Abstract: Database query processing may be scaled using additional processing clusters. A database query is received at a processing cluster. A determination is made as to whether additional processing clusters will be used to process the database query. Operations to cause compute nodes of the processing cluster to instruct operations at the additional processing clusters are included in a plan generated to perform database queries determined to use additional processing clusters. The plan is executed to be perform the database query causing compute nodes of the processing cluster to send instructions to corresponding additional processing clusters in order to generate and return a response to the database query.

Abstract: Database query processing may be scaled using additional processing clusters. A database query is received at a processing cluster. A determination is made as to whether additional processing clusters will be used to process the database query. Operations to cause compute nodes of the processing cluster to instruct operations at the additional processing clusters are included in a plan generated to perform database queries determined to use additional processing clusters. The plan is executed to be perform the database query causing compute nodes of the processing cluster to send instructions to corresponding additional processing clusters in order to generate and return a response to the database query.

Abstract: A system and method of determining an earliest delivery date for a demand on the basis of a fact table stored in a database, where the demand indicates both a desired date and a desired quantity for items from a stock, where a fact time series is comprised by and/or deducible from the fact table, and where the fact time series comprises fact tuples indicating both a fact date and a change in quantity of stock items that occurred on this fact date. The method includes generating an aggregated time series from the fact time series by defining and/or obtaining discrete time intervals, and generating, for at least one discrete time interval, an aggregated tuple indicating both its corresponding discrete time interval and the aggregated change in quantity of stock items that occurred in its corresponding discrete time interval.

Abstract: A system and method of determining an earliest delivery date for a demand on the basis of a fact table stored in a database, where the demand indicates both a desired date and a desired quantity for items from a stock, where a fact time series is comprised by and/or deducible from the fact table, and where the fact time series comprises fact tuples indicating both a fact date and a change in quantity of stock items that occurred on this fact date. The method includes generating an aggregated time series from the fact time series by defining and/or obtaining discrete time intervals, and generating, for at least one discrete time interval, an aggregated tuple indicating both its corresponding discrete time interval and the aggregated change in quantity of stock items that occurred in its corresponding discrete time interval.

Abstract: A system and method of determining an earliest delivery date for a demand on the basis of a fact table stored in a database, where the demand indicates both a desired date and a desired quantity for items from a stock, where a fact time series is comprised by and/or deducible from the fact table, and where the fact time series comprises fact tuples indicating both a fact date and a change in quantity of stock items that occurred on this fact date. The method includes generating an aggregated time series from the fact time series by defining and/or obtaining discrete time intervals, and generating, for at least one discrete time interval, an aggregated tuple indicating both its corresponding discrete time interval and the aggregated change in quantity of stock items that occurred in its corresponding discrete time interval.

Abstract: A system and method of determining an earliest delivery date for a demand on the basis of a fact table stored in a database, where the demand indicates both a desired date and a desired quantity for items from a stock, where a fact time series is comprised by and/or deducible from the fact table, and where the fact time series comprises fact tuples indicating both a fact date and a change in quantity of stock items that occurred on this fact date. The method includes generating an aggregated time series from the fact time series by defining and/or obtaining discrete time intervals, and generating, for at least one discrete time interval, an aggregated tuple indicating both its corresponding discrete time interval and the aggregated change in quantity of stock items that occurred in its corresponding discrete time interval.