The present disclosure provides a method of user clustering, and the method includes: acquiring a clustering condition for a predetermined user group, wherein the clustering condition includes a time selecting condition and an event selecting condition; determining at least one target time period for each user behavior data in a user behavior database based on the time selecting condition; determining association data indicating a relationship between the each user behavior data and each target time period based on the each user behavior data and the each target time period; and selecting target association data for a time period to be monitored based on the time period to be monitored and the event selecting condition, so as to determine a target user belonging to the predetermined user group according to the target association data. The present disclosure also provides an apparatus of user clustering, a computer device and a non-transitory medium.

Providing access to usage reports on a cloud-based data warehouse including maintaining, by a management module, a metadata table on the cloud-based data warehouse, wherein the metadata table comprises usage reports for a plurality of organizations; receiving, by the management module, a request for the metadata table from an administrator account for a first organization of the plurality of organizations; granting, by the management module, the administrator account permission to access a filtered portion of the metadata table, wherein the filtered portion of the metadata table is generated by filtering the metadata table by an organization identifier of the first organization; and providing, by the management module, the filtered portion of the metadata table to the administrator account.

A method for assisting launch of a machine learning model includes: acquiring a model file from offline training of the machine learning model; determining a training data table used in a model training process by analyzing the model file; creating in an online database an online data table having consistent table information with the training data table; and importing at least a part of offline data into the online data table.

A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

An information processing apparatus acquires a plurality of lists including one or more rows from table data including a plurality of columns and a plurality of rows, based on a value of one column in the plurality of columns and extracts, based on a list including a predetermined search key among the acquired plurality of lists, a value corresponding to the search key. Thus, for example, even in a case where there is no character string on the right side of the search key, value information corresponding to the search key is extractable.

A centralized database identifier repository may identify databases using a unique identifier, or key tag, for each database. Each identified database may include data relating to one or more specific data elements. The repository may include a variety of data elements. Each data element may be associated with one or more database keys. The repository may be a repository of reference pointers. The repository may facilitate data viewing and data retrieval. A requestor may search for a data element using the centralized repository. The repository may retrieve data relating to a specific data element, from all databases identified by unique identifiers, that include data relating to the data element. The databases' unique identifiers may be encrypted tokens.

A method may include inserting, into a hash trie, data records from a database table. The inserting may include traversing the hash trie to identify, for each data record included in the database table, a corresponding node at which to insert the data record. The hash trie may be traversed based on a hash of a key value associated with each data record. The node at which to insert a data record may be identified based on an offset forming a binary representation of the hash of a key value associated with that data record. The offset may include a portion of a plurality of binary digits forming the binary representation. A data record may be inserted at a corresponding node by updating a data structure included at the node. A database operation may be performed based on the hash trie filled with the data records from the database table.

Operations include halting a runaway query in response to determining that a performance metric of the query exceeds a performance threshold. The runaway query halting system receives a query execution plan associated with a query and divides the received execution plan into one or more components. For each component, the system determines a predicted resource usage associated with executing the component. The system further determines a predicted resource usage associated with the query execution plan based on the predicted resource usage associated with each component. The system executes the query associated with the received query execution plan and compares the predicted resource usage associated with the query to a resource usage threshold. In response to determining that the predicted resource usage of the query execution plan exceeds the resource usage threshold, the system halts execution of the query associated with the query execution plan.

A method for providing optimized support for transactional processing and analytical processing with minimal memory footprint may include storing, on a data page in a disk of a database system, a portion of one or more columns of data from a database table. A metadata associated with the data page may be stored on a metadata page in the disk of the database system. The metadata may include one or more byte ranges on the data page at which the portion of the one or more columns of data is stored. The database system may execute one or more queries by accessing, based at least on the metadata associated with the data page, a portion of the data page storing the portion of the one or more columns of data required by the one or more queries. Related systems and articles of manufacture are also provided.

A method for mitigating errors in the transportation of configuration data may include identifying, at a development system, dependent configuration data associated with a first transport request. The dependent configuration data may implement a customization to a software application hosted at a production system. A reference table identifying the dependent configuration data may be sent to the production system. A missing object list identifying dependent configuration data absent from the production system may be generated at the production system based on the reference table. The missing object list may be sent to the development system where a corrective action may be performed such that the dependent configuration data identified by the missing object list as being absent from the production system is sent to the production system in the first transport request and/or a second transport request. Related systems and articles of manufacture, including computer program products, are also provided.