Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

Abstract: Embodiments of the present disclosure relate generally to determining node embedding using multi-view graphs for analyzing electronic content.

Abstract: A system and related method are provided for dynamically modifying a rule-based matching system. A processor receives a source data entity, and then locates a matching data entity by a search based on the source data entity and a rule set. A rater assessment is provided by a rater that utilizes at least one factor that is independent of the rule set and comprises a degree of matching between the source and matching data entity. A revised rule set is dynamically created based on an output of the analyzer, which in turn is based on the source data entity, the matching data entity, the rater assessment, and the rule set. Once this is complete, a second matching data entity is located by searching for the second matching data entity based on the source data entity and the revised rule set.

Abstract: A method is provided for converting a single database query into multiple database queries for multiple databases and aggregating the results of each of the queries. The method includes receiving, from a calling device, an input database query including a selection set defining an aggregation key, at least one first field to be queried in a first database, and at least one second field to be queried in a second database, generating a first database query representing a first request to retrieve the at least one first field from the first database, generating a second database query representing a second request to retrieve the at least one second field from the second database, aggregating the first response and a second response to the second database query from the first database to produce an aggregated response, and providing the aggregated response to the calling device.

Abstract: A distributed storage system may store data object instances in persistent storage and may store keymap information for those data object instances in a distributed hash table on multiple computing nodes. Each data object instance may include a composite key containing a user key. The keymap information for each data object instance may map the user key to a locator and the locator to the data object instance. A request to store or retrieve keymap information for a data object instance may be routed to a particular computing node based on a consistent hashing scheme in which a hash function is applied to a portion of the composite key of the data object instance. Thus, related entries may be clustered on the same computing nodes. The portion of the key to which the hash function is applied may include a pre-determined number of bits or be identified using a delimiter.

Abstract: A model training method and an apparatus thereof are provided. The method includes reading a portion of sample data in a sample full set to form a sample subset; mapping a model parameter related to the portion of sample data from a first feature component for the sample full set to a second feature component for the sample subset; and training a model based on the portion of sample data having the second feature component. A size of a copy of model parameters(s) on a sample computer can be reduced after mapping, thus greatly reducing an amount of training data and minimizing the occupancy of memory of the computer. Memory of a sample computer is used to place vectors, and store and load samples, thereby performing machine learning and training large-scale models with relatively low resource overhead under a condition of minimizing the loss of efficiency.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for enhanced preparation and integration of data sets. In some implementations, data indicating user input that identifies a first data set that includes streaming data and a second data set that includes non-streaming data is received. The first data set and the second data set are integrated to generate a hybrid data set. The data processing system provides access to the hybrid data set through a (i) non-streaming access channel that provides a periodically-refreshed summary of both the streaming data and the non-streaming data and (ii) a streaming access channel that provides a data stream based on combined data of the first data set and the second data set. One or more application programing interfaces are provided. The one or more application programming interfaces allow at least one client device to access the non-streaming access channel and the streaming access channel.

Abstract: A computer-implemented method dynamically detects and corrects an error in a query. The method includes identifying an error in a first query. The method further includes generating a set of alternate execution structures for the first query. The method includes running each of the alternate execution structures, including generating a set of results corresponding to each set of alternate execution structure, comparing each of the set of results against each other of the set of results, and storing each of the set of alternate execution structures to include a result of the set of results, for each alternate structure. The method further includes selecting, from the set of alternate execution structures, a first alternate execution structure based on a predetermined criteria, and implementing the first alternate structure in place of the first query.

Abstract: Aspects of the subject disclosure may include, for example, receiving input data via a transformation UI, generating transformation configuration data, causing the transformation UI to present transformation object data per the transformation configuration data, where the transformation object data identifies data objects each including an input and output field name and a data type, detecting, from the transformation UI, an instruction defining a mapping for the input data, including a modification to the output field name of a data object such that the input field name of the data object is mapped to the modified output field name, based on the detecting the instruction, modifying the first transformation configuration data per the mapping to derive second transformation configuration data, performing a transformation of the input data based on the second transformation configuration data, and causing the transformation UI to present a transformation output. Other embodiments are disclosed.

Abstract: Provided is a method, computer program product, and system for automatically predicting unknown semantic data types in a rectangular dataset using a holistic knowledge of said dataset. A processor may receive one or more rectangular datasets, the one or more rectangular datasets comprising a plurality of columns having a set of known semantic data types. The processor may extract a set of features from the plurality of columns, where the set of features is used to determine a relationship among each column of the plurality of columns. The processor may construct a set of training data based on the extracted set of features. Using the training data, the processor may train a machine learning model to predict a semantic data type of a target column in a rectangular dataset having an unknown semantic data type.

Abstract: Disclosed herein are systems and methods for using machine learning for managing application incidents. An embodiment takes the form of a method that includes receiving extracted data pertaining to one or more applications, Model-input data is generated from the extracted data. Model-output data is generated at least in part by processing the generated model-input data with one or more machine-learning models trained to make one or more application-incident predictions. Based at least in part on the model-output data, an application-incident-likely determination is made that a likelihood of an occurrence of an application incident exceeds an application-incident-likelihood threshold, where the application incident corresponds to a given application of the one or more applications. Responsive to making the application-incident-likely determination, one or more alerts of the likelihood of the occurrence of the application incident are output.

Abstract: Techniques are described with respect to processing a federated query. An associated computer-implemented method includes compiling a query received from a client computing system to generate a query statement and a query access plan. The query access plan incorporates a modified database access application programming interface (API) that supports data serialization. The method further includes executing the query access plan to transmit the query statement to a remote database system. The method further includes fetching a query result set from the remote database system including serialized binary large object (BLOB) data. The method further includes deserializing the serialized BLOB data of the query result set and populating an in-memory data structure with deserialized query results. In an embodiment, the method further includes transmitting the deserialized query results to the client computing system.

Abstract: Techniques are described relating to automatic data standardization in a managed services domain of a cloud computing environment. An associated computer-implemented method includes receiving a dataset during a data onboarding procedure and classifying datapoints within the dataset. The method further includes applying a machine learning data standardization model to each classified datapoint within the dataset and deriving a proposed set of data standardization rules for the dataset based upon any standardization modification determined consequent to model application. Optionally, the method includes presenting the proposed set of data standardization rules for client review and, responsive to acceptance of the proposed set of data standardization rules, applying the proposed set of data standardization rules to the dataset. The method further includes, responsive to acceptance of the proposed set of data standardization rules, updating the machine learning data standardization model accordingly.

Abstract: An information processing apparatus according to an embodiment includes an aligner that aligns, with reference to a reference data set that is a sequential data set, another sequential data set; and a target data extractor that extracts a portion of the another sequential data set corresponding to the reference data set as a target data set.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for scalable architectures for reference signature matching and updating. An example method for scalable architectures for reference signature matching and updating includes accessing site signatures to be compared to reference signatures from a first group of media sources. The example method also include determining if a first reference node is an owner of a first one of the site signatures, comparing a neighborhood of site signatures including the first site signature to reference signatures in a first subset of reference signatures when the first reference node is the owner of the first site signature, the first subset of references signatures stored in a first memory partition associated with the first reference node, and not comparing site signature to reference signatures when the first reference node is not the owner of the first one of the site signatures.

Abstract: In accordance with an embodiment, described herein is a system and method for generation of starter websites. A content management system (CMS), such as for example, an Oracle Content and Experience Cloud (CEC) environment, can be provided. The content management system can include a site generator within the CMS. The site generator can receive a selection of content managed by the CMS, wherein the content includes data associated with the content. The site generator can determine, based on the data associated with the content, a layout of the content on a document. The site generator can generate machine readable code that, when interpreted or executed by a machine, causes the machine to produce a document including the content wherein the content is displayed in the determined layout.

Abstract: A system may include memory containing: (i) a master data set representable in columns and rows, and (ii) a query expression. The system may include a software application configured to apply a machine learning (ML) pipeline to an input data set. The system may include a computing device configured to: obtain the master data set and the query expression; apply the query expression to the master data set to generate a test data set, where applying the query expression comprises, based on content of the query expression, generating the test data set to have one or more columns or one or more rows fewer than the master data set; apply the ML pipeline to the test data set, where applying the ML pipeline results in either generation of a test ML model from the test data set or indication of an error in the test data set; and delete the test data set from the memory.

Abstract: A method comprising: receiving a plurality of pairs of queries associated with a database, wherein the queries in each pair in the plurality of pairs of queries have an identical FROM clause; at a training stage, training a machine learning model on a training set comprising: (i) the plurality of pairs of queries, and (ii) labels associated with containment rates between each of the pairs of queries over the database; and at an inference stage, applying the trained machine learning model to a pair of target queries, to estimate containment rates between the target pair of queries over the database.

Abstract: Techniques are described herein for determining a predicted intent of a user and displaying additional content selected based on the predicted intent of the user. A method includes: receiving information identifying a webpage that a user is visiting and a navigational path of the user in navigating to the webpage; determining a predicted intent of the user based on the information identifying the webpage that the user is visiting and the navigational path of the user in navigating to the webpage; selecting additional content based upon the predicted intent of the user; and displaying an overlay, on a portion of the webpage, that includes the additional content.

Abstract: In some implementations, a method includes logging, by a user device, mapping data that maps domain names of Internet resources presented on the user device to Internet Protocol (IP) addresses of the Internet resources, determining, by the user device, that one or more criteria are satisfied for transmitting the mapping data to a passive Domain Name Service (DNS) system, and in response to determining that the one or more criteria are satisfied, transmitting, by the user device, the mapping data to the passive DNS system.