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: 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: 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: 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: 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: Increasing index search efficiency and availability in a database is provided. An index space matrix corresponding to a plurality of mini-indices of the database is built using ordered boundary values that correspond to key value ranges of each respective mini-index to increase index search efficiency by removing redundant mini-index accesses. Mini-indices of the plurality of mini-indices are consolidated asynchronously without suspending access to the mini-indices using a particular consolidation policy of a plurality of consolidation policies that corresponds to an amount of key value overlap identified between the mini-indices. Data operations corresponding to transactions are performed using the index space matrix during consolidation of the mini-indices without suspending access to the mini-indices.

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: The invention relates to an isolated DC power supply utilizing a Class-E inverter and its design method, within the field of power electronics and high voltage technology. This DC power supply comprises a DC power source, a Class-E inverter, a CL network, a series resonant unit, and a rectifier circuit, featuring two structures. It is characterized by a single switching device and a stable output voltage that does not vary with load changes. This resolves stability issues commonly associated with isolated DC power supplies based on Class-E inverters. The invention also introduces a design method that includes calculating core parameters like capacitors and inductors, ensuring optimal component selection. Offering strong isolation, constant voltage output, high efficiency, and cost-effectiveness, this DC power supply is ideal for use in wireless power transfer systems, high-frequency power supplies, and isolated sensor and gate driver power supplies.

Abstract: In several aspects, a computing device analyzes data to determine its characteristics. The computing device selects at least one compression process based on the characteristics. A compression switch and a compression level are dynamically adjusted based on multiple factors including available system resources, desired storage savings and performance requirement. The compression level dynamically varies depending on the data being processed and a workload on a system. Performance of compression and decompression operations are continuously monitored for dynamically adjusting compression parameters to optimize performance.

Abstract: An embodiment includes executing a querying process that returns database documents containing text associated with a database environment. The embodiment tokenizes the database documents into a series of n-gram tokens and groups the tokens into topic classes using natural language processing (NLP). The embodiment also generates a feature map by applying a convolution layer to an image that depicts a database structure graph of the database environment. The embodiment detects an architectural element in the image by applying a region proposal network (RPN) to the feature map and classifies the architectural element into one of the plurality of topic classes using a trained neural network. The embodiment renders, responsive to a user selection of the architectural element, an overlay depicting information about the architectural element using text and other architectural elements that are in a same topic class as the user-selected architectural element.

Abstract: Self-tuning merged code testing is provided which includes testing merged code using a suite of test cases, where the merged code includes one or more code changes, and obtaining, based on the testing, a test case failure using the suite of test cases. Further, the process includes determining, using an artificial intelligence engine, a likely faulty code change of the one or more code changes resulting in the test case failure, and customizing, based on the likely faulty code change, the suite of test cases to facilitate verifying that the likely faulty code change is a faulty code change. In addition, the process includes continuing testing of the merged code using the customized suite of test cases to facilitate verifying that the likely faulty code change is the faulty code change.

Abstract: A computer-implemented method, including receiving, by a processor set, a query including a query string for a system catalog; identifying, by the processor set, a default index structure of the system catalog; executing, by the processor set, the query based on the default index structure of each index in the system catalog; ranking, by the processor set, a performance of each execution of the query by each index; mapping, by the processor set, a query pattern to a corresponding index of the system catalog; selecting, by the processor set, the index to perform the query using a machine learning (ML) model trained with a knowledge base that includes the ranking and the mapping; executing, by the processor set, the query on the selected index; and in response to executing the query on the selected index, returning, by the processor set, a result of the query.

Abstract: A method, computer program product, and computer system for dynamically allocating shared memory among multiple execution threads via use of a dynamically changing memory threshold. A trained machine learning model (MLM) is executed to calculate the memory threshold (MTH) using values of one or more input parameters as input to the MLM. After executing the MLM, a request is received from a requesting execution thread for a requested amount (MR) of the shared memory. In response to the request, the shared memory is distributed among one or more current execution threads currently using the shared memory and the requesting execution thread. The redistributing is performed as a function MTH, MR, MU, and MC, wherein MU is a total amount of the shared memory currently being used by the one or more current execution threads, and wherein MC is a memory capacity of the shared memory.

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: A system for recycling textiles includes a sorting module configured to automatically remove trims and to sort textiles into the fabric streams; a first decoloring system configured to receive the non-black and white cotton fabric stream and to provide a de-colored cotton fabric stream; a dry sanitization system configured to provide a sanitized cotton fabric stream and a sanitized polyester fabric stream; a second decoloring system configured to provide a semi de-colored cotton/polyester blend fabric stream; a fabric separation system configured to provide pure polyester fiber; a decoloring system configured to provide a de-colored polyester fabric stream; a cotton fiber recovery system configured to receive the de-colored or black or white and sanitized cotton fabric, and sanitized denim fabric streams; and a polyester fiber recovery system configured to receive the de-colored or black or white and sanitized polyester fabric streams.

Abstract: A computer-implemented method to inject security semantics into database queries. The method includes receiving, by a database system, a query, wherein the query is received from a host and the query is generated by a first user account. The method also includes, generating an access plan for the query. The method further includes, determining a first portion of the access plan matches a first security syntax. The method includes, injecting, in response to determining the first portion of the access plan matches the first security syntax, the first security syntax into the access plan. The method further includes, executing the query. The method includes, returning a set of results of the query to the host.

Abstract: A computer-implemented method masks shard operations in a distributed database. The method includes performing a shard operation on a first table with a first column and a second column, where the shard operation divides the first table into a first shard with the first column and a second shard with the second column. The method also includes receiving a query, where the query is configured to perform at least a first command. The method further includes determining the first command is directed to the first column. The method includes updating the query to access the first shard in place of the first table. The method also includes executing the query and returning a set of results for the query to a source of the query.

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.