Abstract: Disclosed herein are system, method, and computer program product embodiments for maintaining of a geometric object in a database. An embodiment operates by a database maintaining a first page storing a data block in the database's on-disk store such that the data block stores at least one byte of the geometric object. After receiving the request for the geometric object, the database loads the page storing the geometric object in the in-memory store and determines the size of the geometric object. Based on the size of the geometric object, the database stores the geometric object in the in-memory store directly or in a heap of the in-memory store.

Abstract: A method may include applying, to a content associated with a first supplier, a machine learning model to determine one or more objectives of an enterprise affected by an incident associated with the content. A change in a first risk associated with the first supplier may be detected based on the objectives affected by the incident. In response to detecting the change in the first risk of the first supplier, a cost associated with replacing the first supplier with the second supplier may be determined by applying the machine learning model to analyze a first electronic document associated with the first supplier. If the cost of replacing the first supplier with the second supplier and/or a second risk of the second supplier satisfy one or more thresholds, a second electronic document associated with the second supplier may be generated to address the second risk of the second supplier.

Abstract: A method, a system, and a computer program product for executing intent classification based on user feedback in a digital assistant environment. Using a natural language processor, an audio input received from user is processed. At least one implicit feedback parameter is extracted from the processed audio input. The feedback parameter classifies an intent derived from the audio input received from the user. The extracted feedback parameter is stored in a replay memory. The replay memory stores labeled data associated with the audio input received from the user. Based on the processed audio input and the labeled data, an initial response to the received audio input is determined. Modeling of the extracted implicit feedback parameter and the stored labeled data is executed. An updated response to the received audio input is generated.

Abstract: Technologies are provided for assigning developers to source code issues using machine learning. A machine learning model can be generated based on multiple versions of source code objects (such as source code files, classes, modules, packages, etc.), such as those that are managed by a version control system. The versions of the source code objects can reflect changes that are made to the source code objects over time. Associations between developers and source code object versions can be analyzed and used to train the machine learning model. Patterns of similar changes to various source code objects can be detected and can also be used to train the machine learning model. When an issue is detected in a version of a source code object, the model can be used to identify a developer to assign to the issue. Feedback data regarding the developer assignment can be used to re-train the model.

Abstract: A metastructure for representing and manipulating or analyzing a data model is provided herein. A source representation of the first data model may be received. The source representation may include a representation of one or more structural components of the first data model, respectively having identifiers and types. One or more instances of a metastructure schema object datatype may be generated based on the one or more structural components of the source representation. Generating a given instance of the metastructure schema object datatype for a given structural component may include assigning a first identifier of the given structural component to the first data member of the given instance, determining a first type associated with the given structural component, and assigning the first type to the second data member of the given instance. The one or more instances of the metastructure schema object datatype may be stored in association.

Abstract: Techniques are described for automatically incorporating lifecycle information for a secured environment (SE) into an intrusion detection system monitoring the secured environment's operations. In one example, a secured environment including at least one component is monitored, where the secured environment is associated with a lifecycle operations manager (LOM) responsible for managing lifecycle operations associated with at least one component in the SE. One or more log files associated with operations of each of the at least one components are obtained, along with log files associated with lifecycle operations executed by the LOM. A determination is made as to whether the particular activities documented in the log files indicate a violation of at least one malicious action rule. In response to determining that the log files are associated with a malicious action rule, a mitigation action associated with the violation is triggered.

Abstract: Systems, methods, and computer media for securing software applications are provided herein. Using deceptive endpoints, attacks directed to API endpoints can be detected, and attackers can be monitored or blocked. Deceptive endpoints can be automatically generated by modifying valid endpoints for an application. Deceptive endpoints are not valid endpoints for the application, so if a deceptive endpoint is accessed, it is an indication of an attack. When a deceptive endpoint is deployed, accessing the deceptive endpoint can cause an alert to be generated, and an account, user, or device associated with accessing the deceptive endpoint can be blocked or monitored.

Abstract: A system may include a historical managed software system data store that contains electronic records associated with controllers and deployed workloads (each electronic record may include time series data representing performance metrics). An entropy calculation system, coupled to the historical managed software system data store, may calculate at least one historical entropy value based on information in the historical managed software system data store. A detection engine, coupled to a monitored system currently executing a deployed workload in the cloud computing environment, may collect time series data representing current performance metrics associated with the monitored system. The detection engine may then calculate a current monitored entropy value (based on the collected time series data representing current performance metrics) and (iii) compare the current monitored entropy value with a threshold value (based on the historical entropy value).

Abstract: Systems and methods include reading of a spreadsheet workbook including a plurality of rows and a header row comprising a plurality of column labels, automatic determination of a hierarchical schema based on the plurality of column labels, the hierarchal schema comprising a hierarchy of nodes, where each of the plurality of column labels is associated with a single node of the hierarchy of nodes, and, for each row of the plurality of rows, determination of a value associated with each of the plurality of column labels, and, for each determined value, population of a node of an instance of the hierarchical schema associated with an associated column label.

Abstract: Techniques and solutions are provided for processing data in conjunction with one or more hyperscale computing systems. An interface is provided for translating calls from an application into a format used by a hyperscale computing system. The calls can be to read data from, or write data to, a hyperscale computing system. In particular examples, data to be read or written is data from a plurality of IOT devices, where each IOT device has one or more hardware sensors. An interface can also be used to configure how the hyperscale computing system processes data, such as determining how IOT data is stored or how aggregates are generated from IOT data.

Abstract: Disclosed herein are system, method, and computer program product embodiments for utilizing non-RAM memory to implement a cloud storage system. An embodiment operates by receiving a request from an on-premises computer system to securely access a cloud drive by receiving an object specific template for an object. Based on the object specific template, an object specific plugin is selected, wherein the object specific plugin is configured to provide a connection to a cloud-based repository to obtain real time data for the object. An instance of the object is generated and communicated to a cloud plugin, wherein the cloud plugin is configured to communicate to a specific cloud drive through an HTTP client and further upload the instance of the object to the specific cloud drive.

Abstract: Disclosed herein are various embodiments for blockchain service based application generator. An embodiment operates by determining a blockchain service configured to access, store, and receive updates to transactional data of the blockchain service. An enhanced data object including a wrapper with one or more additional properties corresponding to an original data object of the blockchain service is generated. The enhanced data object is transformed into a data protocol object. Metadata is generated based on the data protocol object. A data protocol service is generated based on the metadata. The application interface is configured based on the metadata, wherein the application interface is configured to communicate with both the data protocol service and the blockchain service. The application interface is provided to a user who is enabled to update the transactional data through the application interface.

Abstract: Systems and methods include execution of a first application associated with a first access schema and a first data model in a data schema of a database system. While the first application is receiving incoming user requests, the first data model in the data schema is extended based on a second data model of a second application, a second access schema of the second application is prepared in the database system, data of the first data model in the data schema is migrated to the second data model, data of the first data model in the data schema is replicated to the second data model while the first application is receiving incoming user requests, the second application is executed, the incoming user requests are re-directed to be received by the executing second application, and the replication of data of the first data model in the data schema to the second data model is stopped.

Abstract: An identifier of a data source specifying data is determined from a schema. The data source is called, and data to be analyzed is retrieved. From the schema, an identifier of a method is determined. The method associated with the identifier includes one or more rules. The method is called and used to determine data consistency with the one or more rules.

Abstract: A method may include performing, at multiple data partitions, a point-in-time recovery to a specified time by applying transactions that have committed at each data partition up to the specified time. Open transactions that have not been committed at each data partition at the specified time may be identified. A corresponding transaction coordinator may be queried for an outcome of the open transactions. Open transactions that affect a single data partition may be aborted. The point-in-time recovery may be performed at the transaction coordinator partitions by determining the outcome of the open transactions. The transaction coordinator partitions may abort any transaction that remains open subsequent to the point-in-time recovery. If the specified time may be greater than a safety threshold, data up to an earlier point-in-time than specified may be recovered depending on whether there are missing portions of transaction logs at the data partitions or the transaction coordinator partitions.

Abstract: Systems and methods are provided for accessing master data comprising a plurality of representative data records, where each representative data record represents a cluster of similar data records, and each similar data record has a confidence score indicating a confidence level that the similar data record corresponds to the cluster, and comparing a new data record to each representative data record of the plurality of representative data records using a machine learning model to generate a distance score. The systems and methods further provide for analyzing the cluster of similar data records corresponding to each representative data record in a selected set of representative data records to generate candidate values for the requested data field of the new data record, and generating a candidate score for each of the candidate values using the distance score and the confidence score to use in providing a recommended candidate value.

Abstract: Some embodiments provide a program that queries a database for a subset of a plurality of records in the database. Each record in the plurality of records includes a value for a field. The program further samples the subset of the plurality of records to identify a set of records in the subset of the plurality of records. The program also sorts the set of records based on the value for the field in each record in the set of records. The program further determines a first value for the field of a first record in the sorted set of records and a second value for the field of a second record in the sorted set of records forms a slope that is greater than or equal to a defined slope. The program determines a threshold value for the subset of the plurality of records based on the first record.

Abstract: Disclosed herein are various embodiments for a system for the automatic creation of database objects. An embodiment operates by receiving an image of a definition of an unknown type of database object to be created in a database. A first keyword of one or more keywords is identified in a text of the image, wherein the text includes one or more values associated with the first keyword. It is determined that the unknown type of database object corresponds to the first type of database object based on the identified keyword. A database object of the first type of database object is generated for the database based on the one or more values.

Abstract: A system for shutting down a process of a database is provided. In some aspects, the system performs operations including tracking, during startup of a process, code locations of a process in the at least one memory. The operations may further include tracking, during runtime of the process and in response to the tracking the code locations, memory segments of the at least one memory allocated to the process. The operations may further include receiving an indication for a shutdown of a process. The operations may further include waking, in response to the indication, at least one processing thread of a plurality of processing threads allocated to a database system. The operations may further include allocating a list of memory mappings to the plurality of processing threads. The operations may further include freeing, by the first processing thread, the physical memory assigned to the processing thread by the memory mappings.

Abstract: A language determination model may be applied to select a first machine learning model or a second machine learning model to analyze the input text. The first machine learning model trained to analyze text in a first language, the second machine learning model trained to analyze text in a second language, and the input text may be in a third language. The language determination model may select the first machine learning model based on the first machine learning model having a better performance analyzing text in the third language than the second machine learning model. The language determination model may be updated based on an actual performance of the first machine learning model analyzing the input text. Moreover, the first machine learning model may be subject to additional training if the actual performance of the first machine learning model analyzing the input text is below a threshold value.