Abstract: Systems and methods are presented for data quality monitoring. Data quality monitors may be created and configured to identify objects with specified data quality issues and/or property values. Objects identified by a data quality monitor can be presented to users for confirmation and resolution. Properties used by the data quality monitor to match objects may also be displayed to users.

Abstract: Techniques for implementing systems using transactional version sets are described. Transactional version sets or t-sets include a collection of elements, each having a collection of metadata. A t-set is transactional in that a sequence of updates to one or more t-sets are made within an atomic transaction. A t-set is versioned since each committed transaction that updates it produces a new timestamped version that can be accessed via time-travel queries.

Abstract: A method for use in a storage system is disclosed, comprising: receiving, at a first server in the storage system, a given block layer request for reservation of a storage resource, by the first server, an identifier corresponding to the given block layer request; performing a search of a database to detect whether the given block layer request has been completed, the search being performed by the first server, the search being performed based on the identifier corresponding to the given block layer request; when the database indicates that the given block layer request has not been completed: completing the given block layer request and transmitting a notification that the given block layer request is completed; and when the database indicates that given block layer request has been completed, re-transmitting a notification that the given block layer request is completed.

Abstract: A system includes first and second subsystems and a third processor. The first subsystem includes a first memory and a first processor. The first memory stores data, which includes metadata associated with transmitted metadata fields. The first processor transmits the data to the second subsystem. The second subsystem includes a second memory and a second processor. The second memory stores expected metadata fields. The second processor receives the data. The third processor determines that the first subsystem transmitted the data to the second subsystem and that a mismatch exists between the transmitted and expected metadata fields. In response, the third processor prevents the second subsystem from executing an application configured to process the data using the expected metadata fields. The third processor resolves the mismatch by modifying the expected metadata fields such that they correspond to the transmitted metadata fields and allows the second subsystem to execute the application.

Abstract: Top frequency worksheet filtering including sending a first request comprising a first search term; receiving, in response to the first request, a first plurality of distinct values having a greatest frequency in a data set and responsive to the first search term, wherein the first plurality of distinct values comprises a number of distinct values less than or equal to a size threshold; presenting the first plurality of distinct values; receiving a selection of a first value from the first plurality of distinct values; and filtering, based on the selected first value, a worksheet comprising a presentation of the data set.

Abstract: A method including obtaining resource overheads according to feature information of a received query request; according to the resource overheads and a compute node resource, dynamically adjusting a compute node in a resource pool; and querying, by using the compute node, data corresponding to the query request. A compute node in a resource pool may be dynamically adjusted, so that the compute node in the resource pool may process all the received query requests, and therefore, the processing efficiency and a resource utilization rate of the compute node are more effectively improved, such that the compute node may more efficiently perform parallel processing on the multiple query requests, and the utilization rates of a CPU resource, a memory resource and a network bandwidth resource are increased, thus achieving better effect from the perspectives of overall computing resource and user query load and improving the usage experience of a user.

Abstract: A method and server for ranking a plurality of documents are disclosed. The method includes determining, by the server, for each document from the plurality of documents a proximity value between a respective document vector and an other document vector. A given proximity value for a respective document being indicative of a similarity between (i) the respective document and (ii) at least one other one from the plurality of documents. The method also comprises using, by the server, the proximity values of the plurality of documents for ranking the plurality of documents.

Abstract: A method includes detecting content items stored locally on a client device, each content item including a time stamp and geospatial metadata; identifying one or more sets of content items based on their time stamps being within a first predetermined time range; determining, for each set of content items, a geographic location associated with the content items of the set based on the geospatial metadata of each content item; generating a list of all geographic locations associated with the sets of content items; selecting subsets of content items based on determining that the time stamps associated with each content item of the subset are within a second predetermined time range; determining, from the selected subsets of content items, non-overlapping subsets of content items based on a parent geographic location associated with the selected subset; and sending instructions to categorize content items into the non-overlapping subsets of content items.

Abstract: Aspects of the disclosure relate to using a machine learning system to process a corpus of documents associated with a user to determine a user-specific consequence index. A computing platform may load a corpus of documents associated with a user. Subsequently, the computing platform may create a first plurality of smart groups based on the corpus of documents, and then may generate a first user interface comprising a representation of the first plurality of smart groups. Next, the computing platform may receive user input applying one or more labels to a plurality of documents associated with at least one smart group. Subsequently, the computing platform may create a second plurality of smart groups based on the corpus of documents and the received user input. Then, the computing platform may generate a second user interface comprising a representation of the second plurality of smart groups.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a content transfer service of a content management system and from a source system, a first content file comprising first content and first content metadata, the first content metadata being stored in a first format, processing the first content file using a set of metadata retrieval definitions to extract file-type-specific metadata from the first content metadata and map at least a portion of the file-type-specific metadata to a first uniform content metadata file having a second format that is different from the first format, each metadata retrieval definition comprising a computer-executable, declarative procedure, and transferring, by the content transfer service, the first content file and the first uniform content metadata file to a target system, the target system consuming the content at least partially based on the first uniform content metadata file.

Abstract: The disclosed embodiments include a method for caching by a data system. The method includes automatically caching a portion of a data object from an external data source to a local cluster of nodes in accordance with a unit of caching. The portion of the data object can be selected for caching based on a frequency of accessing the portion of the data object. The portion of the data object in the cache is mapped to the external data source in accordance with a unit of hashing. The method further includes, responsive to the data system receiving a query for data stored in the external data source, obtaining query results that satisfy the received query by reading the portion of the cached data object instead of reading the data object from the external data source.

Abstract: In an embodiment, a data platform receives a query that includes a two-sided N dimensional interval join of first and second input relations. The data platform samples, with respect to each of one or more of the N dimensions, one or both of the first input relation and the second input relation with respect to an interval size of an interval determined from the input relation. The data platform demarcates the N dimensional input domain into non-overlapping N dimensional input-domain cells based on the sampling. The data platform implements the interval join using a query-execution plan that includes an equality join that is keyed on input-domain-cell identifiers of input-domain cells that at least partially overlap bounding polygons of the intervals determined from the first and second input relations. The equality join is followed in the query-execution plan by a filter that applies the interval-join predicate. The data platform obtains results of the query by executing the query-execution plan.

Abstract: A method for managing the configuration of an in-memory database running in a cluster of computing nodes may include receiving a configuration request to configure an instance of the in-memory database. The configuration request may include an identifier of a configuration profile specifying a value for each of one or more parameters of the in-memory database. The presence of the instance of the in-memory database and the configuration profile specified in the configuration request may be verified. Upon verifying the presence of the in-memory database and the configuration profile, the instance of the in-memory database may be configured based the configuration profile. The configuring may include setting, to the value specified in the configuration profile, the one or more parameters of the in-memory database. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: A data normalization system receives a first string and a second string that are ordered according to an initial string ordering. The data normalization system analyzes, the first string and the second string based on a list of known character sets included in surnames, yielding an analysis, and determines, based on the analysis, that a set of characters in the second string matches a known character set included in the list of known character sets included in surnames. In response to determining that the set of characters in the second string matches a known character set included in the list of known character sets included in surname, the data normalization system orders the first string and the second string according to an updated string ordering.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an object graph from records of a relational database. One of the methods includes receiving a request to populate a software object with values from a plurality of tables represented in a graph, the plurality of tables stored in a relational database maintained by a database management system, each table comprising one or more respective tuples of values; generating a single query statement that, when executed by the database management system, causes the database management system to retrieve the values from the plurality of tables represented in the graph as a result set; querying the relational database using the single query statement to obtain the result set; and populating the software object with one or more values of the result set.

Abstract: A method, apparatus, system, and computer program product for training a global machine learning model. A hierarchical structure for nodes in which the global machine learning model is located at a primary node in the hierarchical structure is identified. Authorized nodes in which local data is authorized for use in training in the authorized nodes for a local training of local machine learning models are determined. The machine learning models in the authorized nodes are trained using the local data in the authorized nodes to generate local model updates to weights in the local machine learning models. The local model updates to the weights are propagated upward in the hierarchical structure to the global machine learning model, wherein a node receiving local model updates to the weights from nodes from a lower level aggregates the weights in the local model updates received from the nodes in the lower level.

Abstract: Disclosed herein are systems and methods for pre-filter deduplication for multidimensional two-sided interval joins. In an embodiment, a data platform receives query instructions for a two-sided N dimensional interval join, where N is an integer greater than 1. The two-sided N dimensional interval join has an interval-join predicate that compares intervals determined from the input relations in each of N dimensions. The data platform implements the two-sided N dimensional interval join as a query-plan section that includes an N dimensional band join that is followed by a deduplication operator that is followed by a filter that applies the interval-join predicate. The N dimensional band join includes a hash join keyed to N dimensional domain cells overlapped at least in part by intervals determined from the input relations in each of the N dimensions. The deduplication operator removes duplicate rows from a potential-duplicates subset of the output of the N dimensional band join.

Abstract: In an embodiment, a data platform implements a two-sided N dimensional interval join using an N dimensional band join followed by a filter that applies a predicate of the interval join. The data platform generates first and second modified relations from first and second input relations. Each modified relation includes a copy of each row from the corresponding input relation for each input-domain cell that overlaps, in each of N dimensions, a bounding polygon of intervals determined from the row of the corresponding input relation. The data platform inserts, in each row in each modified relation, an input-domain-cell identifier of the corresponding overlapping input-domain cell and uses a hash-equality join that receives the first and second modified relations and that is keyed on the input-domain-cell identifiers. The data platform obtains results of a query by executing a query-execution plan that includes the query-plan section.

Abstract: Methods, systems, and apparatuses for generating notifications corresponding to queries submitted for execution by virtual warehouses are described herein. A request to execute a query may be received. An execution plan, for the query, may be identified. A processing complexity for the query may be predicted based on the query and the execution plan. A notification may be generated based on the processing complexity meeting an alert threshold. A user device may display the notification. A response to the notification comprising a selection of a first virtual warehouse, of a plurality of virtual warehouses, to execute the query may be received.

Abstract: In some implementations, a system can optimize offline map data updates. For example, a server device in the system can determine a metric for identifying map data objects based on attributes of the map data objects. The server device can then generate a quadtree that stores the map data objects in nodes of the quadtree based on the metric. When processing an update to the map data stored at the server device, the server device can generate update data describing the updates for each node in the quadtree based on a binary difference algorithm and/or a semantic difference algorithm. The server device can select the algorithm based on which algorithm results in the smallest compressed size of the update data.