Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.

Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: An illustrative embodiment for optimizing scans using a Bloom filter synopsis, defines metadata to encode distinct values in a range of values associated with a particular portion of a managed object in a database management system into a probabilistic data structure of a Bloom filter that stores an indicator, encoded in a fixed size bit map with one or more bits, indicating whether an element of the particular portion of the managed object is a member of a set of values summarized in the Bloom filter using a value of 1 or definitely not in the set using a value of 0. The Bloom filter is compressed to create a compressed Bloom filter. The Bloom filter is added to the metadata associated with the managed object and used when testing for values associated with predicates.

Abstract: The method includes identifying at least one of a minimum value, a maximum value, and a Bloom filter value for a row of data in a metadata table, wherein the metadata table contains metadata corresponding to a row of data in a main table. The method includes adjusting at least one of an identified first minimum value to a second minimum value, an identified first maximum value to a second maximum value, and an identified first Bloom filter value to a second Bloom filter value.

Abstract: The method includes identifying at least one of a minimum value, a maximum value, and a Bloom filter value for a row of data in a metadata table, wherein the metadata table contains metadata corresponding to a row of data in a main table. The method includes adjusting at least one of an identified first minimum value to a second minimum value, an identified first maximum value to a second maximum value, and an identified first Bloom filter value to a second Bloom filter value.

Abstract: Various ways of storing data in tabular form such that some records are stored in a row oriented manner and some data is stored in a column oriented manner. The records respectively stored in the row and column oriented manners may, or may not, overlap (in whole or in part. Some embodiments accomplish this dual orientation of data by: (i) using a range partition; (ii) using a union all view command; and/or (iii) using a first table, a staging table and a materialized query table.

Abstract: Various ways of storing data in tabular form such that some records are stored in a row oriented manner and some data is stored in a column oriented manner. The records respectively stored in the row and column oriented manners may, or may not, overlap (in whole or in part. Some embodiments accomplish this dual orientation of data by: (i) using a range partition; (ii) using a union all view command; and/or (iii) using a first table, a staging table and a materialized query table.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: Responding to relational database queries (for example, SQL queries) in a new way. More specifically, qualifying queries are written (for example, written by a human individual) in a way so that a join operation precedes a row limiting operation. Notwithstanding the fact that the join operation precedes the row limiting operation, when responding to the query, machine logic (for example, software) performs the row limiting operation before the join operation. This can improve time and processing efficiency.

Abstract: Disclosed is a data processing system, and an article of manufacturing for use with the data processing system. The data processing system joins rows associated with a column of a source table with rows associated with a column of a target table. The data processing system includes a source node containing the source table and including a target node containing the target table.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for executing a query in a computer to retrieve data from a relational database stored on a data storage device. A query is received that contains a CASE expression having one or more subqueries. A query execution plan is generated. The query execution plan uses a parameterized union all operator to keep the semantics of the CASE expression, and a nested loop join operator to drive data across the section of the query execution plan that executes the CASE expression without performing any join operations. The generated query execution plan is executed in the computer to retrieve data from the relational database.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for executing a query in a computer to retrieve data from a relational database stored on a data storage device. A query is received that contains a CASE expression having one or more subqueries. A query execution plan is generated. The query execution plan uses a parameterized union all operator to keep the semantics of the CASE expression, and a nested loop join operator to drive data across the section of the query execution plan that executes the CASE expression without performing any join operations. The generated query execution plan is executed in the computer to retrieve data from the relational database.

Abstract: Techniques are disclosed for generating statistical views in a database system. In one embodiment, a request is received to execute a database workload. One or more constraints pertaining to executing the database workload is retrieved. The database workload is evaluated to generate multiple statistical view candidates. The statistical view candidates are refined based on the one or more constraints. One or more statistical views are then generated based on the refined statistical view candidates.

Abstract: Disclosed is an apparatus and system for reducing locking in materialized query tables (MQT) for distributive functions. The apparatus includes an insert module that inserts into an MQT table a child record when a new record is inserted into a base table associated with the MQT. The child record includes values associated with the insert operation. Also included is a delete module that inserts into the MQT a child record that includes measure values that are the negative of the measure values in the base table row that is the subject of the delete operation. An update module inserts two child rows into the MQT, one negating the affected record and the other adding the values of the update operation. Each inserted child row includes a unique identifier that relates the inserted row to a parent row. An execution module generates responses using the values indicated by the cumulative records in a family.

Abstract: An optimized query processing system and method for a database system. The database system includes a table of data and an index correlated to both the query and to the table. The optimized query processing system includes an index accessing module adapted to access the index to determine if the table contains an entry satisfying a query predicate; and a tablescan module for scanning substantially the entire table and retrieving data satisfying the query. A method is also disclosed for generating database diagnostic data.

Abstract: Disclosed is an apparatus and system for reducing locking in materialized query tables (MQT) for distributive functions. The apparatus includes an insert module that inserts into an MQT table a child record when a new record is inserted into a base table associated with the MQT. The child record includes values associated with the insert operation. Also included is a delete module that inserts into the MQT a child record that includes measure values that are the negative of the measure values in the base table row that is the subject of the delete operation. An update module inserts two child rows into the MQT, one negating the affected record and the other adding the values of the update operation. Each inserted child row includes a unique identifier that relates the inserted row to a parent row. An execution module generates responses using the values indicated by the cumulative records in a family.

Abstract: A workload to be handled by a database system can be identified. The workload can include at least one query that the database system is to handle. A set of at least one candidate statistical views (statviews) to be utilized when optimizing the workload can be enumerated. A benefit value and a cost value of the each of the enumerated candidate statistical views relative to the entire workload can be computed. The cost value can reflect a cost of constructing and collecting statistics on the associated statistical view. A set of the candidate views most beneficial for handling the workload can be determined based upon the computed benefit values and computed cost values. A generalization phase that augments the candidate view set with higher value candidate views for consideration during the recommendation phase. The optimum subset of views from the determined set of candidate views can be recommended, which can cause them to be constructed and utilized by a database optimizer.

Abstract: Disclosed is a data processing system implemented method, a data processing system, and an article of manufacturing for use with the data processing system. The data processing system implemented method is used for directing the data processing system to join rows associated with a column of a source table with rows associated with a column of a target table. The data processing system includes a source node containing the source table and including a target node containing the target table.