Abstract: The present disclosure provides techniques and solutions for comparing source data with data extracted from a source. A user provides an identifier of a web page and an identifier of a file having data exported from the web page. Data for a table of a web application is extracted from web application code by analyzing the web application code for a table identifier token. The extracted data is compared with data exported from the web application to the file. Differences between the extracted data and the data exported from the web application to the file are determined. The differences are presented to a user on a user interface display.

Abstract: A data platform includes an artificial intelligence integration component (AIIC) to facilitate user interaction with the data platform, such as creation of a data pipeline to implement a desired use case. The AIIC manages interactions between the user and a chatbot which includes an artificial intelligence (AI) model. The AIIC also manages interactions between the user and technical internal components of the data platform such as a connectivity framework for establishing connections with source and target systems external to the data platform. The AI model is trained with language data and data regarding the data platform, such that the chatbot can participate in a dialog with the user of the data platform to formulate a problem statement associated with a desired use case. The AIIC connects with the technical internal components of the data platform to manage generation of a data pipeline based on the problem statement.

Abstract: Provided is a system and method for identifying and recommending the best hierarchical levels for training a predictive model such as a time-series forecasting model. In one example, the method may include receiving an identification of a measure of multidimensional data, generating a plurality of training data sets that comprise different combinations of hierarchical dimension granularities of aggregation, training a plurality of instances of a machine learning model based on the plurality of training data sets, respectively, and determining and outputting predictive accuracy values of the plurality of instances of the trained machine learning model.

Abstract: A machine-learning model is trained using a training set that comprises labeled examples. Each example is a source code repository for an application and each label is a digital assistant for the application. The trained machine-learning model receives a source code repository as input and generates a digital assistant as output. The machine-learning model may be trained using the transformer architecture that is based on attention mechanisms without using recurrence and convolution. Prior to use in training, a collection of source code repositories may be filtered to remove repositories or files that use unknown or unsupported programming languages, that are labeled with incorrect or invalid digital assistant definitions, that include too many or too few files, or that include too many or too few lines of code. A separate machine-learning model may be trained for each programming language, for each digital assistant definition framework, or for each language/framework pair.

Abstract: Provided are systems and methods for automated language testing for use in continuous integration and continuous delivery software development. In one example, the method may include identifying language tests in a plurality of different natural languages to be performed for a software application, executing the language tests and validating the language tests based on results of executed scripts, automatically assigning the language tests among a plurality of language testers based on execution of a machine learning model which pairs together a language test with a language tester based on test attributes and tester attributes, and transmitting notifications to computing devices of the plurality of language testers with information about the automatically assigned language tests.

Abstract: Embodiments relate to metadata-based generic parsing of an incoming data object to transform it for different purposes, such as conversion into a format more suitable for a specific storage type. The received data object comprises members organized according to a structure, each member containing data. The object's structure and the member's type are defined by the associated metadata. The metadata is referenced by parsing logic to generate a schema representing the structure of the data object in a different format (such as an ER table schema). The schema is then stored. When data is received, the metadata is referenced to parse the corresponding object's data accordingly and for ingestion into the generated schema and storage.

Abstract: Some embodiments provide a system and method to receive, as an input, configuration properties of a group of operators of a data pipeline, the data pipeline including a specified multiplicity greater than one (1); generate, as an output, a configuration for two new operators, including a first new operator and a second new operator; and automatically insert the first new operator and the second new operator into a deployment of the data pipeline, the first new operator being inserted before a number of replicas of the group of operators of the data pipeline corresponding to the specified multiplicity and the second new operator being inserted after the number of replicas of the group of operators of the data pipeline corresponding to the specified multiplicity.

Abstract: A method may include receiving, from a first thread of a first operator performing a first operation of a query, a first request for memory allocation or memory deallocation. In response to the first request, a first local data structure of the first thread may be accessed to identify the first operator associated with the first request. A first memory usage metric for the first thread of the first operator may be updated based on the first request. A third memory usage metric for the first operator may be determined based on the first memory usage metric and a second memory usage metric for a second thread of the first operator. A fourth memory usage metric for a second operator may be determined based on a fifth memory usage metric for a third thread used by the second operator to perform a second operation of the query.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a cross-process scheduling engine executed within a cloud platform, work scenario metadata and sensitivity metadata, generating, by the cross-process scheduling engine, a lineage graph representing relationships between one or more of data and processes to be executed for a work scenario represented by the work scenario metadata, defining, by the cross-process scheduling engine, a set of tile segments for the work scenario based on the lineage graph and the sensitivity metadata, dispatching, by the cross-process scheduling engine, a first sub-set of tile segments for execution in a private deployment including one or more private nodes, and dispatching, by the cross-process scheduling engine, a second sub-set of tile segments for execution in a public deployment including one or more public nodes.

Abstract: Implementations can include providing a snapshot table, replacing, in a first access schema, a first projection view with a union view, creating, in a second access schema, a snapshot projection view, a second projection view, and a set of database triggers, and during execution of an upgrade procedure that results in changes to records of the data table: recording the changes to the records of the data table in the snapshot table, for each record of the data table changed, providing the record in the snapshot table in an unchanged state through at least one database trigger and the snapshot projection view, and returning a set of records through the union view in response to a query, the set of records including a first sub-set of records of the data table and a second sub-set of records of the snapshot table.

Abstract: Methods, systems, and computer-readable storage media for network subscription management. An example method includes receiving a request, from a collaboration network, for sub-network subscription offerings that are available for a user of the collaboration network. The user is identified in the collaboration network using a collaboration network identifier and is currently subscribed to a first subscription plan offering of a first sub-network. Compatible sub-network subscription offerings that are available for the user of the collaboration network that are compatible with the first subscription plan offering are determined and provided to the collaboration network. An indication of a selected second subscription plan offering for a second sub-network is received. Subscription service interface information for connecting to the second sub-network is determined and a connection is established to the second sub-network using the subscription service interface information.

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

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives an image of a document, the document comprising a set of text. The program further provides the set of text to a machine learning model configured to determine, based on the set of text, a plurality of probabilities for a plurality of defined types of documents. Based on the plurality of probabilities for the plurality of defined types of documents, the program also determines a type of the document from the plurality of defined types of documents.

Abstract: A computer-implemented method can receive a message sent from a source entity, perform first pre-processing operations for verifying validity of the message, perform second pre-processing operations for determining a category of the message, extract metadata from the message, generate an enriched message comprising the metadata and the determined category, perform post-processing operations for classifying the enriched message into one of a plurality of event types, broadcast the enriched message to a message broker, and routing, by the message broker, the enriched message to one or more target entities registered an event type into which the message is classified.

Abstract: Technologies are described for correcting data, such as master data, in an unsupervised manner using supervised machine learning. Correction of master data can involve receiving a table containing unlabeled master data. Machine learning models are applied to the fields of one or more columns of the table to predict values of the fields, and the machine learning models use unsupervised learning. For example, a machine learning model can be applied to a particular field of a particular column to predict the value of the particular field. The machine learning model uses the fields of other columns as features. Results of applying the machine learning models include indications of recommended values, indications of probabilities of the recommended values, and indications of which original values do not match their respective recommended values. The results can be used to perform manual and/or automatic correction of the master data.

Abstract: Embodiments of the present disclosure include techniques for predictive memory maintenance. In one embodiment, error locations in a RAM are specified by columns and rows. Error locations are detected and stored in a storage system. One or more plots of the error locations may be presented to a user. In some embodiments, the error locations are time stamped. Rules may be defined to automatically detect patterns of error locations statically or over time. Alerts may be generated automatically to perform maintenance of a computer system with failing memory.

Abstract: Technologies are provided for iterative machine learning and relearning. A training dataset can be divided into a plurality of training data blocks which can be used to generate separate machine learning models. The accuracy of the machine learning models can be assessed using a test dataset. Training data blocks which result in models with good accuracy can be combined into larger training data blocks which can then be used to generate new machine learning models. The process of combining training data blocks can be repeated as long as the resulting machine learning model has acceptable accuracy. However, if a model for a combined training data block has a poorer accuracy than the machine learning models for its component training data blocks, then the combined training data block and its machine learning model can be forgotten and its component training data blocks (and their associated machine learning models) can be relearned.

Abstract: A database includes a blockchain infrastructure that allows data tables comprising the database to be associated with respective blockchains. Data inserted into a data table is replicated in its associated blockchain. Certain operations on a data table are disabled when it is associated with a blockchain.

Abstract: A computer-implemented method includes translating into a routing configuration, tenant-specific preferences for primary and secondary datacenter locations. A service mesh is set up for communication between services within and across the primary and secondary datacenter locations. Service persistencies with endpoints in datacenter locations are used to configure replication agents between the service persistencies. Using service endpoints, configuring Virtual Services that implement the service mesh. An Ingress Gateway is configured to route end user requests into the service mesh to a first service instance in the tenant-selected primary datacenter. According to the tenant-specific preferences, data replication is configured to copy data to redundant storage. Using endpoints of persistent storage replication agents for each service persistence in the tenant-selected primary datacenter, configuring persistent storage replication agents for each service persistence in the tenant-selected primary datacenter.

Abstract: Systems and methods include acquisition of a first image of a first activity record, determination of first text based on the first image, generation of a first embedding based on the first text, generation of a second embedding based on the first embedding using a first model, where a number of dimensions of the second embedding is less than a number of dimensions of the first embedding, determination of a first cluster based on the second embedding using a second trained model, the second trained model trained using unsupervised learning, and determination of a first activity type associated with the first activity record based on the first cluster, the second embedding and historical activity data associating the first cluster with a plurality of activity types and each of the plurality of activity types with a respective embedding metric.