A query method and a query device, where the method includes determining N execution plans respectively corresponding to N query requests according to the received N query requests, determining at least two same first sub-plans, generating a first sub-query result corresponding to any one of the at least two same first sub-plans, and in a process of generating, according to the N query requests, a query result corresponding to each of the N query requests, the same first sub-query result corresponding to any one of the first sub-plans is used for all the same first sub-plans. Hence, a large amount of repeated calculation can be reduced, database system resources are reduced, and query efficiency and a throughput of a database system in a large-scale concurrent query application scenario are improved.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

Embodiments described herein are directed to generating and returning materialized views for queries (or subexpressions thereof) having a particular relationship with each other. For instance, machine learning-based techniques may be utilized to identify query subexpressions that have at least one of a semantically equivalent relationship or a containment relationship with each other. Responsive to identifying such relationship(s), a materialized view may be generated for the identified subexpressions. When a query is subsequently received, machine learning-based techniques may be utilized to determine whether a subexpression of the query possesses at least one of a semantically equivalent relationship or a containment relationship with another subexpression for which a materialized view has been generated. Responsive to determining that such a subexpression of the query possesses one or more of such relationships, the materialized view generated for the other subexpression is returned.

The present teaching relates to method, system, and programming for evaluating expressions. An expression indicative of conditions and metadata associated therewith is obtained. A determination is made as to whether the expression corresponds to a modified version of an earlier expression based on the metadata. In response to a determination that the expression is the modified version of the earlier expression, a query associated with the modified expression is transmitted to a forecasting cluster so that the modified expression is to be evaluated by the forecasting cluster. In response to a determination that the expression does not have a corresponding earlier expression, the expression is evaluated.

A data pipeline architecture provides for efficient and scalable data collection within a 5G wireless network. The data pipeline includes a data collection engine that receives streaming and/or query-based data. The data collection engine collects the data, amalgamates the streaming and the querly-based data into a common format, and provides the amalgamated data for delivery to a data reporting engine. The data reporting engine provides dashboards, reports, alerts or other information about the collected data. The data reporting engine may also interface with a database system for longer-term storage of collected data, report generation and/or the like.

A computer-implemented method of evaluating a set of filter parameters being represented by a filter tree comprising a plurality of nodes. The method can include identifying whether a node is a root of a sub-tree comprising other nodes of the filter tree; generating a cost for said node by processing a sample input comprising a plurality of data items of a data source using the filter parameter and measuring the time taken for the plurality of data items to be processed; and determining a selectivity of said node based on an output of its filter parameter as a result of processing the sample input using the filter parameter; then ordering at least some of the plurality of nodes of the filter tree having the same parent node based on their relative costs and selectivities, for use in generating an ordered filter tree.

Techniques are described for storing and maintaining, in a materialized view, bitmap data that represents a bitmap of each possible distinct value of an expression and rewriting a query for a count of distinct values of the expression using the materialized view. The materialized view contains bitmap data that represents a bitmap of each possible distinct value of a first expression, and aggregate values of additional expressions, and is stored in memory or on disk by a database system. The database system receives a query that requests a number of distinct values, of the first expression, and an aggregate value for an additional expression. In response, the database system, rewrites the query to: compute the number of distinct values by counting the bits in the bitmap data of the materialized view that are set to the first value, and obtains the aggregate value for the additional expression in the materialized view.

Optimizing database statements using a query compiler including receiving, by a query compiler from a client computing system, a state specification of a graphical user interface; compiling, by the query compiler, a database statement from the state specification, including: optimizing the database statement by repositioning, within the database statement, a limit clause such that the limit clause is processed by the database before at least one join clause; and sending, by the query compiler, the optimized database statement to a database on a cloud-based data warehouse.

Aspects of the present disclosure relate to providing, based on a partial query string, a plurality of autosuggestions that are diverse in nature such that the user is more likely to see the preferred complete query terms and therefore more likely to select one of the preferred suggestions which will increase search efficiency. As described herein, such functionality relates to generating cluster groups of candidate suggestions, each cluster including sub-topics, then performing the search based on a selected cluster or sub-topic. To generate the cluster groups, systems and methods, as described herein, analyze the similarity between candidate suggestions as well as the popularity of generated sub-topics. The cluster groups and sub-topics may be displayed visually, and in certain embodiments the cluster groups are ordered vertically from top to bottom and aligned to the left side of the display, while sub-topics are ordered horizontally from left to right following the cluster label.

An input is accessed representing a tuning parameter for a first query statement and a tolerance level. The tolerance level represents a degree of acceptable discrepancy between the first query statement and another query statement. A first fingerprint is generated for the first query statement based on a content of the first query statement and the tolerance level; and the first fingerprint and the tuning parameter are stored. The first fingerprint is used as an index for an optimizer to associate the tuning parameter with a second query statement that corresponds to the first fingerprint.