Abstract: Examples disclosed herein relate to accessing electronic databases. Some examples disclosed herein may include partitioning a computation task into subtasks. A processing node of a computation engine may generate a database query for retrieving an electronic data segment associated with at least one of the subtasks from a database. The database query may include pre-processing instructions for a database management system (DBMS) associated with the database to pre-process the electronic data segment before providing the electronic data segment to the processing node. The pre-processing instructions may include at least one of: filtering, projection, join, aggregation, count, and user-defined instructions. The generated query may be provided to the DBMS.

Abstract: Examples disclosed herein relate to parallel transfers of electronic data. Some examples disclosed herein may include executing, by a processing node of a computation engine, a computation task among a plurality of computation tasks generated by the computation engine for transferring an electronic data table to a target table. The computation task, when executed by the processing node, may cause the processing node to transfer a segment of the electronic data table to a staging table, update a task status table upon completing the transfer of the segment to the staging table, in response to determining that the plurality of computation tasks have completed, update a last committer table with a task identifier associated with the computation task, and in response to determining that the last committer table includes the task identifier associated with the computation task, transfer the staging table to the target table.

Abstract: Examples disclosed herein relate to parallel transfers of electronic data. Some examples disclosed herein may include executing, by a processing node of a computation engine, a computation task among a plurality of computation tasks generated by the computation engine for transferring an electronic data table to a target table. The computation task, when executed by the processing node, may cause the processing node to transfer a segment of the electronic data table to a staging table, update a task status table upon completing the transfer of the segment to the staging table, in response to determining that the plurality of computation tasks have completed, update a last committer table with a task identifier associated with the computation task, and in response to determining that the last committer table includes the task identifier associated with the computation task, transfer the staging table to the target table.

Abstract: Examples disclosed herein relate to accessing electronic databases. Some examples disclosed herein may include partitioning a computation task into subtasks. A processing node of a computation engine may generate a database query for retrieving an electronic data segment associated with at least one of the subtasks from a database. The database query may include pre-processing instructions for a database management system (DBMS) associated with the database to pre-process the electronic data segment before providing the electronic data segment to the processing node. The pre-processing instructions may include at least one of: filtering, projection, join, aggregation, count, and user-defined instructions. The generated query may be provided to the DBMS.

Abstract: A system includes first and second data stores, each store having a set of materialized views of the base data and the views comprise a multistore physical design; an execution layer coupled to the data stores; a query optimizer coupled to the execution layer; and a tuner coupled to the query optimizer and the execution layer, wherein the tuner determines a placement of the materialized views across the stores to improve workload performance upon considering each store's view storage budget and a transfer budget when moving views across the stores.

Abstract: A system includes first and second data stores, each store having a set of materialized views of the base data and the views comprise a multistore physical design; an execution layer coupled to the data stores; a query optimizer coupled to the execution layer; and a tuner coupled to the query optimizer and the execution layer, wherein the tuner determines a placement of the materialized views across the stores to improve workload performance upon considering each store's view storage budget and a transfer budget when moving views across the stores.

Abstract: A system for evolutionary analytics supports three dimensions (analytical workflows, the users, and the data) by rewriting workflows to be more efficient by using answers materialized as part of previous workflow execution runs in the system.

Abstract: Systems and methods are disclosed to run a multistore system by receiving by-products of query processing in the multistore system, wherein the by-products include views or materializations of intermediate data; placing the views or materializations across the stores based on recently observed queries as indicative of a future query workload; determining a benefit score for each view based on a predicted future query workload, wherein each store has an allotted view storage budget, and there is a view transfer budget for transferring views between the stores; and tuning a physical design of the multistore system.

Abstract: A system for evolutionary analytics supports three dimensions (analytical workflows, the users, and the data) by rewriting workflows to be more efficient by using answers materialized as part of previous workflow execution runs in the system.