Abstract: Database replay log compaction verification includes identifying at least one replay log of a table that includes first database manipulation commands; obtaining a compacted replay log that includes second database manipulation commands that are insert commands, where an insert command includes a column and a corresponding value; replaying, to obtain a first replay result, the first database manipulation commands; replaying, to obtain a second replay result, the second database manipulation commands; and, responsive to one row of the first replay result not matching a corresponding row of the second replay result, sending a notification including a non-match. Replaying the first database manipulation commands includes identifying condition columns of the table; responsive to the condition columns not including the column, obtaining a row corresponding to the insert command, where the row includes a modified value of the corresponding value of the column; and adding the row to the first replay result.

Abstract: Identifying table joins includes obtaining respective casting similarities between pairs of columns of a first table and a second table. Each pair of columns includes a first column of the first table and a second column of the second table. Ones of the pairs of columns not satisfying a casting similarity condition are discarded to obtain first join candidates. Respective string similarities for the first join candidates are obtained. Ones of the first join candidates not satisfying a string similarity condition are discarded to obtain second join candidates. Final join candidates are obtained using the respective casting similarities and the respective string similarities of the second join candidates. A selected join candidate of the final join candidates is received from a user.

Abstract: Low-latency autonomous-analysis includes obtaining data expressing a usage intent with respect to a low-latency database analysis system that intent omits data corresponding to user input expressly requesting low-latency autonomous-analysis, obtaining requested results data based on the data expressing the usage intent, outputting requested visualization data representing at least a portion of the requested results data for presentation to a user, and, in response to outputting the requested visualization data, obtaining low-latency autonomous-analysis data by performing low-latency autonomous-analysis based on the data expressing the usage intent by identifying an autonomous-analysis predicate based on the requested visualization data, obtaining a defined autonomous-analysis latency constraint, obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint, such that the low-latency autonomous-analysis data d

Abstract: 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.

Abstract: Object scriptability methods and system are described herein. The method includes generating 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, where the edge represents a relationship between the respective objects in the data analysis system, and where generating the graph-based data structure includes receiving a high-level language script describing at least one data-analysis object, and generating at least one node from the plurality of nodes in accordance with the high-level language script.

Abstract: Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Abstract: Systems and methods for conversational user experiences and conversational database analysis disclosed herein improve the efficiency and accessibility of low-latency database analytics.

Abstract: 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 a candidate phrase by searching a phrase index based on the phrase fragment, wherein the candidate phrase at least partially matches the phrase fragment in accordance with the defined phrase pattern, and outputting the candidate phrase for presentation to a user as a candidate for resolving the unresolved portion.

Abstract: 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-specifi

Abstract: 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 datab

Abstract: 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.

Abstract: Systems and methods for multi-layered key-value storage are described. For example, methods may include receiving two or more put requests that each include a respective primary key and a corresponding respective value; storing the two or more put requests in a buffer in a first datastore; determining whether the buffer is storing put requests that collectively exceed a threshold; responsive to the determination that the threshold has been exceeded, transmitting a write request to a second datastore, including a subsidiary key and a corresponding data file that includes the respective values of the two or more put requests at respective offsets in the data file; for the two or more put requests, storing respective entries in an index in the first datastore that associate the respective primary keys with the subsidiary key and the respective offsets; and deleting the two or more put requests from the buffer.

Abstract: Described are methods and systems for improved cardinality estimation. A method may include obtaining a data-query, obtaining a row, generating a hash value, determining a cardinality of leading zeros in the hash value, identifying a bucket with respect to the hash value, including a bucket identifier and the cardinality of leading zeros in a representation, determining the approximate unique count, and outputting the approximate unique count as results data responsive to the portion of the data-query.

Abstract: Systems and methods for mapping natural language to queries using a query grammar are described. For example, methods may include generating, based on a string, a set of tokens of a database syntax; generating a query graph for the set of tokens using a finite state machine representing a query grammar, wherein nodes of the finite state machine represent token types, directed edges of the finite state machine represent valid transitions between token types in the query grammar, vertices of the query graph correspond to respective tokens of the set of tokens, and directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens; determining, based on the query graph, a sequence of the tokens in the set of tokens, forming a database query; and invoking a search of a database using a query based on the database query to obtain search results.

Abstract: 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.

Abstract: Improved systems and methods for database analysis are described herein. A method includes generating a graph-based ontological data structure including nodes connected by edges in a low-latency database analysis system, wherein each node represents a respective analytical-object in the low-latency database analysis system, maintaining versions for each of the nodes in the graph-based ontological data structure, maintaining versions for each of the edges in the graph-based ontological data structure, maintaining a transaction log for each transaction with respect to the graph-based ontological data structure, reverting to an earlier version of at least a portion of the graph-based ontological data structure using the transaction log, versioned nodes, and versioned edges in response to an event, and outputting a version of the graph-based ontological data structure in a defined form for presentation to a user or for use by a client.

Abstract: 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.

Abstract: Search guidance methods and system are described. A method includes generating user interface data for at least a portion of a user interface for performing data-analytics with respect to a low-latency database analysis system, the portion including a text string user input element, a data-analytics construct card type, and a data-analytics guidance portion. The data-analytics construct card type includes text on usage of a data-analytics construct card grammatical function with respect to a data-analytics grammar used by the system to process data associated with the data-analytics construct card type. The user interface data is output for presentation to a user. Data associated with the data-analytics construct card construct type is received, the text string user input element is output with the data, and an updated data-analytics guidance portion is output in response to the data. A request for data including the data is processed and results are output.

Abstract: Operating a low-latency database analysis system using domain-specific chronometry may include obtaining, in the low-latency database analysis system, data expressing a usage intent with respect to the low-latency database analysis system, in response to obtaining the data expressing the usage intent, obtaining ontological data for a chronometric object in the low-latency database analysis system indicated by the data expressing the usage intent, identifying a chronometry dataset from a plurality of chronometry datasets, wherein the plurality of chronometry datasets includes a domain-specific chronometry dataset and a canonical chronometry dataset, obtaining results data in accordance with the chronometry dataset and the chronometric object, generating output data representing the results data in accordance with the chronometry dataset, and outputting the output data for presentation via a user interface.

Abstract: 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.