Abstract: An embodiment for analyzing and tracking data flow to determine proper schemas for unstructured data. The embodiment may automatically use a sidecar to collect schema discovery rules during conversion of raw data to unstructured data. The embodiment may automatically generate multiple schemas for different tenants using the collected schema discovery rules. The embodiment may automatically use ETL to export unstructured data to SQL databases with the generated multiple schemas for the different tenants. The embodiment may automatically monitor usage data of the SQL databases and collect the usage data. The embodiment may automatically optimize schema discovery using the collected usage data. The embodiment may automatically discover schemas with hot usage and apply the discovered schemas with hot usage to other tenants for consumption and further monitoring.

Abstract: An end cover assembly, an air cylinder, a tread sweeper and a railway vehicle.

Abstract: A method, computer system, and a computer program product is provided for computer log management. In one embodiment, in response to receiving a log request from a user, an input content is analyzed and adjusted according to input contents and user's previous activities. A similarity analysis and a fairness analysis is performed to determine similarities between the input content, as adjusted, and a plurality of log records in an object library. The similarity analysis includes analyzing any patterns and attributes. The attributes have a dimension, and each dimension has a predefined weight (W). The fairness analysis ensures that one type of log is not favored over others. A best possible match is then determined, and one or more logs are presented to the user providing the best possible match.

Abstract: One or more computer processors generate a debug chain from one or more similar resource bound breakpoints, wherein the debug chain provides dynamic code flow. The one or more computer processors distribute the generated debug chain to one or more tenants.

Abstract: A disclosed database system and enhanced methods implement enhanced mini-plans and dynamically changing a query mini-plan with trustworthy Artificial Intelligence (AI) to improve query execution performance in a database system. An AI cost model evaluates candidate mini-plans for executing a query. AI truth monitors evaluate the execution of the mini-plans, such as predicted input factors and adjusted mini-plans of one or more AI running data models. The AI truth monitors provide feedback to adjust the AI cost model based on evaluating the execution of the mini-plans. The AI truth monitors validate adjusted mini-plans, provide feedback to the AI cost model with improved overall prediction accuracy, and enhanced mini-plans to gain query performance.

Abstract: An embodiment includes generating a partition schema for a distributed database based on historical usage data indicative of usage of the distributed database, where the generating of the partition schema comprises determining a partition range of a partition of the partition schema. The embodiment also includes generating a node identifier for the partition using a hash function and a first weight value assigned to the partition. The embodiment also includes monitoring performance data indicative of a performance of the distributed database, the monitoring comprising detecting a failure of the performance to satisfy a performance threshold. The embodiment also includes initiating, responsive to detecting the failure, a redistribution procedure by changing the node identifier of the partition by replacing the first weight value with a second weight value.

Abstract: Provided are techniques for building and using a sparse Time Series Database (TSDB). Time series records are received from a native TSDB, where each of the time series records includes a timestamp and one or more tags. Timeslots are determined for shards for the sparse TSDB based on the timestamp included in each of the time series records. The sparse TSDB is built by creating the shards for the determined timeslots and storing the time series records in the shards, while filling in empty ranges in the shards. A query that specifies at least one of the one or more tags is received. It is determined whether to execute the query against the sparse TSDB, and, in response to a determination to execute the query against the sparse TSDB, the query is executed against the sparse TSDB to generate results that are returned.

Abstract: In a computer-implemented method for improving performance of a database, a processor receives batch jobs for a relational database. The batch jobs may include a first member with a first buffer pool, and a second member with a second buffer pool. The processor may also identify a first actual object and an isolation level for the batch jobs, generate related queries based on the first actual object and the isolation level, calculate a cost for the first member and the second member to run the batch jobs based on the related queries, and assign the batch jobs to the first member based on a lower calculated cost.

Abstract: Embodiments of the present disclosure relate to a method, system, and computer program product for data management. According to the method, one or more processors divide data into a plurality of partitions. The one or more processors store the plurality of partitions in a plurality of nodes of a mixed distributed database system, wherein a first node of the mixed distributed database system comprises a plurality of databases, and wherein at least a part of the plurality of partitions are shared by the plurality of databases of the first node and being not shared by other of the plurality of nodes.

Abstract: An embodiment for analyzing and tracking data flow to determine proper schemas for unstructured data. The embodiment may automatically use a sidecar to collect schema discovery rules during conversion of raw data to unstructured data. The embodiment may automatically generate multiple schemas for different tenants using the collected schema discovery rules. The embodiment may automatically use ETL to export unstructured data to SQL databases with the generated multiple schemas for the different tenants. The embodiment may automatically monitor usage data of the SQL databases and collect the usage data. The embodiment may automatically optimize schema discovery using the collected usage data. The embodiment may automatically discover schemas with hot usage and apply the discovered schemas with hot usage to other tenants for consumption and further monitoring.

Abstract: A supervised similarity measure machine learning method, system, and computer program product that includes generating embeddings by training a supervised deep neural network (DNN) on a feature data to determine which nodes correspond to which clustered learning group of clustered learning groups, performing half-distributed learning by distributing data in a time-series database to the clustered learning groups, and evaluating, based on the embeddings, new tenant data in the clustered learning groups with an upward bow pose.

Abstract: A processor receives an optimization target for a first cluster in a distributed computing environment. A processor trains a neural network based on the optimization target. A processor generates a decision tree based on the neural network. A processor selects a workload executing in the first cluster of the distributed computing environment. A processor identifies a second cluster to relocate the workload. A processor determines a current optimization of the distributed computing environment based on the workload being retained in the first cluster. A processor determines a migration optimization of the distributed computing environment based on the workload being migrated to the second cluster. A processor, in response to the migration optimization improving the current optimization of the distributed computing environment, migrates the workload to the second cluster.

Abstract: A computer-implemented method includes: collecting, by a computing device, database activities and database structure information of a database; identifying, by the computing device, related columns in the database; determining, by the computing device, one or more data types for column transference of the identified related columns; generating, by the computing device, a super union column based on the column transference and the identified related columns; and updating, by the computing device, the database with the super union column.

Abstract: An embodiment includes generating a partition schema for a distributed database based on historical usage data indicative of usage of the distributed database, where the generating of the partition schema comprises determining a partition range of a partition of the partition schema. The embodiment also includes generating a node identifier for the partition using a hash function and a first weight value assigned to the partition. The embodiment also includes monitoring performance data indicative of a performance of the distributed database, the monitoring comprising detecting a failure of the performance to satisfy a performance threshold. The embodiment also includes initiating, responsive to detecting the failure, a redistribution procedure by changing the node identifier of the partition by replacing the first weight value with a second weight value.

Abstract: In a computer-implemented method for improving performance of a database, a processor receives batch jobs for a relational database. The batch jobs may include a first member with a first buffer pool, and a second member with a second buffer pool. The processor may also identify a first actual object and an isolation level for the batch jobs, generate related queries based on the first actual object and the isolation level, calculate a cost for the first member and the second member to run the batch jobs based on the related queries, and assign the batch jobs to the first member based on a lower calculated cost.

Abstract: An approach is provided in which the approach segments each one of multiple components corresponding to multiple component levels in an SQL database system into multiple functions. The approach combines a first one of the multiple functions with a second one of the multiple functions into an image, and invokes the image to process an SQL query using the first function and the second function.

Abstract: A method of managing time series data workload requests includes receiving a workload job request from a user in a multi-tenant network, the request specifying a plurality of workloads, each workload including time series data configured to be stored in a time series database (TSDB), inputting workload information to a workload model that is specific to the user, and classifying each workload according to the workload model, the workload model configured to classify each workload based on a plurality of parameters, the plurality of parameters including at least a workload type and an amount of storage associated with each workload. The method also includes assigning each workload of the plurality of workloads into one or more workload groups based on the classifying, and executing each workload according to the workload type and the storage size.

Abstract: A method, system, and computer program product for abnormal data detection. According to the method, a plurality of data points collected at different time points are classified into a plurality of groups. A plurality of groups of potential abnormal data points are determined from the plurality of groups. Correlations between a first group of the plurality of groups of potential abnormal data points with other groups of potential abnormal data points are determined. In response to the first group of the plurality of groups of potential abnormal data points being uncorrelated to a majority of the other groups of potential abnormal data points based on the correlations, data points in the first group are identified as abnormal data points.

Abstract: Modifying an index for a database is provided. An index tree for the database is traversed by: identifying a next node in the index tree, requesting a lock of the next node in the index tree, responsive to locking of the next node in the index tree, releasing a lock of a current node of the index tree, and making the next node in the index tree the current node of the index tree, until the current node of the index tree is a leaf node of the index tree. Responsive to determining to split the leaf node, the lock of the leaf node is released and, after releasing the lock of the leaf node, a lock of a non-leaf node parent of the leaf node in the index tree is requested.

Abstract: Modifying an index for a database is provided. An index tree for the database is traversed by: identifying a next node in the index tree, requesting a lock of the next node in the index tree, responsive to locking of the next node in the index tree, releasing a lock of a current node of the index tree, and making the next node in the index tree the current node of the index tree, until the current node of the index tree is a leaf node of the index tree. Responsive to determining to split the leaf node, the lock of the leaf node is released and, after releasing the lock of the leaf node, a lock of a non-leaf node parent of the leaf node in the index tree is requested.