Abstract: An electronic pen core body includes a front end portion configured to project outward from an opening on one end side in an axial direction of a tubular casing of an electronic pen. A plurality of projections and depressions are provided along a circumferential direction of the electronic pen core body and/or along an axial direction of the electronic pen core body, in at least a part of the front end portion. The part of the front end portion is configured to come into contact with an input surface when the electronic pen performs an indication input on the input surface by the electronic pen.

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: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to training an AI model to predict status of a DBMS. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a data ingestion component that can use testing data of an AI model to generate ingested data by randomly changing one or more records of at least one feature comprised in the testing data, wherein the ingested data can be used to compute a first ratio indicative of inequity of the at least one feature. The computer executable components can further comprise a training component that can train the AI model using at least the first ratio to predict a status of system.

Abstract: Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

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: Provided is an electronic pen including a tubular first casing having an opening on a pen tip side, and a tubular second casing rotatably coupled to the first casing with an axial direction of the first casing as a center to receive an instruction of a user, on a side of the first casing opposite to the pen tip side in the axial direction. The first casing includes a first circuit board having a first circuit including a circuit that performs signal interaction with a position detecting sensor. The second casing includes a second circuit board having a second circuit. The first and second circuit boards are electrically connected to each other by a flexible cable disposed in a state of having a surplus portion that can be extended is extendable or contractable in the axial direction and is twistable in a rotational direction about the axial direction.

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: 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: 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: 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.