A system and method is described for database split generation in a massively parallel or other distributed database environment including a plurality of databases and a data warehouse layer providing data summarization and querying functionality. A database table accessor of the system obtains, from an associated client application, a query for data in a table of the data warehouse layer, wherein the query includes a user preference. The system obtains table data representative of properties of the table, and determines a splits generator in accordance with one or more of the user preference or the properties of the table. The system generates, by the selected splits generator, table splits dividing the user query into a plurality of query splits, and outputs the plurality of query splits to an associated plurality of mappers for execution by the associated plurality of mappers of each of the plurality of query splits against the table.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
2.
Machine Learning Based Duplicate Invoice Detection
Embodiments detect duplicate invoices, each invoice including a plurality of fields. Embodiments generate synthetic training data using a plurality of training invoices and generating one or more modified fields for each of the plurality of training invoices. Embodiments train a machine learning model using the synthetic training data and generate a plurality of candidate invoice pairs. Embodiments input the plurality of candidate invoice pairs to the trained machine learning model and generate, by the trained machine learning model, a prediction of whether each of the candidate invoices pairs is a duplicate invoice pair.
G06F 16/215 - Amélioration de la qualité des données; Nettoyage des données, p.ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
3.
DATA AUGMENTATION AND BATCH BALANCING FOR TRAINING MULTI-LINGUAL MODEL
A computer-implemented method includes: accessing a plurality of datasets, where each dataset of the plurality of datasets includes training examples; selecting datasets that include the training examples in a source language and a target language; and sampling, based on a sampling weight that is determined for each of the selected datasets, the training examples from the selected datasets to generate the training batches; training an ML model for performing at least a first task using the training examples of the training batches, by interleavingly inputting the training batches to the ML model; and outputting the trained ML model configured to perform the at least the first task on input utterances provided in at least one among the source language and the target language. The sampling weight is determined for each of the selected datasets based on one or more attributes common to the training examples of the selected dataset.
G06F 40/58 - Utilisation de traduction automatisée, p.ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
The present disclosure is related to techniques for converting a natural language utterance to a logical form query and deriving a natural language interpretation of the logical form query. The techniques include accessing a Meaning Resource Language (MRL) query and converting the MRL query into a MRL structure including logical form statements. The converting includes extracting operations and associated attributes from the MRL query and generating the logical form statements from the operations and associated attributes. The techniques further include translating each of the logical form statements into a natural language expression based on a grammar data structure that includes a set of rules for translating logical form statements into corresponding natural language expressions, combining the natural language expressions into a single natural language expression, and providing the single natural language expression as an interpretation of the natural language utterance.
Techniques are described for a hierarchical caching mechanism enabling efficient cross-region replications. In some embodiments, replication-related information (e.g., key-value pairs) is stored in a particular layout in a binary tree (B-tree) of a file system for replication processing. A hierarchy of caches storing a first type of information (e.g., crypto keys associated with iNodes) may be arranged to match the particular layout in the B-tree to enable efficient parallel processing of a second type of information (e.g., files, file data, or symbolic links), where the replication-related information in the B-tree is partitioned into multiple key ranges for parallel processing. In some embodiments, the caches in different hierarchies may be shared by different parallel-processing key ranges and replication jobs in a file system.
A node within a group of participant nodes begins an election by sending a vote request to the other nodes in the group. The vote request sets an input term argument to a future term value without incrementing the actual current term value. The current term value at each participant node is only incremented in response to a successful leadership change. At startup time, a candidate node issues a vote request with a non-disruptive election type. An established leader automatically rejects a non-disruptive vote request. A heartbeat loss vote request is rejected by each receiving node if its own heartbeat timeout does not exceed a predetermined limit. A mandatory vote request informs the leader node that it should stop requesting new workload. This is used in manual leadership transition to make sure that the old leader does not accept new transactions during the leadership transition.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
7.
REINFORCEMENT-LEARNING-AGENT-BASED GUI METRICS FOR MONITORING SYSTEM EFFECTIVENESS
Systems, methods, and other embodiments associated with reinforcement learning agent-based metrics for describing monitoring system strength are described. In one embodiment, a method to test effectiveness of a transaction monitoring system includes executing a reinforcement learning agent to perform a sequence of test transactions that cumulatively transfer an amount without detection by a scenario. The set of test transactions is recorded along with responses made by the transaction monitoring system in response to each test transaction being performed. A metric that represents the effectiveness of the transaction monitoring system for resisting suspicious activity is generated based on the sequence of test transactions and the responses. A visualization of the metric to represent the effectiveness of the transaction monitoring system for resisting suspicious activity is generated for display in a graphical user interface.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Systems, methods, and other embodiments associated with computer deepfake detection are described. In one embodiment, a method includes converting audio-visual content of a person delivering a speech into a set of time series signals. Residual time series signals of residuals that indicate an extent to which the time series signals differ from machine learning estimates of authentic delivery of the speech by the person are generated. Residual values from one synchronous observation of the residual time series signals are placed into an array of residual values for a point in time. A sequential analysis of the residual values of the array is performed to detect an anomaly in the residual values for the point in time. In response to detection of the anomaly, an alert that deepfake content is detected in the audio-visual content is generated.
G10L 17/26 - Reconnaissance de caractéristiques spéciales de voix, p.ex. pour utilisation dans les détecteurs de mensonge; Reconnaissance des voix d’animaux
Herein is database query acceleration from dynamic discovery of whether contents of a persistent column can be stored in an accelerated representation in storage-side memory. In an embodiment, based on data type discovery, a storage server detects that column values in a persistent column have a particular data type. Based on storage-side metadata including a frequency of access of the persistent column as an offload input column for offload computation requests on a certain range of memory addresses, the storage server autonomously decides to generate and store, in storage-side memory in the storage server, an accelerated representation of the persistent column that is based on the particular data type. The storage server receives a request to perform an offload computation for the offload input column. Based on the accelerated representation of the persistent column, execution of the offload computation is accelerated.
In accordance with an embodiment, described herein is a system and method for providing cross-microservice query processing. The system provides an object service framework that supports the use of microservices that may be loosely-coupled but related in some way, for example in that they interoperate together or require access to each other's data in order to process queries. Each microservice can be developed, deployed and evolve independently, and interact with the other microservices through contracts or interfaces that are defined as public APIs and are then exposed via the framework. The object service framework can be used, for example to provide a cross-microservice layer that automatically transforms queries that join objects in different microservices into a single database query that is optimized for use with the database.
Approaches of reassigning a home region from a first data center to a second data center as requested by a customer are described herein. The home region is able to implement write operations to a domain, whereas other data centers cannot implement write operations to the domain. The customer can request the home region being reassigned to another data center such that the customer can utilize the other data center to implement write operations to the domain.
Techniques for perspective-preserving seamless application switching are disclosed. A system may display a first interface using a first application. The first interface includes interface elements representing a plurality of objects. The system may detect a zoom-in command, received by the first application, requesting a particular zoom level for a first interface element, corresponding to a first object in the first plurality of objects. The system may determine that the requested zoom level exceeds a threshold. Responsive to determining that the requested zoom level exceeds the threshold, the system may display, using a second application, a second interface corresponding to the first object. The second interface may include one or more of: (a) characteristics associated with the first object that were not displayed by the first application, or (b) user input elements for executing operations associated with the first object that were not displayed by the first application.
Embodiments permit scope limited access to a user's secure information using non-fungible tokens (NFTs). A user can register with a secure information manager and control the scope with which the user's secure information is shared. For example, the user can permit a vetted entity access to the user's secure information via a portable access point. The user can select scope definition that control how the user's secure information is shared with the vetted entity. The vetted entity can scan the user's portable access point and request a credential. The credential can be a NFT that is assigned access privileges that correspond the user's selections. The vetted entity can then issue data access request(s) using the credential. The secure information manager can permit the vetted entity scope limited access to the user's secure information that corresponds to the access privileges assigned to the NFT.
Techniques for lazy compaction are disclosed, including: selecting, by a garbage collector, multiple regions of a memory for inclusion in a relocation set; populating, by the garbage collector, a lazy free list (LFL) with the multiple regions selected for inclusion in the relocation set; subsequent to populating the LFL: determining, by an allocator, that an ordinary free list managed by the garbage collector is depleted; responsive to determining that the ordinary free list is depleted: selecting a region in the LFL; executing one or more load barriers associated respectively with one or more objects marked as live in the region, each respective load barrier being configured to relocate the associated object from the region if the associated object is still live; subsequent to executing the one or more load barriers: allocating the region.
In one or more embodiments, a software service allows software providers to implement machine learning (ML) features into products offered by the software providers. Each ML feature may be referred to as an encapsulated ML application, which may be defined and maintained in a central repository, while also being provisioned for each user of the software provider on an as-needed basis. Advantageously, embodiments allow for a central definition for an ML application that encapsulates data science and processing capabilities and routines of the software provider. This central ML application delivers a ML deployment pipeline template that may be replicated multiple times as separate, tailored runtime pipeline instances on a per-user basis. Each runtime pipeline instance accounts for differences in the specific data of each user, resulting in user-specific ML models and predictions based on the same central ML application.
Techniques for generating recommendations for re-ordering scheduled tasks to improve completion times of one or more tasks are disclosed. A system displays a representation of a schedule for performing tasks among work centers in a work environment. The system identifies performance metrics associated with a current configuration of tasks in the work environment. The system further analyzes performance metrics for alternative task schedules in the work environment. The system displays interface elements to allow a user to re-order tasks among the work centers in the work environment. The system also displays predicted performance metrics associated with the alternative task schedules. When a user selects a particular interface element to implement an alternative task schedule, the system generates instructions to work centers to reorder tasks in the work environment.
A method and one or more non-transitory storage media are provided to train and implement a one-hot encoder. During a training phase, computation of an encoder state is performed by executing a set of relational statements to extract unique categories in a first training data set, associate each unique category with a unique index, and generate a one-hot encoding for each unique category. The set of relational statements are executed by a query optimization engine. Execution of the set of relational statements is postponed until a result of each relational statement is needed, and the query optimization engine implements one or more optimizations when executing the set of relational statements. During an encoding phase, a set of categorical features in a second training data set are encoded based on the encoder state to form a set of encoded categorical features.
Techniques are described herein for analyzing and tuning database workloads to optimize application performance. In some embodiments, a workload analyzer identifies a captured workload that includes a set of database queries executed within a particular timeframe. The workload analyzer compares the workload within one or more other workloads executed within a previous timeframe to determine differences between the different workloads. For example, the workload analyzer may identify changes in the distributions of queries, including how many queries are unchanged, missing, and/or new. The workload analyzer may further detect changes in the performance of individual queries. The workload analyzer may determine the overall performance impact of such changes on the total workload. Based on the analysis, the workload analyzer may generate reports, alerts, tuning advice, and/or recommendations to boost performance.
Embodiments permit scope limited access to a user's secure information using non-fungible tokens (NFTs). A user can register with a secure information manager and control the scope with which the user's secure information is shared. For example, the user can permit a vetted entity access to the user's secure information via a portable access point. The user can select scope definition that control how the user's secure information is shared with the vetted entity. The vetted entity can scan the user's portable access point and request a credential. The credential can be a NFT that is assigned access privileges that correspond the user's selections. The vetted entity can then issue data access request(s) using the credential. The secure information manager can permit the vetted entity scope limited access to the user's secure information that corresponds to the access privileges assigned to the NFT.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
20.
GENERATING TAGGED CONTENT FROM A LIST IN AN ELECTRONIC DOCUMENT
Techniques for maintaining list-type text formatting when converting content from a source content format to a destination content format are disclosed. A system generates text content by applying text formatting tags to segments of characters obtained from a source electronic document. The system parses a static-display type source electronic document to obtain character data of the characters in the source document. The system analyzes the parsed data to identify text arranged in a list-type text format in the source document. The system generates text content in a destination content format different from the source format by applying tags to segments of the text content designating the segments items in a list.
G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
21.
AUTHORIZATION FRAMEWORK IN A MULTI-CLOUD INFRASTRUCTURE
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
22.
IMPLEMENTING COMMUNICATIONS WITHIN A CONTAINER ENVIRONMENT
Techniques are described for implementing a container environment where each pod within the container environment is provided with a unique IP address and a virtual communication device such as an IPvlan device. Communications from source pods are directly routed to destination pods within the container environment by one or more virtualized network interface cards (VNICs) utilizing the unique IP addresses of the destination pods, without the need for bridging and encapsulation. This reduces a size of data being transmitted and also eliminates a compute cost necessary to perform encapsulation of data during transmission.
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
23.
DISTANCE-BASED LOGIT VALUE FOR NATURAL LANGUAGE PROCESSING
Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p.ex. des réponses automatiques ou des messages générés par un agent conversationnel
24.
SYSTEMS AND METHODS FOR SECURELY USING CLOUD SERVICES ON ON-PREMISES DATA
The present disclosure relates to systems and methods for providing cloud-based services securely to on-premises networks or other infrastructure. More particularly, the present disclosure relates to systems and methods for enriching first-party data (e.g., data collected directly by an on-premises server) stored within on-premises networks by enabling the on-premises networks to retrieve and process third-party data stored on cloud-based networks. As a technical benefit, cloud-based services can be performed on the first-party data within the on-premises networks.
H04L 41/0893 - Affectation de groupes logiques aux éléments de réseau
H04L 67/1001 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour accéder à un serveur parmi une pluralité de serveurs répliqués
H04L 67/133 - Protocoles pour les appels de procédure à distance [RPC]
Techniques for processing top-K queries are provided. In one technique, a database statement is received that requests top-K results related to a database object and that indicates two columns thereof: a first column by which to partition a result set and a second column by which to order the result set. A buffer is generated. For each of multiple rows in the database object: a first key value that associated with a first value in the first column of said each row is identified; a second key value that associated with a second value in the second column of said each entry is identified; a slot in the buffer is identified based on the first key value and the second key value; and the slot in the buffer may be updated based on the second key value. A response to the database statement is generated based on the buffer.
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.
Techniques for managing digital messages to and from a proxy message address are disclosed. A system receives a message directed to a particular destination address. The system replaces any source address included in the message with a proxy address. When the system receives a reply to the message, the reply is directed to the proxy address. The system analyzes message data to identify a target address for the reply message. The system identifies contextual data associated with the reply message. The system transmits the reply message, and the contextual data, to the target address.
JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.
A Lock-Free Reservation mechanism is provided. When a transaction issues an update that affects a value in a “reservable column” of a row, the database server does not immediately obtain a lock that covers the row. Instead, the database server adds a reservation to a reservation journal. At the time the transaction commits, a lock is obtained and the requested update is made. In one implementation, before adding the reservation to the reservation journal, the database server determines whether making the update would violate any constraints involving the reservable column. In one implementation, the constraint check not only takes into account the current value of the data item that is being updated and the amount of the update, but also pre-existing reservations in the reservation journal that affect the same data item.
Techniques are disclosed herein for integrating document question answering in an artificial intelligence-based platform, such as a chatbot system. The techniques include receiving a query from a user, rewriting the query to include one or more specific descriptors, computing an embedding vector for the rewritten query, retrieving one or more textual passages from a document store utilizing the embedding vector for the rewritten query, determining one or more answers to the rewritten query within the one or more textual passages, and returning the one or more answers.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p.ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/216 - Gestion de l'historique des conversations, p.ex. regroupement de messages dans des sessions ou des fils de conversation
32.
USER SIGN-UP FOR SERVICES OFFERED IN A MULTI-CLOUD INFRASTRUCTURE
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
Method includes populating fake value for each of entities, to generate string of fake entity values that correspond to entities; inserting sentinel token between adjacent fake values included in the string to generate first input data; generating, by natural language generation model, natural language sentences based on first input data, natural language sentences including one or more fake values from the string; analyzing natural language sentences to determine whether any fake value from the string is missing; based on the fake value missing, summarizing, using text summarization model, natural language sentences to generate text summary; concatenating the text summary with the fake value, to generate second input data; and generating, by a next sentence generation model, additional natural language sentence using the second input data, the additional natural language sentence including the fake value. Additional natural language sentence is combined with natural language sentences to generate final natural language sentences.
Techniques for lazy compaction are disclosed, including: selecting, by a garbage collector, multiple regions of a memory for inclusion in a relocation set; populating, by the garbage collector, a lazy free list (LFL) with the multiple regions selected for inclusion in the relocation set; subsequent to populating the LFL: determining, by an allocator, that an ordinary free list managed by the garbage collector is depleted; responsive to determining that the ordinary free list is depleted: selecting a region in the LFL; executing one or more load barriers associated respectively with one or more objects marked as live in the region, each respective load barrier being configured to relocate the associated object from the region if the associated object is still live; subsequent to executing the one or more load barriers: allocating the region.
Systems, methods, and other embodiments associated with computer deepfake detection are described. In one embodiment, a method includes converting audio-visual content of a person delivering a speech into a set of time series signals. Residual time series signals of residuals that indicate an extent to which the time series signals differ from machine learning estimates of authentic delivery of the speech by the person are generated. Residual values from one synchronous observation of the residual time series signals are placed into an array of residual values for a point in time. A sequential analysis of the residual values of the array is performed to detect an anomaly in the residual values for the point in time. In response to detection of the anomaly, an alert that deepfake content is detected in the audio-visual content is generated.
In accordance with an embodiment, described herein is a system and method for use with a data analytics or other computing environment, for on-demand fetching of backend server logs into a frontend environment, such as for example a browser. Such on-demand log fetching can be specific to the working context that is for current session and current request; and can be accomplished by appending a parameter or flag to a current request. For each step associated with an instruction being performed, the method can create a timestamp within one or more log files associated with the instruction; and fetch the one or more log files associated with the instruction. Performance logs are then included with a dashboard response, and logged into the browser's console.
Described herein is a system and method for providing an integrated function editor, for use with a data analytics environment. The function editor can be utilized to create and register functions available within a cloud infrastructure or cloud environment, for use within a data analytics environment. Such functions available for use within the cloud infrastructure or cloud environment can be displayed for the user, and used, for example, in data analytics workbooks, to create an interface or API that allows connection of the data analytics environment to a cloud infrastructure database.
G06F 16/21 - Conception, administration ou maintenance des bases de données
38.
CONSENSUS PROTOCOL FOR ASYNCHRONOUS DATABASE TRANSACTION REPLICATION WITH FAST, AUTOMATIC FAILOVER, ZERO DATA LOSS, STRONG CONSISTENCY, FULL SQL SUPPORT AND HORIZONTAL SCALABILITY
A consensus protocol-based replication approach is provided. For each change operation performed by a leader server on a copy of the database, the leader server creates a replication log record and returns a result to the client. The leader does not wait for consensus for the change operation from the followers. For a commit, the leader creates a commit log record and waits for consensus. Thus, the leader executes database transactions asynchronously, performs replication of change operations asynchronously, and performs replication of transaction commits synchronously.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
G06F 16/22 - Indexation; Structures de données à cet effet; Structures de stockage
A consensus protocol-based replication approach is provided. Chunks are grouped into replication units (RUs) to optimize replication efficiency. Chunks may be assigned to RUs based on load and replication throughput. Splitting and merging RUs do not interrupt concurrent user workload or require routing changes. Transactions spanning chunks within an RU do not require distributed transaction processing. Each replication unit has a replication factor (RF), which refers to the number of copies/replicas of the replication unit, and an associated distribution factor (DF), which refers to the number of servers taking over the workload from a failed leader server. RUs may be placed in rings of servers, where the number of servers in a ring is equal to the replication factor, and quiescing the workload can be restricted to a ring of servers instead of the entire database.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
40.
ACCELERATING QUERY EXECUTION BY OPTIMIZING DATA TRANSFER BETWEEN STORAGE NODES AND DATABASE NODES
Techniques for accelerating query execution by optimizing data transfer between storage nodes and database nodes are provided. In one technique, a compute node receives a database statement and transmits a set of one or more selection criteria associated with the database statement to a storage node. Based on the database statement, the storage node retrieves a set of data blocks from storage. Each data block comprises multiple rows of an index-organized table (IOT), each row comprising a key section and a non-key section. The storage node applies the set of selection criteria to a data block, resulting in a modified data block. The storage node generates a modified header data for the modified data block and transmits the modified data block to the compute node.
Techniques are described for creating a network-link between a virtual network in a cloud environment and a service endpoint associated with a service provided by another cloud environment. The network-link is created based on network resources and one or more link-enabling virtual networks being deployed in the first cloud environment and the second cloud environment.
In one or more embodiments, a metadata-driven user interface is implemented for presenting a set of available services, provided by a plurality of vendors, to a particular service consumer. The system queries metadata associated with of a set of candidate vendors to identify services provided by the candidate vendors that are available to a service consumer. The system generates and customizes the meta-driven interface for the service consumer based on the services available. The system receives user input selecting at least one of the services provided by a vendor. The system then queries configuration information associated with the particular service. The configuration information includes, for example, a workflow associated with the service, and identification of the service consumer's information to be used in execution of the workflow associated with the service. The system obtains the service consumer's information and uses the service consumer's information to cause execution of the workflow.
For end-to-end encryption of a virtual cloud network, a VPN tunnel from a customer device is terminated at a host network headend device using encryption keys secured in hardware and managed by the customer. The network headend device can be a card in a bare-metal server with one or more network virtualization devices. The network headend device is configured to receive a first key provisioned by a customer; receive a first data packet sent from a device of the customer; and decrypt the first data packet using the first key to obtain information. A network virtualization device is configured to receive the information from the network headend device; ascertain that the information is to be sent to a virtual machine in a virtual cloud network; ascertain that data in the virtual cloud network is configured to be encrypted; and encrypt the information with a second key to generate a second data packet before routing the second data packet to the virtual machine.
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
Disclosed herein are techniques for storing, within a database system, metadata that indicates an intended usage (IU). Once created, an IU may be assigned to a column to (a) indicate how the column is intended to be used, and (b) affect how the database server behaves when database operations involve values from the column. The IU assigned to a column supplements, but does not replace, the datatype definition for the column. Each IU may have an IU-bundle. The IU-bundle of an IU indicates how the database server behaves with respect to any column that is assigned the IU. For example, the IU-bundle may indicate constraints that the database server must validate during operations on values from columns assigned to the IU. Techniques are also described for implementing multi-column IUs and flexible IUs.
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
H04L 45/76 - Routage dans des topologies définies par logiciel, p.ex. l’acheminement entre des machines virtuelles
H04L 67/10 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau
H04L 67/1031 - Commande du fonctionnement des serveurs par un répartiteur de charge, p.ex. en ajoutant ou en supprimant de serveurs qui servent des requêtes
46.
AUTHORIZATION FRAMEWORK IN A MULTI-CLOUD INFRASTRUCTURE
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
G06F 21/41 - Authentification de l’utilisateur par une seule ouverture de session qui donne accès à plusieurs ordinateurs
H04L 12/66 - Dispositions pour la connexion entre des réseaux ayant différents types de systèmes de commutation, p.ex. passerelles
H04L 41/0806 - Réglages de configuration pour la configuration initiale ou l’approvisionnement, p.ex. prêt à l’emploi [plug-and-play]
H04L 41/0895 - Configuration de réseaux ou d’éléments virtualisés, p.ex. fonction réseau virtualisée ou des éléments du protocole OpenFlow
H04L 41/5054 - Déploiement automatique des services déclenchés par le gestionnaire de service, p.ex. la mise en œuvre du service par configuration automatique des composants réseau
H04L 41/50 - Gestion des services réseau, p.ex. en assurant une bonne réalisation du service conformément aux accords
H04L 67/10 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau
H04L 67/51 - Découverte ou gestion de ceux-ci, p.ex. protocole de localisation de service [SLP] ou services du Web
H04L 67/53 - Services réseau en utilisant des fournisseurs tiers de services
H04L 67/1031 - Commande du fonctionnement des serveurs par un répartiteur de charge, p.ex. en ajoutant ou en supprimant de serveurs qui servent des requêtes
47.
IDENTITY MANAGEMENT IN A MULTI-CLOUD INFRASTRUCTURE
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
Techniques are described for creating a network-link between a virtual network in a cloud environment and a service endpoint associated with a service provided by another cloud environment. The network-link is created based on network resources and one or more link-enabling virtual networks being deployed in the first cloud environment and the second cloud environment.
In accordance with an embodiment, described herein is a system and method for automatically enriching datasets in a data analytics environment, with system knowledge data. The system can operate, upon an analysis of a data set, to automatically enrich the data set. Users of data analytics environments, such as business users preparing data visualizations, may be unaware of additional data and system knowledge data that could be utilized to improve the data visualizations. The systems and methods described herein can provide an automatic enrichment of data from, for example, a knowledge repository, which can be delivered to a data analytics customer using various delivery means.
JSON schemas are implemented efficiently within a DBMS. Through these techniques, the power and benefit of schema-based paradigm are realized in a more cost-effective manner in terms of computer system performance. JSON schema-based techniques described herein improve execution efficiency of database statements that access JSON objects and improve software development productivity.
In accordance with an embodiment, described herein is a system and method for automatically enriching datasets in a data analytics environment, with system knowledge data. The system can operate, upon an analysis of a data set, to automatically enrich the data set. Users of data analytics environments, such as business users preparing data visualizations, may be unaware of additional data and system knowledge data that could be utilized to improve the data visualizations. The systems and methods described herein can provide an automatic enrichment of data from, for example, a knowledge repository, which can be delivered to a data analytics customer using various delivery means.
In accordance with an embodiment, described herein is a system and method for determining or finding segments in a dataset within a data analytics environment. Upon selection of a segment of interest from a dataset, the systems and methods can determine, based on scoring, one or more other segments of data that are determined to be likely of interest. The determination and finding of segments of a dataset can allow a user to explore dataset filters, which can maximize or minimize an average of a numeric column, or the frequency of certain attribute of an ordinal column. The systems and methods can utilize, for example, a combination of brute force, smart sampling, or other optimization techniques for the determination of segments.
Disclosed is an improved approach to integrate distributed applications into an XA transaction. A transaction manager library is integrated into a distributed application, where the transaction manager library provides the benefit of implementing optimizations for the XA transaction, as well as minimizing or eliminating the need to create custom software code to make the application operable with the transaction manager for the XA transaction.
A new type of table join operation, outer semi join (OSJ), is provided, which can be used by an optimizer layer and an execution layer of a database management system (DBMS). OSJ combines the semantics of both left outer-join and semi-join. The concept of an anti-join marker (AJM) is also introduced, which specifies whether a matching row was not found between joined tables for each result row in an OSJ operation. The OSJ operation supports unnesting of a class of disjunctive ANY, ALL, EXISTS, NOT EXISTS, IN, and NOT IN subqueries for execution plan optimization. The disjunction may contain filter predicates. For unnesting, OSJ avoids the need of using a distinct operator on the right table and also supports using inequality (e.g. >, >=, <, <=) in connecting or correlating conditions of subqueries, rather than being limited to equality only.
Systems, methods, and other embodiments for supporting high availability by using in-memory cache as a database are disclosed. In one embodiment, a system includes an application server that is configured to select a sub-set of data from a remote database that is predicted to be accessed by an application server, wherein the application server includes an in-memory cache. The sub-set of data is reformatted to reduce the size. The in-memory cache is configured to act as a backup database by pre-populating the reformatted sub-set of data into the in-memory cache. In response to detecting the remote database is in an off-line state: the in-memory cache is assigned as a primary database to replace the remote database and subsequent data requests are re-directed from being processed using the remote database to being processed using the in-memory cache.
G06F 12/0862 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p.ex. mémoires cache avec pré-lecture
57.
PREDICTING DOWNSTREAM SCHEDULE EFFECTS OF USER TASK ASSIGNMENTS
Techniques for managing task assignments to workers in a work environment are disclosed. A system identifies one or more workers with qualifications that match recommended qualifications to perform a task in a work environment. The system applies a trained machine learning model to task performance data associated with the worker, such as a past history of tasks performed and statistics associated with the performance of the task. The machine learning model generates a prediction of downstream effects associated with assigning the task to the user. The downstream effects include delays and performance improvements on subsequent tasks performed by the worker, as well as effects on tasks performed by other workers, at work centers in the work environment.
Described are improved systems, computer program products, and methods for a new local rolling online patching solution to a database server with minimized disruption to other instances during patching. For a one-node database server, it achieves single rolling online patching by starting a new instance of the same database from the newly patched home before shutting down the collocated instance running out of the old home.
A secure private network connectivity system (SNCS) within a cloud service provider infrastructure (CSPI) is described that provides secure private network connectivity between external resources residing in a customer's on-premise environment and the customer's resources residing in the cloud. The SNCS provides secure private bi-directional network connectivity between external resources residing in a customer's external site representation and resources and services residing in the customer's VCN in the cloud without a user (e.g., an administrator) of the enterprise having to explicitly configure the external resources, advertise routes or set up site-to-site network connectivity. The SNCS provides a high performant, scalable, and highly available site-to-site network connection for processing network traffic between a customer's on-premise environment and the CSPI by implementing a robust infrastructure of network elements and computing nodes that are used to provide the secure site to site network connectivity.
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
Techniques for configuring an enriched data metrics pipeline (DMP) include: obtaining node configuration data associated with an unenriched DMP for nodes of a heterogeneous computing platform, including (a) a first kind of node including an application programming interface (API) for obtaining unenriched data associated with the first kind of node and (b) a second kind of node including an API for obtaining unenriched data associated with the second kind of node; the unenriched DMP being configured to provide node-level unenriched data to a service according to a first schedule; the service being configured to generate node-level data metrics based on the unenriched data; based on the node configuration data, configuring an enriched DMP to provide node-level enriched data to the service according to a second schedule that is less frequent than the first schedule; the service being configured to generate fleet-level data metrics based on the node-level enriched data.
In one or more embodiments, a software service allows software providers to implement machine learning (ML) features into products offered by the software providers. Each ML feature may be referred to as an encapsulated ML application, which may be defined and maintained in a central repository, while also being provisioned for each user of the software provider on an as-needed basis. Advantageously, embodiments allow for a central definition for an ML application that encapsulates data science and processing capabilities and routines of the software provider. This central ML application delivers a ML deployment pipeline template that may be replicated multiple times as separate, tailored runtime pipeline instances on a per-user basis. Each runtime pipeline instance accounts for differences in the specific data of each user, resulting in user-specific ML models and predictions based on the same central ML application.
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
JSON schemas are implemented efficiently within a DBMS. Through these techniques, the power and benefit of schema-based paradigm are realized in a more cost-effective manner in terms of computer system performance. JSON schema-based techniques described herein improve execution efficiency of database statements that access JSON objects and improve software development productivity.
JSON schemas are implemented efficiently within a DBMS. Through these techniques, the power and benefit of schema-based paradigm are realized in a more cost-effective manner in terms of computer system performance. JSON schema-based techniques described herein improve execution efficiency of database statements that access JSON objects and improve software development productivity.
In an embodiment, a computer stores, in memory or storage, many explanation profiles, many log entries, and definitions of many features that log entries contain. Some features may contain a logic statement such as a database query, and these are specially aggregated based on similarity. Based on the entity specified by an explanation profile, statistics are materialized for some or all features. Statistics calculation may be based on scheduled batches of log entries or a stream of live log entries. At runtime, an inference that is based on a new log entry is received. Based on an entity specified in the new log entry, a particular explanation profile is dynamically selected. Based on the new log entry and statistics of features for the selected explanation profile, a local explanation of the inference is generated. In an embodiment, an explanation text template is used to generate the local explanation.
Techniques are described for creating a network-link between a first virtual network in a first cloud environment and a second virtual network in a second cloud environment. The first virtual network in the first cloud environment is created to enable a user associated with a customer tenancy in the second cloud environment to access one or more services provided in the first cloud environment. The network-link is created based on network resources and one or more link-enabling virtual networks being deployed in the first cloud environment and the second cloud environment.
JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.
A database-native Lock-Free Reservation infrastructure is used to provide automatic compensation for the reservable column updates made by successful local transactions (or microservice actions) that are part of a saga that is being aborted. The automatic compensation is achieved by tracking the reservable column updates in a reservations journal, within the database, during the execution of the local transaction and remembering them beyond the commit of the local transaction until the finalization of the saga that the transaction is a part of. If the saga aborts, then the database server automatically uses the information retained in the reservations journal to compensate for the changes made by the committed transactions that were part of the saga.
Techniques are described for backup and restore of a thin-cloned data file. The process iterates through a plurality of memory portions of the thin-cloned data file and determines whether a memory portion of the thin-cloned data file is a memory portion with common data shared with the source data file. Without storing the common data of the shared memory portion into the thin-backup data file, the process stores placeholder metadata and corresponding reference to the shared memory portion for the thin-backup data file, in an embodiment. At restore, the process may replicate the derivative data, different from the common data, from the thin-backup data file into the thin-restored data file. For the common data, the process restores a reference for the thin-restored data file to the share memory portion, in an embodiment.
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p.ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
71.
EFFICIENT COMPILATION OF BOUNDED RECURSIVE GRAPH QUERIES ON TOP OF SQL BASED RELATIONAL ENGINE
Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query that includes a bounded recursive pattern query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries that include bounded recursive patterns on top of the relational engine by avoiding any change in the existing SQL engine.
Embodiments permit scope limited access to a user's secure information using credential authentication and user information verification. Certain information sharing protocols can require an explicit grant to share a user's secure information with a requesting entity. In some scenarios such an explicit grant may be impractical, such as when the user is not available to provide such an explicit grant. Embodiments of a secure information manager can permit a vetted entity scope and time limited access to a user's secure information in such scenarios, for example when the vetted entity provides an assertion that the user is unable to provide an explicit grant. For example, in scenario(s) with exigent circumstances, the secure information manager can permit the vetted entity to access a limited scope of user information that corresponds to the vetted entity's relationship to the user, role in a workflow, or other suitable characteristics of the vetted entity.
A method includes preparing a base model using an input model pretrained on at least three languages different from each other and a base vocabulary including words corresponding to two languages among the at least three languages, where the preparing the base model includes constraining the input model to the words included in the base vocabulary; training the base model using a first enhanced training dataset generated from public data, to generate a text summarization model; training the base model using a second enhanced training dataset generated from the first enhanced training dataset, to generate a text generation model; and training the base model using a third enhanced training dataset that is generated using the second enhanced training dataset and the text summarization model, to generate a next sentence generation model.
G06F 40/58 - Utilisation de traduction automatisée, p.ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
G06F 40/166 - Traitement de texte Édition, p.ex. insertion ou suppression
G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
Embodiments permit scope limited access to a user's secure information using blockchain backed credential(s). A user can register with a secure information manager and control the scope with which the user's secure information is shared. For example, the user can permit a vetted entity access to the user's secure information via a portable access point. The user can select scope definition that control how the user's secure information is shared with the vetted entity. The vetted entity can scan the user's portable access point and request a credential. The credential can be a blockchain backed credential that is assigned access privileges that correspond the user's selections. The vetted entity can then issue data access request(s) using the credential. The secure information manager can permit the vetted entity scope limited access to the user's secure information that corresponds to the access privileges assigned to the credential.
A computer-implemented method includes obtaining, from text corpus including article-summary pairs in a plurality of languages, a plurality of article-summary pairs in a target language among the plurality of languages, to form an article-summary pairs dataset in which each article corresponds to a summary; inputting articles from the article-summary pairs to a machine learning model; generating, by the machine learning model, embeddings for sentences of the articles; extracting, by the machine learning model, keywords from the articles with a probability that varies based on lengths of the sentences, respectively; outputting, by the machine learning model, the keywords; applying a maximal marginal relevance algorithm to the extracted keywords, to select relevant keywords; and generating a keyword-text pairs dataset that includes the relevant keywords and text from the articles, the text corresponding to the relevant keywords in each of keyword-text pairs of the keyword-text pairs dataset.
Described are improved systems, computer program products, and methods for obtaining space usage information within a clustered database system. Some approaches provide an improved algorithm and structure that gives the ability to compute the latest and accurate space usage with only in-memory operations.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
A lead-sync log record is used to synchronize the replication logs of follower shards to the leader shard. In response to a failure to determine that there is a consensus for a database transaction commit operation after a shard server becomes a new leader, the new leader shard performs a sync operation using the lead-sync log record to synchronize replication logs of the follower shards to the replication log of the new leader. A shard server identifies a first transaction having a first log record but not a post-commit log record in the replication log, defines a recovery window in the replication log starting at the first log record of the identified first transaction and ending at the lead-sync log record, identifies a set of transactions to be recovered, and performs a recovery action on the set of transactions to be recovered.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p.ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
An expense report generation system receives transaction authorization data from a card issuer and compares the data with expense report generation criteria to determine whether to generate an expense report, prior to settlement of the transaction, based on the authorization data. The expense report generation system evaluates additional data obtained from other data sources including contextual information of the employee, transaction authorization, location, and other employees to generate the expense report. The expense report generation system subsequently updates the generated expense report based on updated transaction authorization data and/or transaction settlement data. The expense report generation system trains and uses a machine learning model for efficiency and accuracy in generating expense reports from transaction authorization data while reducing an employee's burden in manually inputting expense information and the approval process burden.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
Techniques for managing anomalies in a software system include monitoring the software system for anomalies via a plurality of signals and determining that signal results associated with the signals indicate a presence of an anomaly in the software system. The techniques also include identifying a hierarchy of sensors associated with the anomaly, where the hierarchy includes a parent sensor and a set of child sensors that are direct or indirect descendants of the parent sensor. The techniques additionally include executing the parent and child sensors to analyze the software system for one or more causes of the anomaly, and determining the cause(s) of the anomaly based on sensor results generated by the parent and child sensors. Finally, the techniques include causing remediation of the cause(s) of the anomaly based on one or more child sensors associated with the cause(s) and one or more resolutions mapped to the child sensor(s).
Techniques for implementing a qualification-based task management system in a work environment are disclosed. When a user logs in to a work center terminal, a system identifies a set of pending tasks that need to be completed. The system filters the tasks available to the user based on the user's qualifications and the equipment present at the work center. When the system identifies a task for which there is not a set of users with matching qualifications, the system applies a machine learning model to the task parameters to identify candidate users or recommended qualifications for performing the task.
Herein is database query acceleration from dynamic discovery of whether contents of a persistent column can be stored in an accelerated representation in storage-side memory. In an embodiment, based on data type discovery, a storage server detects that column values in a persistent column have a particular data type. Based on storage-side metadata including a frequency of access of the persistent column as an offload input column for offload computation requests on a certain range of memory addresses, the storage server autonomously decides to generate and store, in storage-side memory in the storage server, an accelerated representation of the persistent column that is based on the particular data type. The storage server receives a request to perform an offload computation for the offload input column. Based on the accelerated representation of the persistent column, execution of the offload computation is accelerated.
The present embodiments relate to data processing model recommendation and review of a portion of data using a recommended model. A model catalog executing on a cloud infrastructure (CI) system can parse data from an obtained dataset identifying aspects of the dataset. The parsed data from the dataset can be compared with a plurality of potential models stored in a domain ontology store of the model catalog to identify one or more recommended models. Review output data can be generated using the dataset and any of the recommended models. The review output data resulting from the recommended model can be provided to the client for the client to either accept or reject the model.
Techniques for managing digital messages to and from a shared mailbox are disclosed. A system receives a message directed to a shared mailbox. The system analyzes contextual data in the message to identify a set of users with access to the shared mailbox who are recipients of the message. The system performs notification operations to notify different users with access to the shared mailbox of different messages. Notification operations include sending a notification to a particular communications platform, such as email, instant message, or text, that a message in the shared mailbox is associated with the recipient, tagging the message in the shared mailbox with names of recipients associated with the message, and/or categorizing the messages in the shared mailbox according to users.
Systems, computer instructions and computer-implemented methods are disclosed for implementing space- and time-efficient enumerations. An instance of an enumeration class may be created with a constant, plurality of enumerations. A plurality of objects corresponding to the respective enumerations may be stored in memory along with a lookup table indexed by respective ordinal values of the plurality of enumerations, the lookup table including respective references to the stored objects of the instantiated enumeration class. A reference to an enumeration may be stored in a memory location by storing an ordinal value of the enumeration. A determination may then be made to convert a stored ordinal value to a reference to an object, and responsive to the determination, the ordinal value may be loaded and used as an index into the lookup table to obtain the reference to the object corresponding to the enumeration.
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
85.
TECHNIQUES FOR EFFICIENT COMPUTE RESOURCE HARVESTING
The present disclosure relates to a system and techniques for resolving dangling references resulting from a dependency relationship between computing resource objects uncovered during a harvesting process. In embodiments, a harvester application adds computing resource objects associated with a client to a resource collection as those computing resource objects are identified. Dependencies are identified as each computing resource object is added to the resource collection, which are resolved only if the computing resource objects associated with those dependencies have already been added to the resource collection. If the computing resource objects associated with the dependencies have not already been added to the resource collection, then the dependency is added to an observer pool. Observer modules are configured to check each computing resource object as it is processed during the harvest process in order to match those computing resource objects to unresolved dependencies.
LOB cache swapping is reduced by multi-LOB writing. Under multi-LOB writing, information that tracks changes made by a database transaction to multiple LOBs is retained in a LOB staging buffer after one or more LOB switches. Thus, at commit time, changes to multiple LOBs in a column may be staged in the LOB staging cache, thereby enabling the changes to be made in contiguous data blocks for all the multiple LOBs. Storing LOBs in the same column in this way improves clustering, thereby improving database performance. In addition, LOBs inserted or updated in the same database transaction are often related and accessed together within database transactions and/or query accesses. Clustering the LOB content of such related LOBs allows the LOBs to be accessed more efficiently.
Techniques are described for preserving the inflight sessions failing over from a primary database to the replicated logical database of the primary database. In an implementation, prior to failover, when the primary database server receives a commit for a transaction, the process stores a commit indication that the transaction has been committed by performing a corresponding SQL command. The commit indication is replicated to the logical replica database by virtue of the replication of the SQL command and its execution on the logical replica database. Accordingly, the standby database server in the failover session may successfully request for the outcome of the transaction. Techniques are also described for the client-side LOB references to be preserved when failing over to the logical replica database, for AS OF queries preserved, and for versioning of checksums, signatures and structures across logical replicas.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
G06F 11/20 - Détection ou correction d'erreur dans une donnée par redondance dans le matériel en utilisant un masquage actif du défaut, p.ex. en déconnectant les éléments défaillants ou en insérant des éléments de rechange
Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.
H04L 41/0895 - Configuration de réseaux ou d’éléments virtualisés, p.ex. fonction réseau virtualisée ou des éléments du protocole OpenFlow
H04L 41/0806 - Réglages de configuration pour la configuration initiale ou l’approvisionnement, p.ex. prêt à l’emploi [plug-and-play]
H04L 41/50 - Gestion des services réseau, p.ex. en assurant une bonne réalisation du service conformément aux accords
H04L 41/5054 - Déploiement automatique des services déclenchés par le gestionnaire de service, p.ex. la mise en œuvre du service par configuration automatique des composants réseau
H04L 45/76 - Routage dans des topologies définies par logiciel, p.ex. l’acheminement entre des machines virtuelles
H04L 45/80 - Sélection des points d'entrée par le point de terminaison source, p.ex. sélection du ISP ou du POP
Techniques are described for creating a network-link between a first virtual network in a first cloud environment and a second virtual network in a second cloud environment. The first virtual network in the first cloud environment is created to enable a user associated with a customer tenancy in the second cloud environment to access one or more services provided in the first cloud environment. The network-link is created based on network resources and one or more link-enabling virtual networks being deployed in the first cloud environment and the second cloud environment.
A computer measures for each column in many rows, a respective frequency of statements that filter the column in a workload of database statements, a respective count of distinct values (CDV) used for filtration on the column in each statement individually, a respective frequency of each of the CDVs used for filtration on the column across all of the database statements, and a respective value range of the column for each of many storage zones. A respective efficiency is measured for each of many distinct interleaved sorts (ILs). Each IL uses a respective distinct subset of the columns. Each IL is based on portions of each of the values for each row in a sampled subset of rows in each column of the subset of the columns of the IL. Efficiency measurement is based on frequencies of statements, value ranges of columns for each storage zone, and frequencies of CDVs.
Disclosed herein are techniques for storing, within a database system, metadata that indicates an intended usage (IU). Once created, an IU may be assigned to a column to (a) indicate how the column is intended to be used, and (b) affect how the database server behaves when database operations involve values from the column. The IU assigned to a column supplements, but does not replace, the datatype definition for the column. Each IU may have an IU-bundle. The IU-bundle of an IU indicates how the database server behaves with respect to any column that is assigned the IU. For example, the IU-bundle may indicate constraints that the database server must validate during operations on values from columns assigned to the IU. Techniques are also described for implementing multi-column IUs and flexible IUs.
Techniques for managing digital messages to and from a proxy message address are disclosed. A system receives a message directed to a particular destination address. The system replaces any source address included in the message with a proxy address. When the system receives a reply to the message, the reply is directed to the proxy address. The system analyzes message data to identify a target address for the reply message. The system identifies contextual data associated with the reply message. The system transmits the reply message, and the contextual data, to the target address.
Techniques for managing task assignments to workers in a work environment are disclosed. A system identifies one or more workers with qualifications that match recommended qualifications to perform a task in a work environment. The system applies a trained machine learning model to task performance data associated with the worker, such as a past history of tasks performed and statistics associated with the performance of the task. The machine learning model generates a prediction of downstream effects associated with assigning the task to the user. The downstream effects include delays and performance improvements on subsequent tasks performed by the worker, as well as effects on tasks performed by other workers, at work centers in the work environment.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
94.
CONSENSUS PROTOCOL FOR ASYNCHRONOUS DATABASE TRANSACTION REPLICATION WITH FAST, AUTOMATIC FAILOVER, ZERO DATA LOSS, STRONG CONSISTENCY, FULL SQL SUPPORT AND HORIZONTAL SCALABILITY
A consensus protocol-based replication approach is provided. For each change operation performed by a leader server on a copy of the database, the leader server creates a replication log record and returns a result to the client. The leader does not wait for consensus for the change operation from the followers. For a commit, the leader creates a commit log record and waits for consensus. Thus, the leader executes database transactions asynchronously, performs replication of change operations asynchronously, and performs replication of transaction commits synchronously.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
95.
CONFIGURATION AND MANAGEMENT OF REPLICATION UNITS FOR ASYNCHRONOUS DATABASE TRANSACTION REPLICATION
A consensus protocol-based replication approach is provided. Chunks are grouped into replication units (RUs) to optimize replication efficiency. Chunks may be assigned to RUs based on load and replication throughput. Splitting and merging RUs do not interrupt concurrent user workload or require routing changes. Transactions spanning chunks within an RU do not require distributed transaction processing. Each replication unit has a replication factor (RF), which refers to the number of copies/replicas of the replication unit, and an associated distribution factor (DF), which refers to the number of servers taking over the workload from a failed leader server. RUs may be placed in rings of servers, where the number of servers in a ring is equal to the replication factor, and quiescing the workload can be restricted to a ring of servers instead of the entire database.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
96.
NATIVELY SUPPORTING JSON DUALITY VIEW IN A DATABASE MANAGEMENT SYSTEM
JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.
JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.
JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.
Techniques are provided for block-level fail atomicity on byte-level non-volatile media. In one technique, an offset table and application data that stores content of a file are stored for a file. The offset table includes multiple entries, each entry being associated with a different offset value and storing a logical block address (LBA) that references a location in the application data. In response to receiving a request, that includes an input buffer and an offset value, to update the file: (a) an entry, in the offset table, that corresponds to the offset value and comprises a first LBA is identified; (b) a second LBA that is considered free is identified; (c) the second LBA is replaced with the first LBA; (d) the input buffer is written to a location, in the application data, that the second LBA references; and (e) the second LBA is added in the entry.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Cloud computing featuring computer software in the nature of software compilers and virtual machines for use in providing peak performance, interoperability, embeddability, tooling, and multi-language support to other software applications, and for running executable program code; providing temporary use of non-downloadable cloud-based computer software for providing peak performance, interoperability, embeddability, tooling, and multi-language support to other software applications, and for running executable program code