Thoughtspot, Inc.

Amit Prakash

Ajeet Singh

Satyam Shekhar

Rakesh Kothari

Shashank Gupta

Ravi Tandon

Ashish Shubham

Sanjay Agrawal

Pavan Ram Piratla

Shikhar Agarwal

Shailesh Chauhan

Amitabh Singhal

Manikanta Balakavi

Aditya Viswanathan

Abhishek Rai

Maneesh Apte

Alonzo Canada

Prateek Gaur

Bhanu Prakash

Manikanta Venkata Rahul Balakavi

Sandeep Kumar

Archit Bansal

Manoj Krishna Ghosh

Naresh Kumar

Vaibhav Agarwal

Gunjan Jha

Antony Chuxiao Chen

Jasmeet Singh Jaggi

Priyendra Singh Deshwal

Peter Kuimelis

Pradeep Dorairaj

Stephane Antonin Kiss

Vibhor Nanavati

Vijay Krishnan Ganesan

Joy Dutta

Swati Kwatra

Zhiquan Sui

Vaibhav Aggarwal

Shikhar Agrawal

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.

Token based dynamic data indexing with integrated security

A request for database results is received from a query coordinator at a database instance of a distributed database. The request includes a query execution instruction of a query plan and an indication of override instructions corresponding to the query execution instruction. The override instructions are such that they do not modify the query plan. The database instance includes the override instructions in a set of high-level language query instructions. The database instance performs just-in-time compiling of the set of high-level language query instructions to obtain a machine language query for performing the query execution instruction of the query plan. The database instance executes the machine language query to obtain the database results. The database instance then transmits the database results to the query coordinator.

Just-in-time injection in a distributed database

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

Query execution on compressed in-memory data includes receiving, at a processor of an instance of a distributed in-memory database, a query for data from a table stored in the distributed in-memory database as compressed table data, obtaining results data responsive to the query from the table, and outputting the results data for presentation to a user. Obtaining results data includes allocating memory to identify allocated memory for decompressing the compressed table data, obtaining uncompressed table data by decompressing the compressed table data into the allocated memory, and obtaining the results data from the uncompressed table data. The allocated memory is deallocated in response to obtaining the results data. Compressing a table to form compressed table data is also described.

Query execution on compressed in-memory data

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

Injecting override instructions associated with query executions instructions performed on a distributed database includes receiving a data-query; generating, by a first database instance, a query plan that includes a first query execution instruction for transmission to a second database instance; transmitting, by the first database instance, a request for partial results to the second database instance, where the request includes the first query execution instruction and an indication of override instructions corresponding to the first query execution instruction; responsive to a determination that the request includes the indication, including, by the second database instance, the override instructions in a set of high-level language query instructions; obtaining, by the second database instance, a machine language query based on the set; executing, at the second database instance, the machine language query to obtain the partial results; and transmitting, by the second database instance, the partial results to the first database instance.

Providing a search interface for a database includes receiving string data entered via a user interface. A sequence of tokens representative of the string data is determined. Determining the sequence of tokens includes applying natural language processing to the string data. A first database query is generated in accordance with the sequence of tokens. Respective text representations for tokens in the sequence of tokens are presented via the user interface. Feedback data related to the sequence of tokens is received via the user interface. A token of the sequence of tokens is modified based on the feedback data to obtain a modified sequence of tokens. A second database query is generated based on the modified sequence of tokens. Results data are obtained from the database responsive to execution of the second database query by the database. Data based on the results data are output for presentation in the user interface.

Automatic database analysis includes identifying a current context for accessing data from a low-latency database and generating an exploration query based on the current context, which includes identifying a column from the low-latency database as a column of utility in response to determining that a probabilistic utility for the column satisfies a defined utility criterion. The current context includes a requested result set satisfying a requested search criterion, and the probabilistic utility is based on the current context. The analysis includes generating an exploration result set based on the exploration query, generating insights based on the exploration result set, ranking the insights, and outputting at least one insight based on the ranking.

Automatic database insight analysis

Data-query execution with distributed machine-language query management in a low-latency database analysis system may include obtaining, at a distributed in-memory database, a data-query expressing a request for data in a defined structured query language associated with the distributed in-memory database, automatically generating a high-level language query representing at least a portion of the data-query, obtaining a machine language query corresponding to the high-level language query, executing the machine language query to obtain results data, and outputting the results data. Obtaining the machine language query may include determining whether the machine language query is cached, and in response to a determination that the machine language query is unavailable, sending a request for the machine language query to a distributed machine-language-query management instance.

Machine language query management for low-latency database analysis system

Index sharding in a low-latency database analysis system includes obtaining index configuration data for indexing constituent data, the constituent data including a plurality of logical tables, and indexing, by an indexing unit, the constituent data by partitioning the constituent data based on a characteristic of the constituent data into at least a first partition and a second partition, segmenting the first partition into a first segment of the first partition, sharding the first segment into a first shard of the first segment of the first partition, segmenting, using hash-partitioning, the second partition into one or more segments of the second partition, and for each segment of the second partition, sharding the segment into one or more respective shards.

Index sharding

Systems and methods for query generation based on a logical data model are described. For example, methods may include accessing a first join graph representing tables in a database; receiving a first query that references data in two or more of the tables of the database; selecting a connected subgraph of the first join graph that includes the two or more tables referenced in the first query; generating multiple leaf queries that reference respective subject tables that are each a root table of the connected subgraph or a table including a measure referenced in the first query; generating a query graph that specifies joining of results from queries based on the multiple leaf queries to obtain a transformed query result; and invoking a transformed query on the database that is based on the query graph and the queries based on the multiple leaf queries to obtain the transformed query result.

Query generation based on a logical data model

Systems and methods for natural language question answering are described. For example, methods may include determining a set of candidate database queries, including respective sequences of tokens of a database syntax, based on a string; determining a first score for a first candidate database query from the set of candidate database queries, wherein the first score is based on a match between one or more words of the string and a token of the respective sequence of tokens of the first candidate database query; determining a second score for the first candidate database query, wherein the second score is based on natural language syntax data determined for words of the string; selecting, based on the first score and the second score, the first candidate database query from the set of candidate database queries; and invoking a search of the database using the first candidate database query to obtain search results.

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Saratoga, CA, United States of America

Amit Prakash