Abstract: Method, computer program product, and computer system are provided. Traffic is collected against a NoSQL database by an activity collector. A database transaction log is periodically extracted and analyzed. The collected traffic and the analyzed database transaction log are input to building a knowledge base of database access patterns. Current traffic is captured and used to compute an activity threshold. Traffic is directed to a workload processor based on the activity threshold. Traffic is directed to an intensive insert/update/delete (IUD) processor in response to the activity threshold exceeding a configured threshold. A plurality of temporary shards is generated along with an adaptive key and adaptive index in the plurality of temporary shards. The intensive IUD processor traffic is re-directed to the plurality of temporary shards while the activity threshold exceeds the configured threshold.

Abstract: An embodiment includes generating a caller list of callers that issue requests for calling a designated program at runtime. The embodiment also includes generating an authorized caller list of authorized callers allowed to call the designated program, wherein the authorized callers are selected from among callers on the caller list. The embodiment also includes generating an authorization key for each of the authorized callers that the designated program will require as a condition for completing call requests.

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 computer-implemented method, computer program product and computer system are provided. A processor receives an indication of sensitive data in one or more files. A processor updates at least one bit in the virtual address space for the one or more files indicated to have sensitive data. A processor, in response to a program accessing the one or more files, evaluates a respective virtual address for the one or more files. A processor, in response to the at least one bit in the respective virtual address for the one or more files, marks intermediate data generated by the program as sensitive data.

Abstract: File system performance enhancement is provided. Information related to a container of the computer accessing a file in a layer of a file system of an image corresponding to the container is recorded to form recorded file system layer access information. The recorded file system layer access information is shared with another file system of a container image corresponding to a different container on the computer. The container image is a same image as the image corresponding to the container that accessed the file to perform an operation associated with a cloud application.

Abstract: Mechanisms are provided for scheduling a workload in a cloud computing system. A cloud affinity factor (CAF) computer model is trained, via a machine learning process based on a training dataset comprising static characteristics of a workload binary for a workload, and dynamic characteristics corresponding to historical performance data for the workload, such that the trained CAF computer model predicts a performance classification for a given workload binary. The trained CAF computer model processes a new workload to generate a performance classification for the new workload. Cloud affinity factor(s) are generated based on the performance classification for the new workload. Node affinity and dispatch rule(s) are applied to the cloud affinity factor(s) to select one or more nodes of the cloud computing system to which to dispatch the workload. The workload is then scheduled on the selected one or more nodes.

Abstract: An approach for managing data set access based on data set relevance. The approach monitors data set access activities associated with a user. The approach detects access of a first data set by the user. The approach determines a group of data sets associated with the first data set based on a data set mapping associated with the user. The approach recalls one or more data sets of the group of data sets from a slower storage device to a faster storage device.

Abstract: A serverless computing-based, continuous gateway watch of a data store for data change process is provided. The process includes the gateway interface of the computing environment receiving a watch request from a user system to monitor the data store for data change. Based on receiving the watch request, the gateway interface invokes a serverless setup service to establish a connection between the gateway interface and the data store of the computing environment to be monitored for data change. Based on receiving, at the gateway interface, a data change indication from the data store, the gateway interface invokes a serverless message process service to mutate the data change indication from the data store into a mutated data change message indicative of a data change at the data store for return to the user system pursuant to the watch request, with the serverless message process service terminating thereafter.

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: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include training a model. The operations may include enhancing the model with reinforcement learning and improving stability of the model with a graph neural network model. The operations may include predicting, with the model, a resource cost of a node and deploying the node.

Abstract: A workload is generated to verify a system. The generating includes parsing one or more statements of an input test case to create one or more parsed structures. A comparison is performed of at least one parsed structure of the one or more parsed structures and at least one workload structure of at least one existing workload to identify one or more workload structures as matching the at least one parsed structure. Runtime data relating to at least the one or more workload structures identified as matching is obtained. The workload is created based on at least one matching workload structure of the one or more workload structures identified as matching, the at least one parsed structure of the one or more parsed structures and the runtime data.

Abstract: A method, computer program product, and computer system are provided. Based on an insert/update/delete (IUD) feature being enabled, and based on receiving an IUD request at a massively parallel processing (MPP) database management system (DBMS) engine, identifying a partition optimized for the IUD request. A partition map is loaded corresponding to a partition optimized for only IUD requests. Based on the IUD request being an insert row operation, a new distribution key is calculated by hash. A new partition map is generated, the insert row operation is executed using the new partition map.

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: A query string for an encrypted database storing a plurality of encrypted data records is received from a requestor. The query string is segmented to obtain at least one word. The at least one word is encrypted with the irreversible encryption algorithm to obtain at least one encrypted word. At least one first encrypted item with a co-occurrence weight higher than a preset threshold based on the at least one encrypted word and a co-occurrence statistics model is acquired. The co-occurrence statistics model is built to provide co-occurrence weights, each indicating a probability that the at least one encrypted word appears in a first encrypted data item of the plurality of encrypted data records. At least one second encrypted data item corresponding to the at least one first encrypted data item is acquired from the plurality of encrypted data records.

Abstract: A system may include a memory and a processor in communication with the memory configured to perform operations. The may operations include obtaining transaction logs in blocks from nodes of a data storage system. The operations may include, for each transaction log, splitting the transaction log into log entries, grouping log entries into groups associated with a same data source, and writing the log entries of the groups to empty blocks such that log entries from different groups do not share a same block. The operations may include identifying a same sequence of log entries from the written transaction logs and uploading first blocks of a first transaction log, including the same sequence of log entries, to an object-based storage without uploading second blocks of a second transaction log including the same sequence of log entries to the object-based storage.

Abstract: This disclosure provides a method, a computing system and a computer program product for managing resource sharing among devices with different platforms. The method comprises obtaining connection information for connecting to a requesting device by decoding authentication information, wherein the authentication information is created based at least on an identifier of the requesting device. The method further comprises sending a request including the connection information to a request proxy, wherein the request is redirected by the request proxy to a plurality of devices having more than one platform. The method further comprises receiving a response to the request, wherein the response is obtained by combining responses received by the request proxy from the plurality of devices.

Abstract: Disclosed are a computer-implemented method, a system and a computer program product for record management in a time series database (TSDB). In the computer-implemented method for record management in a TSDB, time series records can be obtained from distributed sources by one or more processing units. The records can be divided into different record groups based on a timestamp of each of the records by one or more processing units. The records in each of the record groups can be normalized into normalized records by one or more processing units. The normalized records can be stored in the TSDB by one or more processing units.

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.