A method includes receiving a request to modify a first value of a first field of a first item in a self-describing data system, and obtaining a domain comprising items in the self-describing data system. The first item and a second item are included in items, and the second item comprises a second field having a second value. The method includes calculating, based on a rule of the second field, a dependency of the second value on the first value. The rule specifies how the second value is to be calculated using the first value. The method includes modifying, based on the request, the first value. The method includes receiving an event triggered by the modification to the first value. The method includes, responsive to the event, calculating the second value based on the rule, and storing the second value in the second field.

Multidimensional data analysis applications, including OLAP applications, simultaneously aggregate across many sets of dimensions. However, computing multidimensional aggregates is a performance bottleneck for OLAP data analysis applications. In order to improve the speed of interactive analysis, OLAP databases often precompute aggregates at various levels of detail and on various combinations of data attributes. However, the cost and speed of precomputation influences how frequently the aggregates can be brought up-to-date. Systems and methods disclosed herein provide graph based multidimensional analysis processing without pre-aggregating or precomputing the data along dimensional hierarchies, and by providing the results to the end user on-demand. Since preaggregation or precomputation of data along dimensional hierarchies is not necessary, implementations allow the end user to perform data analysis as soon as the data is available.

Systems and techniques for searching multiple data sources are described herein. Users may specify searches of multiple data sources to occur on a periodic basis. The searches may be configured to search time or date ranges that have not previously been searched. A user may select the data sources of interest and specify search terms, review and edit previously created searches, and review results of searches. The system automatically performs the specified searches, and notifies the user and/or a team of the user each time new results are found. The system may efficiently search the data sources by storing previous search results and comparing the previous results to current search results to identify new search results.

A method includes receiving a request to modify a first value of a first field of a first item in a self-describing data system, and obtaining a domain comprising items in the self-describing data system. The first item and a second item are included in items, and the second item comprises a second field having a second value. The method includes calculating, based on a rule of the second field, a dependency of the second value on the first value. The rule specifies how the second value is to be calculated using the first value. The method includes modifying, based on the request, the first value. The method includes receiving an event triggered by the modification to the first value. The method includes, responsive to the event, calculating the second value based on the rule, and storing the second value in the second field.

A method for execution by a query processing system includes receiving a query expression that includes a call to a computing window function. The computing window function is executed in accordance with execution of the query expression against a database. Execution of the query expression includes accessing an ordered set of rows of the database indicated in the call to the computing window function, and applying a recursive definition indicated in the call to the computing window function to each row in the ordered set of rows to generate output for each row in the ordered set of rows. A query resultant for the query expression is generated based on the output for each row in the ordered set of rows.

A method for execution by a query processing system includes receiving a query expression that includes a call to a computing window function. The computing window function is executed in accordance with execution of the query expression against a database. Execution of the query expression includes accessing an ordered set of rows of the database indicated in the call to the computing window function, and applying a recursive definition indicated in the call to the computing window function to each row in the ordered set of rows to generate output for each row in the ordered set of rows. A query resultant for the query expression is generated based on the output for each row in the ordered set of rows.

Methods and systems for data management are described, particularly for processing global queries. Each global query includes a user-defined query constraint value, such as laxity or query response time limit. The query receiving node maintains a copy of the previously updated data from all of its children node. The query receiving node first searches for the requested query data in its local data storage to minimize children node query. If any portion of the requested data in the local data storage fails to meet the query constraint value, then the child node from which the data came from is tasked with recursively executing the global query.

A method for performing recursive searching of items of a data structure having a data mode includes creating an instance of a query definition, the instance of the query definition comprising a unique identifier, specifying one or more elements of the query definition, providing the query definition as an input to a query engine. The method further includes the operations of determining, by the query engine, query execution instructions based on the query definition, the query instructions specifying a recursive level-by-level search until a terminal node of the data structure is reached, obtaining results of a query executed based on the query execution instructions; and outputting query results.

Methods, systems, and apparatuses, including computer programs for generating and using a hash table configured to improve mapping of reads are disclosed that include obtaining a first seed of K nucleotides from a reference sequence, generating a seed extension tree having a nodes, wherein each node of the nodes corresponds to (i) an extended seed that is an extension of the first seed and has a nucleotide length of K* and (ii) one or more locations, in a seed extension table, that include data describing reference sequence locations that match the extended seed, and for each node: storing interval information at a location of the hash table that corresponds to an index key for the extended seed, wherein the interval information references one or more locations in the seed extension table that include reference sequence locations that match the extended seed associated with the node.

Multidimensional data analysis applications, including OLAP applications, simultaneously aggregate across many sets of dimensions. However, computing multidimensional aggregates is a performance bottleneck for OLAP data analysis applications. In order to improve the speed of interactive analysis, OLAP databases often precompute aggregates at various levels of detail and on various combinations of data attributes. However, the cost and speed of precomputation influences how frequently the aggregates can be brought up-to-date. Systems and methods disclosed herein provide graph based multidimensional analysis processing without pre-aggregating or precomputing the data along dimensional hierarchies, and by providing the results to the end user on-demand. Since preaggregation or precomputation of data along dimensional hierarchies is not necessary, implementations allow the end user to perform data analysis as soon as the data is available.