Thoughtspot, Inc.

Rakesh Kothari

Archit Bansal

Amit Prakash

Manikanta Balakavi

Tushar Mahale

Siva Singaram

Ravi Tandon

Vaibhav Agarwal

Ashish Shubham

Pulkit Arora

Vishal Kasera

Ramnik Jain

Donko Donjerkovic

Manikanta Venkata Rahul Balakavi

Simranjyot Singh Gill

Sanchit Gupta

Vishwas Sharma

Naman Shah

Sandeep Kumar

Amitabh Singhal

Ajeet Singh

Aditya Viswanathan

Ben Limonchik

Pavan Ram Piratla

Alonzo Canada

Maneesh Apte

Naresh Kumar

Akshay Agrawal

Nikhil Yadav

Jasmeet Singh Jaggi

Brian Lee

Hungpin Kao

Rahul Manikanta Balakavi

Rahul Paliwal

Romil Gadia

Zhiquan Sui

Swati Kwatra

Vaibhav Aggarwal

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.

Generating object morphisms during object search

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.

Query generation based on a logical data model with one-to-one joins

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.

Phrase translation for a low-latency database analysis system

Operating a low-latency database analysis system using domain-specific chronometry may include obtaining chronometry configuration data including chronometric instance data describing an instance of a chronometric unit of a domain-specific chronometry dataset that describes an era, such that the chronometry configuration data includes respective chronometric instance data describing each instance of the first chronometric unit of the domain-specific chronometry dataset for the era of the domain-specific chronometry dataset, generating, in the low-latency database analysis system, a domain-specific chronometry dataset in accordance with the chronometry configuration data, such that the domain-specific chronometry dataset describes a chronometric unit such that a temporal location expressed with reference to the chronometric unit and indicative of an epoch value differs from a temporal location indicative of the epoch value and expressed in accordance with a canonical chronometry, and storing the domain-specific chronometry dataset in the low-latency database analysis system.

Dynamic chronometry data orientation

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.

Natural language question answering systems

Object scriptability includes receiving a high-level language script describing at least one data-analysis object, including a node representing the data-analysis object in a graph-based data structure including a plurality of nodes, where each node from the plurality of nodes represents a respective data-analysis object in a data analysis system, where each node from the plurality of nodes is connected to at least one other node from the plurality of nodes by an edge, and where the edge represents a relationship between the respective objects in the data analysis system.

Object scriptability

A method and system may be implemented for automatically analyzing data in a database. A method for use in a low-latency database analysis system may include generating a schema. The schema may be based on a portion of an external database. The method may include storing the schema in an in-memory database. The method may include receiving a data-query. The method may include generating a resolved-request. The resolved-request may be based on the data-query and the stored schema. The stored schema may be used for executing the query on the external database. The method may include receiving results data responsive to the data-query from the external database. The method may include outputting the results data for display on a user interface.

Low-latency database analysis using external data sources

A low-latency database analysis system using an object index may include obtaining data expressing a usage intent, and, in response to a determination that the data expressing the usage intent includes object search request data including a user identifier and zero or more object search terms, obtaining, from an object-index responsive to the object search request data, object indexing data for an object, obtaining object detail data for the object, obtaining an object visualization capture for the object, and outputting object search response data including the object visualization capture and at least a portion of the object detail data for presentation to a user.

Object indexing

Systems and methods for query generation based on merger of subqueries are described. For example, methods may include accessing a first join graph representing tables in a database, wherein the first join graph has vertices corresponding to respective tables in the database and directed edges corresponding to join relationships; receiving a first query specification that references data in two or more of the tables of the database to specify multiple subqueries in a set of subqueries; checking that two or more subqueries from the set of subqueries have the same join graph; checking that the two or more subqueries have the same set of grouping columns; responsive, at least in part, to the two or more subqueries having the same join graph and the same set of grouping columns, merging the two or more subqueries to obtain a consolidated query.

Query generation based on merger of subqueries

Systems and methods for conversational user experiences and conversational database analysis disclosed herein improve the efficiency and accessibility of low-latency database analytics. The method may include obtaining data expressing a usage intent with respect to the low-latency database analysis system, wherein the data expressing the usage intent includes a current request string expressed in a natural language, a current context associated with the current request string, and a previously generated context associated with a previously generated resolved-request, identifying, from the current request string, a conversational phrase corresponding to a conversational phrase pattern from a defined set of conversational phrase patterns, generating a resolved-request based on the identified conversational phrase, including the resolved-request in the current context, obtaining results data responsive to the resolved-request from a distributed in-memory database, generating a response including the results data and the current context, and outputting the response.

Conversational database analysis

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.

Phrase indexing

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Rakesh Kothari