Abstract: A testing system and method for testing application code against various failure scenarios. The testing system and method generate a test workflow including test source code implementing a series of actions that affect an application component and or an infrastructure component included in application code. The testing system and method execute the test workflow to determine the performance of the application code during one or more failure scenarios caused by the series of actions included in the test workflow. Performance data generated by the test code is analyzed by a performance analysis service or method to identify limitations of the application code and build resiliency patterns that address the limitations and improve the performance of the application code.

Abstract: In accordance with an embodiment, an analytic applications environment enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud environment; and supports the development of computer-executable software analytic applications. A data pipeline or process, such as, for example, an extract, transform, load process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema.

Abstract: Methods, systems, and computer-readable storage media for providing a first access schema in a view layer of the database system, the first access schema including first database objects and projection views, each projection view corresponding to a second database object of second database objects in a data persistency, transitioning access of sessions of an application from the data persistency to the first access schema, executing at least a portion of the maintenance procedure in the database system, the at least a portion of the maintenance procedure changing one or more second database objects within the data persistency, creating a second access schema, and transitioning access of the sessions of the application from the first access schema to the second access schema, transitioning access of the sessions of the application from the second access schema to the data persistency, and removing the second access schema from the database system.

Abstract: In some instances, a method for ingesting data files is provided. The method comprises: obtaining, by an ingestion system associated with a distributed file system, a data file from an external data source; determining, by the ingestion system, a plurality of processing threads within the ingestion system to use to process the data file; determining, by the ingestion system, one or more offsets within the data file based on the plurality of determined processing threads; and ingesting, by the ingestion system, the data file by having each of the plurality of processing threads begin processing the data file at a plurality of different locations within the data file, wherein the plurality of different locations are associated with the one or more offsets.

Abstract: A method of generating a dependence graph, an apparatus, a device, a storage medium and a program product are provided. The method includes: obtaining a hook API used by a function component; obtaining a parameter value of the hook API used by the function component; and generating a dependence graph. The hook API corresponds to a hook node in the dependence graph, the parameter value corresponds to a value node in the dependence graph, and there is a connection relationship between the value node and a corresponding hook node.

Abstract: Various examples are directed to systems and methods for migrating an analytics tool from a source deployment to a target deployment. A migration tool may access source metadata describing a plurality of source deployment queries to the at least one source deployment database. The migration tool may identify a plurality of source deployment database artifacts using the source metadata and migrate the plurality of source deployment database artifacts to a target database. The migration tool may apply a trained model to the plurality of source deployment database artifacts to generate a set of data relationships and generate a first analytics report from the migrated plurality of source deployment database artifacts at the target database, the first analytics report corresponding to at least one of the set of data arrangements.

Abstract: Methods and systems for securing unstructured data are provided. One method includes generating, by a processor, a schema from unstructured data, the schema including one or more relationships between named entities of the unstructured data; identifying, by the processor, a plurality of semantic relationships between the named entities; determining, by the processor, a sensitive relationship from the plurality of semantic relationships; and anonymizing, by the processor, sensitive data associated with the sensitive relationship by replacing, a first portion of the sensitive data with generalized information.

Abstract: An owner-tenant wireless shared omnibus access control system configured to enable owners and tenant users of an electronic access control system to seamlessly access a lock that is shared between remote site owners and one or more tenants, while maintaining access control and audit trail with minimal administrative overhead. An owner-tenant wireless shared omnibus access control system may be operably configured to enable expanded or limited access and audit trail information based on different user roles and permissions comprising an owner user and a tenant user.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

Abstract: Reverting between codified states in a cloud-based storage system, including: generating, in response to receiving a request to change a configuration of the cloud-based storage system to an updated state, a snapshot state of a current state of the cloud-based storage system; transitioning the configuration of the cloud-based storage system from the snapshot state to a next state toward the updated state; and reverting, in response to not configuring the cloud-based storage system in accordance with the updated state, configuration of the cloud-based storage system from the next state to the snapshot state.

Abstract: Embodiments provide systems, methods, and computer program products that utilize artificial intelligence/machine learning to process database change data and correlated performance data to predict the impact of database changes and generate rules with respect to database changes to prevent undesired behavior or promote increased performance.

Abstract: A method or system for generating recommendations based on detected correlations of static and dynamic data. For example, the system generates feature inputs for the machine learning model that is based on labeled static data as well as dynamic data indicating current usage. Notably, the dynamic data is not retrieved from a data store of known training data, but instead is streamed in real time from active databases accessible to the network. Furthermore, the system provides a formatting mechanism that translates user selected requirements and parameters from a human-readable format indicating particular attributes of a database to optimize (e.g., database performance, security, compliance, capacity planning, etc.) into data that may be included in a machine learning feature input that specifies which correlations to use (e.g., server operational statistics data, server log data, monitoring metrics, etc.).

Abstract: Systems and methods for automatically training an AI request processing system are disclosed. These utilize a request-response AI chatbot implemented with rules-based computer code and which has a functional connection to a generative language AI chatbot. The latter is implemented using an attention-based neural network. On receiving a request, a request/response database is checked. When the request is known and valid, the corresponding response is returned. When the request is not known, or not valid, the request is converted and sent as a prompt to the generative language AI chatbot and a reply received. The reply is converted to a response, stored on the request/response database along with a new validity, and output as the response. In one embodiment, the request is automatically amended before sending as a prompt in order to obtain a tailored reply. In another, the reply is automatically edited to obtain a tailored response.

Abstract: The present disclosure relates to a method, device, and system for configuring parameters, a computer device, a medium, and a product. A configuration device for configuring parameter sampling with respect to an edge device includes: one information acquiring unit, configured to acquire information related to the purpose and use environment of the edge device; one transmitting unit, configured to transmit the information to a cloud platform; and one configuration information determining unit, configured to receive configuration information for parameter sampling with respect to the edge device from the cloud platform, where the configuration information is configuration information determined as matching the information by the cloud platform utilizing a configuration model stored thereby.

Abstract: An apparatus comprises at least one processing device that includes a processor coupled to a memory. The processing device is configured to store configuration information, for utilization by software of a host device, in at least one file of a first database of a first type, and in conjunction with an upgrade of at least a portion of the software, to export the configuration information from the at least one file of the first database to one or more files in an intermediate format, to remove the at least one file of the first database, to export the configuration information from the one or more files in the intermediate format to at least one file of a second database of a second type different than the first type, and to configure the upgraded software to utilize the configuration information of the at least one file of the second database.

Abstract: Methods and apparatus are described for splitting a page (or node) in an index tree database index for a multi-master database. The methods include creating a first split page of a first page. The first page belongs to a first set of pages and comprises a hinge page. The hinge page also belongs to a second set of pages. Updating the first set of pages may cause the hinge page itself to split, creating a split of the hinge page. The second set of pages is updated in response to creating the split of the hinge page. Described are techniques whereby the updates to the first and second sets of pages are atomic, that is, they all succeed or they all fail, which may realize any of several benefits.

Abstract: Techniques for generating distributed representations of computing processes and events are provided. According to one set of embodiments, a computer system can receive occurrence data pertaining to a plurality of computing processes and a plurality of events associated with the plurality of computing processes. The computer system can then generate, based on the occurrence data, (1) a set of distributed process representations that includes, for each computing process, a representation that encodes a sequence of events associated with the computing process in the occurrence data, and (2) a set of distributed event representations that includes, for each event, a representation that encodes one or more event properties associated with the event and one or more events that occur within a window of the event in the occurrence data.

Abstract: Disclosed herein are system, method, and computer program product embodiments for accessing a database collection. In some embodiments, an application generates a database collection. The database collection is configured to store documents (e.g., records). The application generates a database table definition. The database table definition is a proxy or adapter for the database collection. The application receives a request to be executed to be executed on the database collection from a client device. The request specifies the database table definition. The application accesses the database collection, via the database, to process the request using the database table definition.

Abstract: A computer-implemented method includes receiving a request of data records for a data schema, obtaining a schema alignment which maps the data schema to a subset of an ontology of a knowledge graph, generating at least one query based on the subset of the ontology of the knowledge graph, obtaining a list of data records by executing the at least one query on the knowledge graph to obtain a list of data records, and persisting the list of data records to the data schema.

Abstract: An apparatus includes at least one node device to host a computing cluster, and at least one processor to generate a UI providing guidance through a set of configuration settings for the computing cluster, wherein, for each configuration setting that is received as an input during configuration, the at least one processor is caused to: perform a check of the set of configuration settings to determine whether the received configuration setting creates a conflict among the set of configuration settings; and in response to a determination that the received configuration setting creates a conflict among the set of configuration settings, perform operations including generate an indication of the conflict for presentation by the UI, and receive a change to a configuration setting as an input from the input device.

Abstract: The various embodiments described herein include methods, devices, and systems for reading and writing data from a database table. In one aspect, a method includes: (1) initiating a read transaction to read from a first non-key column of a row in the database table, the database table having a plurality of rows, each row comprising a primary key and a plurality of non-key columns, the initiating including: (a) determining that a write transaction holds a lock on a second non-key column of the row in the database table, and (b) determining that no lock is held on the first non-key column; and (2) in response, concurrently reading data from the first non-key column and writing a new column value to the second non-key column; where each non-key column includes a last-write timestamp that indicates when the last write occurred for the respective non-key column.

Abstract: Techniques are disclosed for generating a database schema using trained machine learning models that, in some embodiments, may include graph neural networks (GNN). A GNN may identify source to target database schema mappings using, among other features of the graph, context data associated with each node in a graph. Context data describes relationships between a particular node and some (or all) of the other nodes in the graph. The system may use this context data (and other graph data) in combination with a trained GNN model to identify a mapping between one or more source database entities to corresponding target database entities.

Abstract: The technology disclosed relates to application-specific data flow for synthetic request injection for cloud security enforcement. In particular, it relates to data flow logic configured to inject an incoming request directed to a cloud application in a processing path of a particular network security system.

Abstract: A database entry may be stored in a container in a database table corresponding with a partition key. The partition key may be determined by applying one or more partition rules to one or more data values associated with the database entry. The database entry may be an instance of one of a plurality of data object definitions associated with database entries in the database. Each of the data object definitions may identify a respective one or more data fields included within an instance of the data object definition.

Abstract: Systems and processes for efficient accessing, storing and transmitting of fixed data elements and dynamic data elements, each having its own native form. The data elements are organized according to a schema, with (a) all fixed data elements stored in their native forms in a fixed memory allocation, and (b) each dynamic data element stored in memory in its own native form, in its own data allocation. With this memory structure, computational overhead of converting data elements from their native forms to JSON, XML or other markup language is avoided, making accessing data (getting), updating data (setting), converting data to a serial stream for transmission or other manipulation (serializing), deserializing, and other manipulations of the data elements much more CPU efficient and requiring less bandwidth.

Abstract: Disclosed herein are systems and methods of executing scanning software, such an executable software program or script (e.g., PowerShell script), by a computing device of an enterprise, such as a security server, may instruct the computing device to search all or a subset of computing devices in an enterprise network. The scanning software may identify PowerShell scripts containing particular malware attributes, according to a malicious-code dataset. The computing system executing the scanning software may scan through the identified PowerShell scripts to identify particular strings, values, or code-portions, and take a remedial action according to the scanning software programming.

Abstract: Systems are provided for facilitating the disclosed methods for performing event storage and diagnostic processing within a hybrid cloud environment. Event records are gathered and batched at an on-premises server. The event records are also appended with correlation vector data that enables the event records to be correlated with other events. The batch of event record batches are signed with a security key associated with a cloud storage container and the on-premises server is restricted to writing the batch of event records to the container. In some instances, the size of the batch is based on a duration of time for collecting records, which can be adjusted to accommodate for missing data.

Abstract: A partition merging method includes obtaining, by a first database server, first metadata of a first partition based on an identifier of a current file of the first partition, obtaining second metadata of a second partition based on an identifier of a current file of the second partition, and generating third metadata of a third partition based on the first metadata and the second metadata.

Abstract: The present invention relates to communications methods and apparatus dynamically detecting and/or mitigating anomalies in communications systems/networks. An exemplary method embodiment includes the steps of: (i) storing a set of N key performance indicator (KPI) models; (ii) associating each of a plurality of recurring time slots of a recurring time frame on a per group member basis with one of the N KPI models wherein the associating including associating a first group member of a first group and a first recurring time slot with a first one of the N models, the first one of the N models being a first model; (iii) receiving event data for the first group member for a first time period; and (iv) determining based on the first model if a key performance indicator value for the first group member and first time period is anomalous.

Abstract: Network appliances can record log entries in log objects. An object store can receive the log objects and can use the log objects to create index objects and flow log objects. Each flow log object and index object can be associated with a time period wherein the flow log object includes flow log entries received during that time period. The index object includes shard tables that can be stored in different nonvolatile memories and can thereby be concurrently searched. Shard entries in the shard tables indicate flow entry indicators. The flow entry indicators indicate log entries in the flow log object. An internally indexed searchable object can include the flow log object and the index object. Numerous indexed fields in the flow log entries and can be indexed with each indexed field searchable via the shard entries.

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: The present disclosure is directed to a modified GraphQL server that enables application developers to define custom GraphQL schema fragments. For example, a developer may provide the GraphQL server with a GraphQL standard definition language (SDL) file having annotations that indicate that a customized data-handling script, such as a custom data-fetching script or a custom type resolver script, be called for handling certain data types or fields. These customized data-handling scripts are resolved and called at runtime based on the annotations in the GraphQL SDL file. By enabling developers to define schema fragments in the SDL language and implement data-handling scripts in a suitable scripting language, schema portability can be maintained across client instances, as well as instance release versions.

Abstract: Generally discussed herein are devices, systems, and methods for machine learning (ML) by flowing a dataset towards a target dataset. A method can include receiving a request to operate on a first dataset including first feature, label pairs, identifying a second dataset from multiple datasets, the second dataset including second feature, label pairs, determining a distance between the first feature, label and the second feature, label pairs, and flowing the first dataset using a dataset objective that operates based on the determined distance to generate an optimized dataset.

Abstract: Various aspects of the subject technology relate to methods, systems, and machine-readable media for generating histograms according to a set-theoretic approach. The method includes receiving a command to generate at least one histogram from selected data, the selected data comprising a plurality of column groups. The method also includes identifying a superset from the plurality of column groups. The method also includes materializing, in a memory, a superset multi-column group for the superset. The method also includes identifying at least one subset of the superset from the plurality of column groups, the at least one subset comprising at least one column group. The method also includes processing the at least one subset of the superset to form a grouped resultset. The method also includes generating the at least one histogram for the grouped resultset of the at least one subset of the superset.

Abstract: A method and/or system includes: adding a new source of data to be stored in the data storage system; obtaining the schema for the new source of data to be stored in the data storage system; storing the data to be stored in the data storage system in a Not Only Structured Query Language (NOSQL) database in the data storage system; and storing schema for the data to be stored in the data storage system in a metadata store in the electronic data storage system wherein the metadata store is separate from the NOSQL database.

Abstract: Pointer alignment in a computer programming to obtain information enabling a compiler to optimize program code. Equivalence classes of pointers are collected in a program using a flow-insensitive yet field-sensitive pointer analysis operation iterating through an entire program code of the program. The equivalence classes of pointers, once collected, are mapped to and recorded in an equivalence class mapping table (ECTable). A portion of the collected equivalence classes of pointers are identified, from the ECTable, as pointer candidates for a pointer alignment computation according to a code pattern analysis of each pointer candidate. The code pattern analysis is based on available alignment information, and whether the alignment information would enable a compiler to optimize pointer references of the candidate pointer. The pointer alignment computation is then performed for each identified pointer candidate to obtain the alignment information used to optimize execution of the program.

Abstract: In accordance with an embodiment, an analytic applications environment enables data analytics within the context of an organization's enterprise software application or data environment, or a software-as-a-service or other type of cloud environment; and supports the development of computer-executable software analytic applications. A data pipeline or process, such as, for example, an extract, transform, load process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema.

Abstract: A method for providing database management system support to a source application configured to operate with a source database management system Is provided. The method comprises detecting database management system communications issued by the source application to the source database management system; and responsive to said database management systems communications, impersonating the source database management system to the source application.

Abstract: Logs to metrics synthesis includes receiving a log message. It further includes translating the log message into a metrics data point comprising a timestamp, a metric name, a metric value, and a set of metadata key-value pairs. It further includes determining a time series in which to insert the metrics data point into which the log message was translated. It further includes inserting the metrics data point into the determined time series. It further includes updating a metadata catalog based at least in part on the metrics data point.

Abstract: Techniques are provided for processing a database command in a sharded database. The processing of the database command may include generating or otherwise accessing a shard key expression, and evaluating the shard key expression to identify one or more target shards that contain data used to execute the database command.

Abstract: In one aspect, a computerized method includes the step of building an information technology operations analytics (ITOA) stack. The method includes the step of ingesting and storing data at rest. The method includes the step of storing a set of metadata of the ingested data in a virtual machine or a container. The method includes the step of maintaining a set of primary data of the ingested data with an object store. The method includes the step of packaging one or more data units of the set of primary data. The method includes the step of writing over objects at select intervals. The method includes the step of breaking objects into chunks to reduce overwrites. The method includes the step of laying the data out as partitioned by time. The method includes the step of creating one or more application boundaries at the time of ingesting.

Abstract: In some implementations a method may be providing that includes importing database tables from a source system to a target system; obtaining, from a data dictionary, a first metadata about a foreign key association for a first database table to at least one of the database tables; obtaining, from a scan of code, a second metadata about an after import method for the first database table; in response to the second metadata representing that the after import method does not exist for the first database table, storing the first database table in the order table; and in response to the second metadata representing that the after import method does exist for the first database table, scanning for a first indication that the after import method corresponds to a read of at least one other database table before storing the first database table in the order table.

Abstract: A method and an apparatus for organization and detection of homogeneous and heterogeneous swarms of devices and application of swarm intelligence using swarm intelligence framework are provided. The Swarm Intelligence Framework provides a generic platform for realizing solutions involving Swarm Intelligence Technology via flexible container-based Algorithm Plug-in Architecture which is essential to utilize Swarm Intelligence Framework for various scenarios and use cases, including dynamically loading and using the Swarm Detection Algorithm.

Abstract: Techniques are disclosed relating to detecting one or more incorrect fields in user submissions, using machine learning techniques. A corrective system may access information for a plurality of fields of a tracking data structure for a user submission. In some embodiments, the corrective system predicts correct values for multiple fields of the tracking data structure using a plurality of respective different machine learning classifier modules. In some embodiments, the classifier modules use different sets of the plurality of fields as inputs and the multiple fields include a priority of the user submission and an assignee for the user submission. In some embodiments, in response to determining that at least one of the predicted correct values does not match a current value for a corresponding field of the tracking data structure, the computing system stores information indicating the mismatch and may automatically correct one or more fields.

Abstract: A method and system for serving assets is disclosed, comprising receiving an asset request to serve an asset, wherein the asset request originates at an application, and wherein the asset request comprises an advertisement of an asset to be served and a request for the network address of an asset server configured to serve the requested asset. The method further comprises causing a service discovery server to identify an asset server configured to serve the requested asset, and causing the requested asset to be served to the application.

Abstract: Automatic system management is provided by controlling/managing part or all of the system operation through the predefined properties embedded in the components of the management system itself. The creation of Quanta with properties allow the programming of the rules for the system management in a way that any user, client, provider or any other type of participant can interact with the system without the need of an intermediary or approver.

Abstract: In one implementation, a data transformation engine is employed to analyze an incoming JSON schema syntax and use a “best effort” model to convert a plurality of JSON schemas to other types of schemas such as an XML schema. The model is configured to be adaptable and dynamic in order to convert as much of the JSON as possible. In addition, the conversion tool is configured to transform schemas to other technologies and allow for conversion errors within one or more error thresholds. Further, the system may be distributed standalone with no dependencies to other implementations, such as BPM implementations.

Abstract: Methods and systems for securing unstructured data are provided. One method includes generating, by a processor, a schema from unstructured data, the schema including one or more relationships between named entities of the unstructured data; identifying, by the processor, a plurality of semantic relationships between the named entities; determining, by the processor, a sensitive relationship from the plurality of semantic relationships; and anonymizing, by the processor, sensitive data associated with the sensitive relationship by replacing, a first portion of the sensitive data with generalized information.

Abstract: A system receives data associated with a communication between one or more individuals. The data is split between each of the one or more individuals into text associated with that individual. Each of the text is modified to remove stop words and to duplicate key words. The text is merged to form a text corpus, from which a bag of words model is generated. Topics of the bag of words are classified using a topic classifier model. A purpose is identified based on the returned topic and keywords from the topic classifier model. Returned topics and keywords from the topic classifier model are linked to the communication.

Abstract: A DBMS manages a high-performance accelerated database that is synchronized with a conventional client database. The accelerated database contains both “regular” accelerated tables, which each duplicate a table of the client database, and accelerator-only tables (AOTs) that are unique to the accelerated database and that may be used for analytical purposes. AOT rows are archived by moving the rows to a dedicated accelerator-only archive stored in the accelerated database. When a user query attempts to access accelerator-only data, the DBMS rewrites the query to adapt the requested operations to the accelerated database's partitioned archive/non-archive structure. The rewritten query specifies steps for accessing archived and non-archived accelerator-only data without forcing the DBMS front-end to generate a merged view of archived and non-archived accelerator-only data.