Abstract: An approach is provided for optimizing a join operation that includes receiving, by one or more processors of a computer system, a join request associated with a fact table and a plurality of related dimension tables; identifying, by the one or more processors of the computer system, a join relationship from the fact table and the plurality of related dimension tables; matching, by the one or more processors of the computer system, different tables of the fact table and the plurality of related dimension tables; filtering, by the one or more processors of the computer system using data parallelism, the fact table and the plurality of related dimension tables, wherein the filtering occurs prior to performing the join request; and performing, by the one or more processors of the computer system, the join operation pursuant to the join request.

Abstract: Query content-based data generation includes obtaining a query having an outer query and one or more subqueries, converting predicates of a subquery to be predicates of the outer query, and thereby obtain a transformed query, generate from the transformed query a query block having a list of predicate(s) selected from the transformed query, obtaining column information based on the list of predicate(s) and using the column information to select data for a dataset such that data records from the dataset are produced as results to executing the obtained query against the dataset.

Abstract: Database recovery is based on workload priorities. A database workload is divided into a plurality of workload groups. A workload group of the plurality of workload groups is selected for recovery, in which the selecting is based on a priority of the workload group. One or more recovery actions are performed for the workload group selected for recovery.

Abstract: Systems and methods for pre-allocating and utilizing storage space in a relational database are provided. In embodiments a method includes: obtaining transaction data including data regarding record insertions in a relational database, wherein each record of the record insertions is associated with a key value; identifying a type of each of the record insertions as either a random insertion type or a key range insertion type based on the database transaction data, wherein the random insertion type comprises records associated with respective key values inserted in a random order, and the key range insert type comprises records associated with a range of key values inserted within a certain time period; predicting a new range of key values associated with future record insertions based on the type of each of the record insertions; and pre-allocating page space in one or more pages of the relational database for the future record insertions.

Abstract: A simulated dataset is queried for regression by validating a structured query language (SQL) statement, determining a pattern type of the SQL statement, reconstructing the SQL statement according to a predetermined process for the pattern type, creating a mutated SQL statement for querying a simulated dataset, and validating the mutated SQL statement. The simulated dataset is based on a confidential dataset having the confidential elements removed or replaced.

Abstract: Leveraging the competence of a computing cluster and a storage cluster by dynamically adjusting an SQL (structured query language) execution path to optimize system performance with fast response time under a distributed relational database on a data storage platform. Also, endpoint devices are divided into groups for the purpose of responding to queries and/or query blocks. The most suitable group of endpoint devices is chosen to respond to a given query or query block.

Abstract: In a computer-implemented method for improving performance of a database optimizer, a processor calculates a first execution time for a database query executing through a first access path of a database system. A processor generates a second access path. A processor determines a second execution time via a simulation of the database query through the second access path. A processor determines that the second execution time is less than the first execution time. A processor, responsive to determining that the second execution time is less than the first execution time, sends the second access path to the database optimizer.

Abstract: A computer-implemented method selects an access path for high cost and/or complex queries. The method includes building a classification model configured to identify a lowest cost access path. The method further includes receiving a query, where the query is configured to retrieve a set of data from a database. The method also includes generating an access map for the query, where the access map includes one or more potential access paths to execute the query. The method includes collecting, for the query, a set of data for each potential access path. The method further includes classifying, by the classification model, the query. The method also includes selecting a first access path of the one or more potential access paths and executing the query.

Abstract: A computer-implemented method to estimate an execution time for large query batches. The method includes identifying a batch of queries, where the batch of queries where each query is configured to retrieve data from a database. Selecting a first query and generating an access path for the first query including one or more mini plans. Building a learning model for the one or more mini plans. Estimating an estimated execution time for each of the one or more mini plans. Adding each of the estimated execution times for each of the one or more mini plans to determine a first total estimated time for the first query. Displaying the first total estimated time.

Abstract: Leveraging the competence of a computing cluster and a storage cluster by dynamically adjusting an SQL (structured query language) execution path to optimize system performance with fast response time under a distributed relational database on a data storage platform. Also, endpoint devices are divided into groups for the purpose of responding to queries and/or query blocks. The most suitable group of endpoint devices is chosen to respond to a given query or query block.

Abstract: A system for monitoring cell-substrate impedance of excitable cells at millisecond time resolution and methods of assessing cell beating by monitoring cell-substrate impedance of beating cells at millisecond time resolution.

Abstract: A method of managing a storage system comprises detecting a reference to a first page in the storage system. The method also comprises creating a first candidate block for the first page based on the detecting. The first candidate block may comprise a continuous series of pages that begins with the first page. The method also comprises monitoring subsequent references to pages within the first candidate block. The method also comprises determining that the first candidate block meets a first set of hot-block requirements. The method also comprises relocating the first candidate block to a hot-block space in a buffer pool based on the determining, resulting in a first hot block.

Abstract: In an approach for improving performance of a batch job running on database servers in an active-active architecture. A processor, responsive to a batch job being ready to be executed on a source database server, sends a first communication to a target database server with a synchronization start point. While executing the batch job, a processor utilizes a pre-lock function, pre-load function, and lock avoidance function to prevent lock conflicts. A processor, responsive to either the source database server or the target database server encountering a commit statement, suspends the respective database server and sends a second communication to ask if the other respective database server is ready to complete the commit statement. A processor, responsive to the other respective database server confirming its ready to complete the commit statement, completes the commit statement on both the source database server and the target database server.

Abstract: Embodiments are disclosed for a method. The method includes determining demand resource for a batch jobs using a resource machine learning model trained to determine a cloud resource that the batch jobs use more than other resources during execution. The method further includes generating resource estimates for the demand resources. Additionally, the method includes determining a batch rating for a batch run using a batch rating machine learning model that is trained to generate a batch rating number based on features representing a priority of the batch run in reference to parallel execution batch runs. The method also includes generating a purchase recommendation for execution of the batch run on a cloud platform based on the resource estimates and the batch rating.

Abstract: Devices, systems and methods for monitoring excitable cells, such as cardiomyocytes, on microelectrode arrays that couple the electro-stimulation of excitable cells to induce or regulate cardiomyocyte beating and the simultaneous measurement of impedance and extracellular recording to assess changes in cardiomyocyte beating, viability, morphology or electrophysical properties in response to a plurality of treatments.

Abstract: Functional verification of a new database feature is provided. A set of SQL query seeds are evolved using information contained in a knowledge map that was generated based on a real SQL query workload of an entity corresponding to a database. Real SQL queries are identified in the database that are similar to the evolved set of sample SQL query seeds. A new feature to be implemented in the database is injected into the real SQL queries that are similar to the evolved set of sample SQL query seeds. Multiple runtime access paths are generated for each of the real SQL queries injected with the new feature for output cross validation. Each of the real SQL queries injected with the new feature is run in a test environment using each of the multiple runtime access paths corresponding to a real SQL query injected with the new feature for functional verification.

Abstract: Embodiments of the present disclosure relate to process management. In an embodiment, a computer-implemented method is disclosed. The method comprises identifying a theme of a process based on a set of communication information. The set of communication information is generated corresponding to performing the process. The method further comprises determining a plurality of roles in the process at least partly based on the set of communication information. The plurality of roles performs respective actions during performing the process. The method further comprises generating a framework of the process based on the theme and the plurality of roles. The framework comprises one or more branches. In other embodiments, a system and a computer program product are disclosed.

Abstract: A lightweight database-management system (DBMS) is based on a dynamic microservices architecture that implements each granular DBMS feature or function as a distinct, independently executable microservice. The DBMS's Parser front-end responds to each incoming query by selecting the first bind-time database feature needed to process the query. The Parser forwards its selection through a Channel-Binding subsystem to an Event Services Activation subsystem that activates a corresponding microservice to perform the selected feature. The first feature then selects the next required bind-time feature for activation, and this process continues sequentially until all required bind-time microservices have been identified, activated, and run. Runtime query-processing features are then sequentially selected in a similar manner. However, each selected runtime microservice is preloaded but not run.

Abstract: Query content-based data generation includes obtaining a query having an outer query and one or more subqueries, performing subquery transformation on each subquery, which converts predicates of the subqueries to be predicates of the outer query, and thereby obtain a transformed query, generate from the transformed query block(s) each having a list of predicates selected from the transformed query, processing each query block for column information, including column range information and column relationship information, and generating data and populating a dataset having table(s) and respective column(s) for each of the table(s). Generating the data uses the column range information and the column relationship information to select data for the dataset such that data records from the dataset are produced as results to executing the obtained query against the dataset.

Abstract: Embodiments are disclosed for a method for dynamic access paths. The method includes generating real-time statistics (RTS) estimates based on a log of a database. Further, the method includes generating access paths based on a structured query language command and the RTS estimates. The method also includes training a machine learning model to map the RTS estimates to the access paths.