Abstract: A method for optimizing data retrieval from a multidatabase system by restructuring a database query tree to optimize query response time in a two step optimization process. First, the query tree is transformed into a left deep join tree having a root query, a plurality of subordinate (descendant) query nodes and a plurality of table nodes, each subordinate query node having a left child subtree and a right child subtree. This transformation is usually the result of a first optimization scheme such as System-R. A response time for the root query and for each of the plurality of subordinate query nodes is estimated and access response times to each table node and subtree are estimated. Then, this data is utilized in the balancing of the left deep join query tree so that the cost for access to each left child subtree is substantially equal to the cost for the right child subtree.

Abstract: A structure and method of arranging recursively derived data items in a database. A set of hierarchical iteration levels, one for each iteration of the recursive relation from which the data items are derived, is provided and all data items derived during a given iteration are associated with the corresponding iteration level. Also provided is an index structure including an index set of non-leaf nodes, a sequence set of leaf nodes, and an iteration level index. The leaf nodes include a record of the iteration level of each data item. The data are globally linked according to iteration level or are clustered on pages which are linked according to iteration level. Highly efficient scan and search are implemented by utilizing the iteration level index and the record of iteration level in the leaf nodes to direct the scanning and searching to data generated during a single iteration. The least fixpoint of a set of mutually recursive relations is efficiently calculated by these methods.

Abstract: A programmable machine system and method for managing electronic data access among multiple different relational databases in a network distributed database environment. The machine is programmed so that it can construct cost-effective access strategies for any of the participating databases absent any DBMS-specific cost models. The system provides query optimization across different database management systems in a network distributed database environment based on a calibrating database relying only on typical relational database statistics and cost data is developed by running queries in the various databases against the calibrating database. A logical cost model is constructed using the resulting cost data and is used to estimate the cost of a given query based on logical characteristics of the DBMS, the relations, and the query itself. The cost of a complex query is estimated using primitive queries.

Abstract: A method of controlling the allocation of resources in a parallel processor computer. A critical path for executing a task such as evaluating a database query is determined. The minimum time to execute the task assuming infinite resources such as processors and memory buffers is calculated. Resources are scheduled against subtasks so as to execute the task in the calculated minimum time. The number of resources would be required to meet the schedule is determined and if the computer has that many resources the schedule is carried out. Otherwise a revised execution time is calculated, preferably by using as a scaling factor the ratio between the number of required resources and the number of available resources. Then the schedule is adjusted so that the task can be executed in the revised time and the number of resources that would be required to meet the adjusted schedule is determined. If the computer has that many resources the schedule is carried out, otherwise the process is repeated.

Abstract: In a database management system that operates according to the Structured Query Language standard, a query containing a reference to a view is processed by dynamically materializing the view through execution of the view definition. Once the view is materialized, it is treated as any other base table. To enable a query optimization plan to refer to the materialized view, a view node is introduced into the execution plan. The view node includes a subquery that results in the creation of the virtual table defined by the view. This created table is temporarily stored in a memory. The query optimizer can treat view nodes in the same manner as stored base tables, and thereby overcomes restrictions that were placed upon views by previous view decomposition approaches.