Abstract: A method for processing a plurality of queries is provided according to embodiments of the present disclosure. In this method, based on a plurality of queries and an execution plan for the plurality of quires, a plurality of record identification (ID) numbers can be stored into a pool in a numerical order. Each of the plurality of record ID numbers can identify a data record in a database. Then, the execution plan can be performed to batch a plurality of data records corresponding to the plurality of record ID numbers in the database based on a distribution of the plurality of record ID numbers in the pool.

Abstract: A method for processing a plurality of queries is provided according to embodiments of the present disclosure. In this method, based on a plurality of queries and an execution plan for the plurality of quires, a plurality of record identification (ID) numbers can be stored into a pool in a numerical order. Each of the plurality of record ID numbers can identify a data record in a database. Then, the execution plan can be performed to batch a plurality of data records corresponding to the plurality of record ID numbers in the database based on a distribution of the plurality of record ID numbers in the pool.

Abstract: A method includes generating a plurality of vectors representing words in a plurality of documents about an information technology (IT) system and clustering the plurality of vectors to produce a plurality of clusters. The method also includes identifying a cluster of the plurality of clusters that contains a plurality of clustered vectors, generating a feature based on a plurality of words represented by the plurality of clustered vectors, and training a machine learning model to identify an anomaly in the IT system based on the feature.

Abstract: An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

Abstract: A method includes generating a plurality of vectors representing words in a plurality of documents about an information technology (IT) system and clustering the plurality of vectors to produce a plurality of clusters. The method also includes identifying a cluster of the plurality of clusters that contains a plurality of clustered vectors, generating a feature based on a plurality of words represented by the plurality of clustered vectors, and training a machine learning model to identify an anomaly in the IT system based on the feature.

Abstract: Aspects of the invention include a computer-implemented method that receives, by a processor, an ensemble decision tree and generates, by the processor, native code from the ensemble decision tree. The method compiles, by the processor, the native code into machine language and scores, by the processor, the execution time of the native code. The method dynamically reoptimizes, by the processor, portions of the native code corresponding to the most traversed portion of the ensemble decision tree.

Abstract: An approach is provided in which a method, system, and program product analyze, while training a machine learning model, a set of first data transformation operators in a first data preparation pipeline that generates a plurality of constructed features from a set of training data. The method, system, and program product create a plurality of second data preparation pipelines from the first data preparation pipeline, wherein the set of first data transformation operators are converted to a set of second data transformation operators and each assigned to one of the plurality of second data preparation pipelines. The method, system, and program product deploy the plurality of second data preparation pipelines to a runtime system.

Abstract: Aspects of the invention include a computer-implemented method that receives, by a processor, an ensemble decision tree and generates, by the processor, native code from the ensemble decision tree. The method compiles, by the processor, the native code into machine language and scores, by the processor, the execution time of the native code. The method dynamically reoptimizes, by the processor, portions of the native code corresponding to the most traversed portion of the ensemble decision tree.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: receiving, by a production database, a query statement for processing by the production database; the production database in performing runtime execution processing of the query statement identifying one or more query statement value of the query statement; the production database in performing runtime execution processing of the query statement establishing, using a predictive model, an access path based on the identified one or more query statement value; and the production database using the established access path in performing runtime execution processing of the query statement.

Abstract: Database configuration tuning is provided. A set of database nodes having similar data factors is selected in a centerless network of database nodes. Configuration models corresponding to the set of database nodes are trained using data parallelism. Trained configuration models corresponding to the set of database nodes are combined to form a federated configuration model. It is determined whether performance indicators corresponding to the set of database nodes are greater than a performance threshold level. In response to determining that the performance indicators corresponding to the set of database nodes are greater than the performance threshold level, a database configuration corresponding to the federated configuration model is recommended to a new database node. The new database node is joined to the centerless network.

Abstract: In an approach to determining an access method for a partition in a partition table, a computer receives a query and determines if there is a partition table utilized by the query. When there is a partition table utilized by the query, then the computer determines that a partition in the partition table meets the plurality of conditions of the query. The computer collects a plurality of partition level statistics for the partition that meets the plurality of conditions of the query. Additionally, the computer determines, based, at least in part, on the plurality of partition level statistics, a cost for one or more access methods for the partition that meets the plurality of conditions of the query. Furthermore, the computer determines, based, at least in part, on the cost for each access method, an access method for the partition that meets the plurality of conditions of the query.

Abstract: Database configuration tuning is provided. A set of database nodes having similar data factors is selected in a centerless network of database nodes. Configuration models corresponding to the set of database nodes are trained using data parallelism. Trained configuration models corresponding to the set of database nodes are combined to form a federated configuration model. It is determined whether performance indicators corresponding to the set of database nodes are greater than a performance threshold level. In response to determining that the performance indicators corresponding to the set of database nodes are greater than the performance threshold level, a database configuration corresponding to the federated configuration model is recommended to a new database node. The new database node is joined to the centerless network.

Abstract: A computer-implemented method includes identifying a primary database interaction statement and determining one or more intermediate database interaction statements based on the primary database interaction statement. The computer-implemented further includes determining, for each of the one or more intermediate database interaction statements, an intermediate statement transformation indicator. The computer-implemented method further includes determining one or more transformed database interaction statements based on each intermediate statement transformation indicator.

Abstract: A computer-implemented method includes identifying a primary database interaction statement and determining one or more intermediate database interaction statements based on the primary database interaction statement. The computer-implemented further includes determining, for each of the one or more intermediate database interaction statements, an intermediate statement transformation indicator. The computer-implemented method further includes determining one or more transformed database interaction statements based on each intermediate statement transformation indicator. A corresponding computer program product and computer system are also disclosed.

Abstract: A computer-implemented method includes receiving a batch processing job. The method identifies multiple access paths for the batch processing job. The method determines one or more runtime costs for each of the multiple access paths. The method determines whether the batch processing job includes comparable access paths. The method responds to the batch processing job not including comparable access paths by receiving a query and selecting a desired access, based on the one or more runtime costs. The method responds to the batch processing job including comparable access paths by determining one or more context costs for each of the one or more access paths, receiving a query, identifying a context for the query, and selecting a desired access path for the query. The desired access path is based on the one or more runtime costs, the one or more context costs, and the context for the query.

Abstract: Aspects of the present disclosure relate to an approach for generating query execution plans for a database query. A computer-implemented method comprises determining whether a size of a file to be generated during execution of a database query is variable in response to obtaining the database query. The method further comprises determining a plurality of ranges for the size of the file in response to determining that the size of the file is variable. The method further comprises generating a plurality of query execution plans corresponding to the plurality of ranges. Accordingly, a plurality of query execution plans corresponding to different file sizes can be generated for the database query, and an efficient and cost effective query execution plan may be selected based on the actual file size during the execution of the database query.

Abstract: A computer-implemented method includes identifying a query, including one or more predicates and one or more branches, wherein one or more branches includes one or more legs. The computer-implemented method further includes, for each branch, in parallel: determining a risk, determining a return row threshold, estimating a number of return rows; terminating access if the return rows exceed the threshold. The computer-implemented method further includes, for each leg, in parallel: determining a leg return row threshold; accessing the leg; fetching one or more return rows into one or more leg return row pages; terminating access if the return rows exceed the threshold; intersecting one or more leg return row pages into one or more intersected leg return row pages; and applying the one or more predicates to the one or more intersected leg return row pages. The method may be embodied in a corresponding computer system or computer program product.

Abstract: Disclosed aspects relate to query processing management in a database management system (DBMS). The DBMS may detect that a query has a set of subqueries. The DBMS may detect that the set of subqueries indicates a set of data structures. A lead data structure may be selected from the set of data structures using a selection criterion. A set of input columns may be identified from the lead data structure of the set of data structures. A set of output columns may be identified from the set of data structures other than the lead data structure. A hash data structure may be constructed using the set of input columns and the set of output columns. A cache may be built based on the hash data structure.

Abstract: A computer-implemented method includes identifying a query, including one or more predicates and one or more branches, wherein one or more branches includes one or more legs. The computer-implemented method further includes, for each branch, in parallel: determining a risk, determining a return row threshold, estimating a number of return rows; terminating access if the return rows exceed the threshold. The computer-implemented method further includes, for each leg, in parallel: determining a leg return row threshold; accessing the leg; fetching one or more return rows into one or more leg return row pages; terminating access if the return rows exceed the threshold; intersecting one or more leg return row pages into one or more intersected leg return row pages; and applying the one or more predicates to the one or more intersected leg return row pages. The method may be embodied in a corresponding computer system or computer program product.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: receiving, by a production database, a query statement for processing by the production database; the production database in performing runtime execution processing of the query statement identifying one or more query statement value of the query statement; the production database in performing runtime execution processing of the query statement establishing, using a predictive model, an access path based on the identified one or more query statement value; and the production database using the established access path in performing runtime execution processing of the query statement.