Abstract: A method may include receiving a query including at least one unnest operation to unnest a plurality of elements from one or more JavaScript Object Notation (JSON) arrays. The at least one unnest operation may unnest the plurality of elements by generating a table in which each row of the table is populated by one of the plurality of elements. An execution plan may be generated to include a pre-filter operation to filter, prior to the at least one unnest operation, the plurality of elements included in the one or more JSON arrays. For example, the pre-filter operation may be performed by iterating through the elements from the one or more JSON arrays to identify one or more elements satisfying a predicate included in a where clause of the query. The query may be executed in accordance with the execution plan. Related systems and computer program products are also provided.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a content stack utilizing one or more machine-learning models. In some implementations, the disclosed systems generate and provide, to a user account, a content stack that includes content items corresponding to a topic prompt for the user account. For instance, in some implementations, the disclosed systems utilize content-based signals and account-based signals to generate an account-specific stack formulation graph that represents a plurality of content items and relationships of the content items with each other and with the user account. Additionally, in some implementations, the disclosed systems analyze the account-specific stack formulation graph to generate a content stack from the plurality of content items, the content stack comprising a set of content items corresponding to the topic prompt.

Abstract: An apparatus includes a processing device configured to identify and determine prioritizations of file systems to be mounted on a destination storage node, the prioritization for a given file system being based at least in part on a static priority assigned to the given file system, a dynamic priority value calculated for the given file system, and a predicted input-output load for the given file system. The at least one processing device is also configured to determine a system performance of the destination storage node, to generate a schedule for execution of mount operations for the file systems on the destination storage node based at least in part on (i) the determined prioritizations of the file systems and (ii) the determined system performance of the destination storage node, and to execute the mount operations for the file systems on the destination storage node in accordance with the generated schedule.

Abstract: Systems and methods are provided for dynamic format conversion of shared files. Computing devices can use different formats to store the same or substantially similar information. This difference in format can cause incompatibilities between such devices when these devices attempt to share files. The present disclosure can address this problem by providing for dynamic format conversion of shared files. Different computing devices may share access to a file directory storing base content, with each device being provided with a different view of the file directory such that base content in the directory appears in a format supported by the respective device. Further, input/output operations to files in the directory can be converted between respective formats, such that each device manipulates base content in the directory in a supported format.

Abstract: Systems, methods, and other embodiments associated with auditing the results of a machine learning model are described. In one embodiment, a method accesses original time series data and machine learning estimates of the original time series data. The method generates reconstituted time series data from the machine learning estimates by reversing operations of a machine learning model trained for generating the machine learning estimates from the original time series data. The method detects tampering (or corruption) in the original time series data based on a difference between the original time series data and reconstituted time series data. And, the method generates an electronic verification report that indicates whether the tampering (or corruption) is detected in the original time series data.

Abstract: Selecting and ordering the execution of data quality rules includes generating a snapshot of a table-formatted dataset. The snapshot comprises a reduced number of rows of the dataset such that each column variation of the dataset is included in the snapshot. A predetermined collection of data quality (DQ) rules is executed on the snapshot. One or more performance statistics is determined for each of the DQ rules. The performance statistics indicate a likelihood that a DQ rule determines a data quality deficiency. Based on the performance statistics, a subset of the DQ rules is generated. Each DQ rule of the subset is selected based on the likelihood that the DQ rule selected detects a quality deficiency. An ordered subset of selected DQ rules is generated by ordering the application of each of the subset of DQ rules selected. The ordering specifies a sequence for executing each selected DQ rule.

Abstract: A client computer includes a synchronization manager. The synchronization manager communicates with a remote content management system to determine a set of objects in a content navigation hierarchy. The synchronization manager provides a user interface with controls to allow a user to independently select multiple objects in the content navigation hierarchy for synchronization and receives an input, via the interface, that selects objects from the set of objects for synchronization. The synchronization manager stores local peers of the selected objects in a local cache in a local file system. The synchronization manager synchronizes changes to the selected objects at the content management system to the local peers in the file system.

Abstract: Dynamic shard allocation in a near real-time search platform is described. A computing system may include a cluster comprising a plurality of nodes, each node of the plurality of nodes comprising a plurality of shards storing searchable data. A query load distribution of the cluster is assessed in real-time. A first shard of a first node and a second shard of a second node are identified based on the query load distribution. The first shard of the first node is commanded to exchange with the second shard of the second node.

Abstract: An access service is positioned between a set of container groups and a SQL database service. Upon receipt, at the access service, of a REST request from any container group indicating that a SQL transaction should be started, an entry is created in a staging database, separate from the SQL database service, to track the SQL transaction. Multiple connections are allowed to be established from the container groups to the access service over which other REST requests from any container group are sent. The other REST requests include details of SQL operations to be performed as part of the SQL transaction. The details are cached in the staging database. Upon receiving a REST request from any container group indicating that the SQL transaction should be committed, a single connection is established between the access service and SQL database service to commit the transaction, including the details, as an atomic transaction.

Abstract: Methods, systems and programming for providing query suggestions based on user feedback. In one example, a prefix of a query is first received. An input including a prefix of a query is received from a user in a search session. A plurality of query suggestions are fetched based on the prefix of the query. Rankings of the plurality of query suggestions are determined based, at least in part, on the user's previous interactions in the search session with respect to at least one of the plurality of query suggestions. The at least one of the plurality of query suggestions has been previously provided to the user in the search session. The plurality of query suggestions are provided in the search session based on their rankings as a response to the input.

Abstract: Disclosed are methods and system for predicting data-source influences on one or more data manifestations of a named entity. The method includes receiving an inheritance dataset of the named entity. The method determines first and second portions of the inheritance dataset of the named entity. The method determines an aggregated data-bit association score for the named entity based on the inheritance dataset at an identified subset of the data-bit regions. The method determines aggregated data-bit association scores associated with the first and second data source based on the first and second portions of the inheritance dataset at the identified subset of the data-bit regions. The method selects one of the first and second data sources as having a measure of influence on a data manifestation of the named entity corresponding to the identified subset of the data-bit regions.

Abstract: A method is disclosed. The method includes a relationship server receiving a discovery service request from a client computer, the relationship server comparing metadata provided in the discovery service request against a database and a crawler retrieving updated metadata from a commerce server for if the metadata provided in the discovery service request does not match the metadata stored in the database column.

Abstract: A large language model predicts a topic summarization of a long document given a set of segments from the long document that pertain to a topic of interest. The set of segments from the long document are selected by searching for similar segments from other documents that have been labeled to indicate whether or not the segment pertains to a topic of interest. The search is based on an embedding of a segment from the long document closely matching embeddings of the labeled segments. Each segment of the long document is scored based on the labels of the closest-matching similar segments. The segments from the long document are ranked by their respective score and the highest-scored segments are included in a prompt to the large language model for the model to generate a topic summarization of the long document.

Abstract: Systems and methods publishing content are disclosed herein. A content item for publication on a multimedia platform is received, and a first plurality of users within a first radius are detected. The content item is published to the first plurality of users and first data of the interaction of the first plurality of users with the content item is collected. Thereafter, an engagement score based on the first data is calculated. In response to the engagement score being above a first threshold, a second plurality of users within a second radius is detected, wherein the second radius is larger than the first radius and publishing the content item to the second plurality of users.

Abstract: Solutions for enforcing both syntactic and semantic correctness of domain-specific data queries include: receiving a data query; constraining an operation of the data query to enforce semantic correctness, wherein enforcing semantic correctness comprises: determining semantic information associated with each of a plurality of data entities; based on at least the semantic information, determining whether combining the two data entities of the plurality of data entities is allowed or is not allowed; based on at least determining that combining the two data entities is allowed, permitting combining the two data entities in the data query; and based on at least determining that combining the two data entities is not allowed, preventing combining the two data entities in the data query; and executing the data query. Some examples further include basing the permission on (at least) an ontology between the semantic information of the two data entities and a set of axioms.

Abstract: A system and method for grouping data files into related groups using a combination of deterministic matching and machine learning based cosine similarity matching. The method includes loading the data files into hive tables, combining files in the hive tables into common structured files, and providing prior stored base data hive tables. The method also includes performing a deterministic matching process that matches the common structured files with each other and with the prior stored matched groups of files into related groups, and performing a machine learning based cosine similarity matching process that matches the common structured files with each other and with the prior stored matched groups of files into related groups, where the machine learning based cosine similarity matching process uses at least one neural network. The method combines the matched files from the deterministic matching process and the machine learning based cosine similarity matching process.

Abstract: The present disclosure generally relates to a method for generating a signal test specification, a data processing circuit, and a cloud system. The signal test specification is to be applied by a measurement device for testing a device under test. At least one first voltage requirement and at least one first timing requirement for a first power rail signal of the device under test to be tested are stored in a structured file format. At least one second voltage requirement and at least one second timing requirement for a second power rail signal of the device under test to be tested are stored in a structured file format. At least one relative constraint having a relation between at least two of voltage requirements and timing requirements is stored in a structured file format.

Abstract: The disclosure generally relates to an enterprise group-based communication system. Group-based communication channels may be created and access by client devices associated with users within the enterprise group-based communication system that have various identifiers associated therewith.

Abstract: Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.

Abstract: A computer-implemented method, computer program product and computing system for providing a plurality of data description models corresponding to a plurality of websites to a machine learning process; providing ontology data concerning the plurality of websites to the machine learning process; providing target website data concerning a target website to the machine learning process; and processing the plurality of data description models, ontology data and target website data using the machine learning process to generate a data description model for the target web site.