Abstract: Systems and techniques are provided for query tree labeling and processing. A query may be received. A query tree may be generated from the query and identifiers may be generated for nodes of the query tree and stored in the nodes. Labels may be stored in nodes determined to meet at least one criteria for being labeled nodes to generate a labeled query tree. The nodes of the labeled query tree may be processed by receiving results from an entry in a results data structure and returning the results from the entry as the results for the node if the identifier of the nodes matches the identifier of an entry in the results data structure. If there is no match, results may be computed for the node. The results generated by computing the node may be stored in the results data structure if the node has a label.

Abstract: Provided are methods, systems, and apparatuses for improving supply chain management interfaces and functionality. The supply chain management interfaces may provide visualizations of one or more standardization constructs associated with an organization and locations thereof. A standardization construct may be generated using supply chain records associated with the organization and locations thereof. The visualizations of the one or more standardization constructs may be adjusted at a user interface. The adjusted one or more standardization constructs may be indicative of ways in which the one or more standardization constructs may be improved.

Abstract: In a computer-implemented method for handling of an index update, time series data is received at an ingestion node of a time series data monitoring system. An index update is determined based on the time series data. The index update is stored to an index database of the time series data monitoring system. The index update is forward to a plurality of query nodes of the time series data monitoring system.

Abstract: A device receives, from a user device, a request to prune a primary database, and receives primary database information associated with the primary database and secondary database information associated with a secondary database that is different than the primary database. The device processes the primary database information and the secondary database information, with a machine learning model, to generate suggested pruning parameters, and provides the suggested pruning parameters to the user device. The device receives selected pruning parameters from the user device, where the selected pruning parameters are selected from the suggested pruning parameters or are input via the user device. The device removes pruned information from the primary database based on the selected pruning parameters, and provides the pruned information to the secondary database based on the selected pruning parameters.

Abstract: A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.

Abstract: Techniques are provided for resynchronizing a synchronous replication relationship. Asynchronous incremental transfers are performed to replicate data of a storage object to a replicated storage object. Incoming write requests, targeting the storage object, are logged into a dirty region log during a last asynchronous incremental transfer. Metadata operations, executed on the storage object, are logged into a metadata log during the last asynchronous incremental transfer. Sequence numbers are assigned to the metadata operations based upon an order of execution. The metadata operations are replicated to the replicated storage object for execution according to the sequence numbers, and the dirty regions are replicated to the replicated storage object in response to the metadata operations having been replicated to the replicated storage object.

Abstract: Techniques are provided for resynchronizing a synchronous replication relationship. Asynchronous incremental transfers are performed to replicate data of a storage object to a replicated storage object. Incoming write requests, targeting the storage object, are logged into a dirty region log during a last asynchronous incremental transfer. Metadata operations, executed on the storage object, are logged into a metadata log during the last asynchronous incremental transfer. Sequence numbers are assigned to the metadata operations based upon an order of execution. The metadata operations are replicated to the replicated storage object for execution according to the sequence numbers, and the dirty regions are replicated to the replicated storage object in response to the metadata operations having been replicated to the replicated storage object.

Abstract: A method for execution-time dynamic range partitioning includes receiving user data including a partitioning key and a clustering key. The user data includes a respective number of total rows defining a total data size for the user data. The method also includes identifying storage constraints for the data storage system. The storage constraints include a target file size and a target number of rows per file. The method further includes determining a plurality of split points for the user data based on the storage constraints. The method also includes generating partitioning quantiles from the plurality of split points that define a range between each split point of the plurality of split points. The method further includes range partitioning each row of the user data into files using the partitioning quantiles.

Abstract: Execution of a query invoking an analytical function (AF) is optimized. The query includes a join operation between an AF table and an AuxiliaryTable. A determination is made that the AF includes a plurality of AF properties. Query-level properties about the query are inferred. A determination is made to change an order of the join operation from the plurality of AF properties and query-level properties.

Abstract: Systems and techniques for reducing probabilistic filter query latency are described herein. A query for a probabilistic filter that is stored on a first media may be received from a caller. In response to receiving the query, cached segments of the probabilistic filter stored on a second media may be obtained. Here, the probabilistic filter provides a set membership determination that is conclusive in a determination that an element is not in a set. The query may be executed on the cached segments resulting in a partial query result. Retrieval of remaining data of the probabilistic filter from the first media to the second media may be initiated without intervention from the caller. Here, the remaining data corresponds to the query and data that is not in the cached segment. The partial query results may then be returned to the caller.

Abstract: A computer system provides improved classification of operating and scheduling plan data of a process plant. The system finds patterns in cases of the plan data and, based on the patterns, organizes the cases into a hierarchical structure of clusters representing distinct conditions. The system receives a dataset of cases of operating plan data represented by process variables. The system reduces a number of process variables representing operating plan data in the dataset by generating principal component(s) from values of the process variables for each case. The principal component(s) are latent variables generated to capture variation in conditions across the cases. For each case, the system determines a value for each generated principal component in the dataset. Using automated clustering or machine learning techniques, the system iteratively clusters the cases into a hierarchical structure based on the respective determined value of each generated principal component.

Abstract: A method and system for determining user-specific proportions of types of content for recommendation to a given user comprising: acquiring for each respective type of content of at least two types of content, a respective base interval of proportion of content for recommendation, computing for each respective type of content of the at least two types of content an associated respective distribution of user interaction parameters associated with a respective set of users, acquiring an associated respective user-specific interaction parameter of the given user, computing for each respective type of content a respective user-specific proportion of the respective type of content for content recommendation to the given user, the respective user-specific proportion being within the respective base interval of proportion of content, the computing being based on: the respective distribution of user interaction parameters of the set of users, and the respective user-specific interaction parameter of the given user.

Abstract: A method and system for determining user-specific proportions of types of content for recommendation to a given user comprising: acquiring for each respective type of content of at least two types of content, a respective base interval of proportion of content for recommendation, computing for each respective type of content of the at least two types of content an associated respective distribution of user interaction parameters associated with a respective set of users, acquiring an associated respective user-specific interaction parameter of the given user, computing for each respective type of content a respective user-specific proportion of the respective type of content for content recommendation to the given user, the respective user-specific proportion being within the respective base interval of proportion of content, the computing being based on: the respective distribution of user interaction parameters of the set of users, and the respective user-specific interaction parameter of the given user.

Abstract: A query is received from a user. A query event type and a query time range associated with the query are determined. An estimated amount of data to be queried associated with the determined query time range is determined based on at least a historical number of the query event type of the user. An allowable amount of data to be queried supported by a database for a single query is determined. One or more sub-queries for the received query are generated. Each sub-query is associated with a different time period within the determined query time range. A corresponding amount of data to be queried associated with each time period is less than, or equal to, the determined allowable amount of data to be queried. The database is queried with the generated one or more sub-queries.

Abstract: In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.

Abstract: A device configured to link correlithm objects to string correlithm objects in a correlithm object processing system includes a string correlithm object generator configured to output a string correlithm object comprising a plurality of sub-string correlithm objects. Each sub-string correlithm object is adjacent in n-dimensional space to a preceding sub-string correlithm object and a subsequent sub-string correlithm object. A node is configured to receive a plurality of non-string correlithm objects, each non-string correlithm object representing data. A memory is configured to store a node table that associates sub-string correlithm objects with non-string correlithm objects such that a first sub-string correlithm object is associated with a first non-string correlithm object and a second sub-string correlithm object is associated with a second non-string correlithm object.

Abstract: Documents are compressed. A partially compressed document is obtained. The partially compressed document includes one or more code words that replace one or more common tokens of a document to be compressed. The one or more common tokens are tokens common to a plurality of documents, and included in a common dictionary. The common dictionary provides a mapping of code words to common tokens. A document associated dictionary is created from non-common tokens of the document to be compressed. The document associated dictionary provides another mapping of other code words to the non-common tokens. A compressed document is created. The creating of the compressed document includes replacing one or more non-common tokens of the partially compressed document with one or more other code words of the document associated dictionary. The compressed document includes the one or more code words of the partially compressed document and the one or more other code words of the document associated dictionary.

Abstract: The present disclosure relates to inferring a time zone of a node profile using electronic activities. A method can include accessing a plurality of electronic activities transmitted or received via a plurality of electronic accounts. The method can include identifying, for a node profile, a set of electronic activities sent from or received by an electronic account of the plurality of electronic accounts linked to the node profile within a time period. The method can include identifying, for each electronic activity of the set of electronic activities, a timestamp at which the electronic activity was sent or received. The method can include generating, for each of a plurality of time intervals within the time period, a temporal distribution of electronic activity based on respective timestamps of each electronic activity. The method can include determining a time zone of the node profile based on the temporal distribution.

Abstract: Described herein are techniques for reading data from a distributed storage system and for writing data to a distributed storage system. The disclosed techniques make use of efficient computing task and thread usage to minimize or reduce overhead and improve read or write efficiency. For example, read or write tasks may handle multiple read or write operations instead of just a single operation, which may reduce overhead associated with task creation and termination. Additionally, operations within a single task may be processed in parallel. For example, the disclosed techniques provide MapReduce implementations useful in Apache Hadoop that perform better than previous MapReduce implementations.

Abstract: A client application is provided with a view of a subset of objects within an object store. A REST protocol, REST interface, or API may be used to access the objects. A coupling server may include a filter and a generator. The generator may automatically generate object metadata for newly created files that matches a filter. A client application may mount a drive on a computer based on metadata objects, such as providing a filter on object metadata to view files a user wants to access; or editing, reading, and updating object metadata as a file.