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: 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: 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: Query planning in a distributed database that includes a table partitioned into shards according to a sharding criterion and distributed to database instances includes receiving a data-query. The data-query includes a “distinct count” clause on a first column and a “group by” clause on least a second column. A query plan is formulated to include respective instructions for converting, at at least some of the database instances, distinct values of the first column grouped by values of the second column into a count of the distinct values grouped by the values of the second column to obtain respective intermediate results; instructions for receiving the respective intermediate results from at least a subset of the at least some of the database instances; and instructions for concatenating the respective intermediate results using a summing operation to obtain the first “distinct count” of the first column grouped by the second column.

Abstract: Querying a distributed database including a table sharded into shards distributed to database instances includes receiving a data-query that includes an aggregation clause on a first column and a grouping clause on a second column; obtaining and outputting results data. Obtaining the results data includes receiving, by a query coordinator, intermediate results data; and combining, by the query coordinator, the intermediate results to obtain the results data.

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: 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: 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: 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: 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: Querying a distributed database including a table sharded into shards distributed to database instances includes receiving a data-query that includes an aggregation clause on a first column and a grouping clause on a second column; obtaining and outputting results data. Obtaining the results data includes receiving, by a query coordinator, intermediate results data; and combining, by the query coordinator, the intermediate results to obtain the results data.

Abstract: Operating a low-latency data access and analysis system using domain-specific chronometry may include obtaining, in the low-latency data access and analysis system, data expressing usage intent with respect to the low-latency data access and analysis system, in response to obtaining the data expressing usage intent, obtaining ontological data for a chronometric object in the low-latency data access and analysis system indicated by the data expressing 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: 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: 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: 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: A business intelligence (BI) server is described that supports data and schemas stored in multiple languages. The BI server implements a lookup table and lookup function that allows users to work with queries in different languages. When the user logs in, a session object is created for the user, which maintains the state information. A session variable specifies the language currently being used by the user. The BI server can inspect this session variable to determine the language of the user and perform the lookup translations as necessary. For example, if the language used by the session is different from the language of the base table storing the necessary information, the BI server can perform a translation by invoking a lookup function. The execution of the lookup can include performing a join operation of the base table with the lookup table to yield a translated value requested by the query.

Abstract: A business intelligence (BI) server and repository are described which support a set of customized and/or calculated data members. In accordance with an embodiment, the BI server maintains a connection to a plurality of data sources which may store a set of dimension members. The data source can be relational, file storage based, multidimensional and other types. In accordance with an embodiment, the BI server can accept queries from the user that contain calculated members as a parameter. The calculated member is defined by an expression including multiple dimension members and one or more arithmetic operators. The BI server can parse and validate the query and rewrite the query for the data source. After the query is rewritten and optimized, it is executed against the data source and a set of results is received.

Abstract: A business intelligence (BI) server and repository are described which support a set of hierarchical relationships among the data. The BI server receives user input specifying a set of parent-child or other ancestral relationship among a set of data in a data source. The BI server generates a set of SQL queries and executes the queries to pre-populate a set of tables which specify the parent child relationships among the data in the data source. One such table is a parent-child relationship closure table that defines the inter-member relationships among the data members. Once the tables are populated, the BI server uses the closure tables to answer queries that require knowledge of the ancestral relationships among data.

Abstract: A business intelligence (BI) server and repository are described which support a set of customized and/or calculated data members. In accordance with an embodiment, the BI server maintains a connection to a plurality of data sources which may store a set of dimension members. The data source can be relational, file storage based, multidimensional and other types. In accordance with an embodiment, the BI server can accept queries from the user that contain calculated members as a parameter. The calculated member is defined by an expression including multiple dimension members and one or more arithmetic operators. The BI server can parse and validate the query and rewrite the query for the data source. After the query is rewritten and optimized, it is executed against the data source and a set of results is received.

Abstract: A business intelligence (BI) server and repository are described which support a set of hierarchical relationships among the data. The BI server receives user input specifying a set of parent-child or other ancestral relationship among a set of data in a data source. The BI server generates a set of SQL queries and executes the queries to pre-populate a set of tables which specify the parent child relationships among the data in the data source. One such table is a parent-child relationship closure table that defines the inter-member relationships among the data members. Once the tables are populated, the BI server uses the closure tables to answer queries that require knowledge of the ancestral relationships among data.