a database is invoked using a query based on the inferred database query to obtain search results.

Intent-resolution using a phrase index may include obtaining data expressing a usage intent, the data expressing the usage intent including an unresolved data portion, identifying a phrase fragment based on the data expressing the usage intent and a defined phrase pattern, the phrase fragment including the unresolved data portion, identifying, by a processor, an indexed phrase as by searching a phrase index based on the phrase fragment, wherein the indexed phrase at least partially matches the phrase fragment in accordance with the defined phrase pattern, in response to identifying the indexed phrase, obtaining a resolved request representing the data expressing the usage intent in accordance with the indexed phrase, generating a data query in accordance with the resolved request and a defined structured query language, obtaining results data responsive to execution of the data query by a database that implements the defined structured query language, and outputting the results data.

Generating object morphisms during object search includes obtaining object-search request data, wherein the object-search request data includes object-search terms, obtaining resolved-request data representing the object-search terms, determining that a first analytical object partially consistent with the resolved-request data is available, wherein the first analytical object is consistent with a first portion of the resolved-request data, generating candidate object-morphism data with respect to the first analytical object in accordance with a second portion of the resolved-request data, outputting object-search response data including the candidate object-morphism data for presentation to a user, obtaining data indicating a selected object morphism from the candidate object-morphism data, generating a second analytical object in accordance with the first analytical object and the selected object morphism, wherein the second analytical object differs from the first analytical object, and outputting response data including the second analytical object for presentation to the user.

A shutdown criterion is determined to be met with respect to a cluster. Responsive to determining that the shutdown criterion is met, a request router is configured to route requests addressed to the cluster to a destination that indicates that the cluster is unavailable. A request to shut down the cluster is transmitted. A request to start the cluster is received. The request is initiated via the destination that indicates that the cluster is unavailable. In response to receiving the request to start the cluster, the cluster is started and the request router is configured to direct, to the cluster, requests addressed to the cluster.

Semantic analysis to resolve ambiguous user input data with respect to a request for data includes identifying tokens based on the text string, identifying a tables in a database, wherein a token indicates a column from a table and a token indicates a column from another table, identifying candidate join paths for joining tables, obtaining respective candidate results using the candidate join paths, outputting combined candidate results including values from the respective candidate results, obtaining second user input data indicating a selected value, identifying the request for data as unambiguous and identifying a selected join path based on the selected value, and, in response to identifying the request for data as unambiguous, outputting data responsive to the request for data using the selected join path.

A metric predictor model for predicting values of a data metric is selected. The metric predictor model is trained using historical data related to the data metric. A predicted value of the data metric is obtained using the metric predictor model. A current value of the data metric is obtained using data other than the historical data. A difference between the predicted value and the current value is determined to meet a reporting criterion. In response to determining that the difference meets the reporting criterion, a notification descriptive of the difference is output.

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.