Abstract: Provided are a computer program product, system, and method for analyzing user activity data to generate activity information for use by generative artificial intelligence (AI) to generate reports. Activity data includes information on an activity in which a monitored user engages, including a time period during which the monitored user was engaged in the activity and a description of the activity. An activity categorizer comprising a machine learning model classifier processes the time period and the description of the activity; to output an activity category. A status report generator generates a status report for the activity category, including the activity category of the activity and key attributes of the activity category including the time spent on the activity category and information on the activity. The status report is updated to a database.

Abstract: Methods for managing a connection pool for cloud-based applications accessing databases are provided. Aspects include establishing a plurality of connections to one or more databases, receiving connection requests from a plurality of cloud-based applications to access the one or more databases, and allocating the connection requests to among the plurality of connections, where the allocation is determined based at least in part on one or more connection pool parameters. Aspects also include monitoring a usage of the plurality of connections by the plurality of applications and updating the one or more connection pool parameters based on an analysis of the usage of the plurality of connections by the plurality of applications.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtains queries from sources via a query interface. The program code analyzes the queries to obtain attributes of the queries. The program code applies a trained classifier to the attributes to classify the queries into finite categories based on a predicted usage of a given resource type exceeding a pre-determined threshold and an anticipated execution duration for each query. The program code determines, based on the finite categories of the one or more queries and resource configurations of resources comprising the database service, which resource of the resources can efficiently execute each query of the one or more queries. The program code schedules each query of the one or more queries for execution by the determined resources.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: generating database logging data of a database, wherein the database includes a certain leaf page associated to a certain row interval; examining logging data of the database logging data, and based on the examining the logging data, determining that a contention risk condition is present with respect to the certain row interval; and in response to the determining that the contention risk condition is present with respect to the certain row interval, storing incoming subsequent INSERT query statement transaction data for the certain row interval to an external indexing structure external to the certain leaf page.

Abstract: Techniques are described with respect to a system, method, and computer program product for managing a virtual environment context. An associated method includes analyzing a plurality of avatars associated with the virtual environment; ranking each avatar of the plurality of avatars based on the analysis; grouping at least a subset of the avatars based on a scoring of each avatar associated with the ranking; and modifying the virtual environment for the subset in accordance with the virtual environment context based on the analysis.

Abstract: An embodiment registers a node and a data object on a blockchain. The embodiment schedules by a Scheduler an object task associated with the data object in a Data Controller, generating a task execution token by the Data Controller on the blockchain where the task execution token is associated with the object task. The embodiment senses the task execution token by the node received from the Data Controller; responsive to the sensed task execution token executes the object task on the node and generating a node energy consumption metric and an energy consumption token of the object task on the blockchain. The embodiment also sends the energy consumption token to the Data Controller from the node causing a smart contract to compute a task energy consumption metric based on the node energy consumption metric where the data object on the blockchain is updated with the task energy consumption metric.

Abstract: Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.

Abstract: In an approach to improve the privacy of chat groups within a virtual world, in response to identifying a private chat group has been created between two or more users, embodiments collect, by an internet of things (IoT) sensor set, a voice for each user in the private chat group. Further, embodiments analyze, by an analysis module, the collected voice, an intonation associated with the collected voice and an original pronunciation associated with the collected voice, and generate a de-content pronunciation based on the analysis of the collected voice, the intonation, and the original pronunciation. Additionally, embodiments output, by the IoT sensor set, the intonation and the original pronunciation to the two or more users in the private chat group and output, by the IoT sensor set and a client computer, the intonation and the de-content pronunciation to one or more users outside the private chat group.

Abstract: A system and method for dynamically visualize and modify user authority and security resource edge based on access behaviors in a cloud computing environment. The method provides a new access control perspective to ensure the credibility of database object usage patterns, build clearer and explicit security data consumption mode for data usage requests, and guide tenants to have a more comprehensive understanding and concise use. Processing components and method are configured to gather data and analytics including collecting data resource and database object usage tagging both resources and users with security level by machine learning and building an access model for each role. The method uses graph modeling with created, trained access patterns for users protected resources and produces in-memory implicit sub-groups for users with similar access requirements and limits their resource accesses according to their history behaviors.

Abstract: Mechanisms are provided to consolidate event data structures in an electronic calendar. An event data structure for an event is received and features are extracted from the event data structure and another having an overlapping temporal location in the electronic calendar. The event data structure is classified as to whether it is duplicative of the other event data structure and the event data structures are rendered based on the classification. This rendering may involve consolidating the event data structures into a consolidated event data structure if they are duplicative, and prioritizing the event data structures relative to each other if they are not duplicative.

Abstract: A method, including: monitoring resource utilization of an operating system (OS) with applications utilizing larger pages; determining the monitored resource utilization is greater than a threshold resource utilization; in response to the determining the monitored resource utilization is greater than a threshold resource utilization, determining a respective larger pages index value for each of the applications utilizing larger pages; and turning off larger pages utilization of a subset of the applications utilizing larger pages, wherein the subset comprises a predefined number of the applications utilizing larger pages that have highest determined larger pages index values.

Abstract: A machine learning power consumption model and process are provided for determining power consumption of computing environment resources in handling requests of an application. The process includes training the machine learning power consumption model to estimate power consumption of computing environment resources in handling requests. The training uses a training dataset derived from historical request-related data, and the training dataset includes request-related data and resource power consumption data. In addition, the process includes analyzing the requests. The analyzing includes, for a particular request of the requests, obtaining a common pattern of resource use and collecting real-time trace metrics to facilitate allocating resource use to the particular request.

Abstract: Embodiments analyze an incoming query for a database, train a query model using machine learning based on the analyzing of the incoming query, determine a model result based on the trained query model; extract a similar information query based on the model result, generate a recommendation for query tuning and a visual impact of the recommendation for the query tuning based on the extracting of the similar information query, and output the recommendation for the query turning and the visual impact of the recommendation for the query tuning.

Abstract: Provided are a computer program product, system, and method for data analysis of interaction information for interacting participants to determine a connection intention. An audio and/or a visual feed collected at an event is processed to determine whether interacting participants at the event are engaged in an interaction. A duration of the interaction, attributes of the interaction, and personal profile information of the interacting participants are determined. The duration of the interaction, the attributes of the interaction, and the personal profile information are processed to determine a connection intention indicating whether the interacting participants should connect or should not connect. Requests for the interacting participants to connect are transmitted in response to determining the connection intention indicates the interacting participants should connect.

Abstract: A method includes: obtaining at least one real-time console log from a compute instance; tagging the at least one real-time console log with at least one log category based on at least one entry within the at least one real-time console log; generating at least one categorized console log; generating at least one encrypted categorized console log based on a public encryption key; publishing the at least one encrypted categorized console log to a log bus; communicating the at least one encrypted categorized console log over at least one multi-port secure tunnel to a user terminal device of a subscribed user; and publishing a private encryption key to the user terminal device of the subscribed user wherein the private encryption key facilitates decrypting the at least one encrypted categorized console log.

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: For a set in sets of candidate factors, a classification model is trained to predict a computer problem possibility, an accuracy score of the classification model is determined based on model validation, and factor weights of the candidate factors in the set are adjusted based on the accuracy score. This processing is done with respect to all sets of candidate factors. A low accuracy classification model having an accuracy score lower than a threshold criterion is selected. A higher accuracy classification model having an accuracy score that is higher than the accuracy score of the low accuracy classification model is selected. The set of candidate factors used to train the low accuracy classification model is updated using one or more of the candidate factors used to train the higher accuracy classification model. The low accuracy classification model is updated based on the updated set of candidate factors.

Abstract: Computer technology for combining an encryption/decryption (e/d) key with additional information to obtain a specialized e/d key. The additional information one or more of the following types of additional information: client UUID (universally unique identifier), FQDN (fully qualified domain name), database hardware information, data physical position on the hard disk and/or stored data creation date. By combining the basic key with these kind(s) of operational information and/or software/hardware identifier information, the security of the underlying encrypted data can be meaningfully enhanced.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtains a given query referencing a LOB. The program code obtains statistics and activities performed in the database system, establishes performance benchmarks, and analyzes the given query to determine how the LOB is utilized. The program code determines that meeting a benchmark requires replacing a reference to the LOB with a reference to implicit column(s). The program code executes the given query by referencing the implicit column(s) to obtain query results. The program code returns the query results.

Abstract: A computer-implemented method, system and computer program product for improving accuracy and efficiency of auditing databases. A table, list or index of a database is analyzed to identify metadata, which includes time series data, user data, an Internet Protocol address and operation data. The identified metadata is associated with the corresponding record or row of the table, list or index from which the metadata was extracted. A determination is then made as to whether to record a raw data image associated with the record or row of the analyzed table, list or index based on the corresponding data operation. The identified metadata as well as the recorded data images, if any, are stored in a structured audit log. Auditing information is then obtained from a structured audit log based on matching the record or row identifier (RID) associated with the query with the RID associated with the structured audit log.