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: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.

Abstract: Provided herein are techniques for processing a database query aggregating data. Data tuples of a database object each including a grouping element and a data element are analyzed to determine a length of the data element for each data tuple. A plurality of tables each accommodate a successively greater length for the data element. A corresponding table of the plurality of tables to store each data tuple is determined based on the length of the data element of that data tuple relative to the accommodated lengths of the plurality of tables. Each data tuple in the determined corresponding table is stored to group the data tuples within each of the plurality of tables based on the grouping element, and an indication of corresponding tables containing members for each group is provided. The groups are combined across the plurality of tables to aggregate the data tuples for a database query.

Abstract: Merging adjacent rows of a synopsis table so as to increase the effectiveness of the synopsis table regarding data skipping. Adjacent rows for merging are identified based on statistics regarding: (i) queries of the database; (ii) effectiveness of the synopsis table for data skipping; and (iii) usage of predicates in queries of the database. Once merged, the synopsis table is smaller, and more effective with respect to data skipping, while fewer computing resources (administrative, maintenance, memory, clock cycles, storage space, etc.) are needed to process the database queries.

Abstract: Merging adjacent rows of a synopsis table so as to increase the effectiveness of the synopsis table regarding data skipping. Adjacent rows for merging are identified based on statistics regarding: (i) queries of the database; (ii) effectiveness of the synopsis table for data skipping; and (iii) usage of predicates in queries of the database. Once merged, the synopsis table is smaller, and more effective with respect to data skipping, while fewer computing resources (administrative, maintenance, memory, clock cycles, storage space, etc.) are needed to process the database queries.

Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.

Abstract: Embodiments of the present invention provide systems and methods for data management. Synopsis tables have been found to be more effective for maintaining a high level of system performance while answering analytical queries. Synopsis tables, which contain MAX, MIN, and Bloom filter columns, may be modified by dropping ineffective data content within these columns and regenerating dropped data when beneficial. By automatically modifying data, database queries may be optimized.

Abstract: Embodiments of the present invention provide systems and methods for data management. Synopsis tables have been found to be more effective for maintaining a high level of system performance while answering analytical queries. Synopsis tables, which contain MAX, MIN, and Bloom filter columns, may be modified by dropping ineffective data content within these columns and regenerating dropped data when beneficial. By automatically modifying data, database queries may be optimized.

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: A method, computer program product, and computer system for optimizing query processing is provided. An asynchronously updated index is provided for a main dataset. A time-sequences log of data modifications to the main dataset is provided. A query of the main dataset is received. The main dataset is joined with the time-sequenced log data resulting in a first intermediate result. The query is processed by keeping one or more entries satisfying the query by emulating a function of the asynchronously updated index resulting in a second intermediate result. Updated, deleted dataset entries are deleted from the asynchronously updated index. The query is processed resulting in a third intermediate result. A union of the second intermediate result and third intermediate result is built defining a final result.

Abstract: A method, computer program product, and computer system for optimizing query processing is provided. An asynchronously updated index is provided for a main dataset. A time-sequences log of data modifications to the main dataset is provided. A query of the main dataset is received. The main dataset is joined with the time-sequenced log data resulting in a first intermediate result. The query is processed by keeping one or more entries satisfying the query by emulating a function of the asynchronously updated index resulting in a second intermediate result. Updated, deleted dataset entries are deleted from the asynchronously updated index. The query is processed resulting in a third intermediate result. A union of the second intermediate result and third intermediate result is built defining a final result.

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: 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: 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: 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: A computer-implemented method includes receiving an artifact and a problem pattern, transforming the artifact into an abstracted artifact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artifact structure. Any matched portions of the abstracted artifact structure are related back to corresponding result portions of the artifact. The corresponding result portions of the artifact are returned. The method may be embodied in a corresponding computer system or computer program product.

Abstract: A computer-implemented method includes receiving an artifact and a problem pattern, transforming the artifact into an abstracted artifact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artifact structure. Any matched portions of the abstracted artifact structure are related back to corresponding result portions of the artifact. The corresponding result portions of the artifact are returned. The method may be embodied in a corresponding computer system or computer program product.

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: A data processing system, and an article of manufacturing, join rows associated with a source table column with rows associated with a target table column. A source node and a target node contain the source and target tables, respectively. A reduced representation of selected rows associated with the source table column is generated, as is a representation of the target table column. A filtering module filters the generated reduced representation of selected rows associated with the source table column through the generated representation of the target table column, the filtered generated reduced representation of selected rows identifying source table rows that do not have to be joined with the target table. The rows associated with the source table column minus the filtered generated reduced representation of selected rows are joined to the rows associated with the target table column.