Techniques are provided for sharing expression evaluation across a database management system and a storage layer. According to an embodiment, a plurality of target operators are identified that include a common expression. Each respective target operator corresponds to a step that evaluates the common expression during query execution. The plurality of target operators are modified to enable sharing of an evaluation result of the common expression. The query is executed according to the target operators. During query execution, the evaluation result is shared by the target operators. In other embodiments, the evaluation of the common expression is pushed to a storage layer. During query execution, a database management system retrieves the evaluation result of the common expression from the storage layer.

Techniques are described herein for early pruning of potential graph query results. Specifically, based on determining that property values of a path through graph data cannot affect results of a query, the path is pruned from a set of potential query solutions prior to fully exploring the path. Early solution pruning is performed on prunable queries that project prunable functions including MIN, MAX, SUM, and DISTINCT, the results of which are not tied to a number of paths explored for query execution. A database system implements early solution pruning for a prunable query based on intermediate results maintained for the query during query execution. Specifically, when a system determines that property values of a given potential solution path cannot affect the query results reflected in intermediate results maintained for the query, the path is discarded from the set of possible query solutions without further exploration of the path.

A batch data query method and apparatus are provided. According to the method, multiple logical query plan trees corresponding to multiple query statements are obtained. The multiple logical query plan trees are divided into multiple query groups according to operators of each logical query plan tree. Logical query plan trees in each query group have the following relationship: an inter-query write-read dependency relationship, an operator overlap relationship, or an operand overlap relationship. Then, inter-query optimization is performed on the logical query plan trees in each query group, and multiple corresponding physical query plan trees are generated according to the multiple optimized logical query plan trees. Finally, physical queries are executed according to the multiple physical query plan trees, to obtain query results of the multiple query statements.

An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Mechanisms for interpreting queries are provided. In some implementations, a method for interpreting queries is provided, comprising: receiving a search query in a search domain; determining search terms based on the search query; determining, for each of the search terms, whether a search term corresponds to an entity name, wherein the entity name is derived from metadata associated with the search domain; in response to determining that entity names correspond to a portion of the search terms, determining an entity type and an entity score associated with each of the corresponding entity names; determining a remaining portion of the entity names by removing at least one of the matching entity names based on the entity score and contextual information in the search query; and performing a search in the search domain with the remaining portion of entity names, wherein each entity name in the remaining portion of entity names is searched corresponding to the associated entity type.

An information processing apparatus includes a processor configured to receive a narrowing condition designated upon a search request of a content, create a sentence which uses a more detailed expression of an expression described in the narrowing condition and indicates details of the content or select the sentence from sentence candidates prepared in advance, and present the created or selected sentence as a search result of the content.

Mechanisms for interpreting queries are provided. In some implementations, a method for interpreting queries is provided, comprising: receiving a search query in a search domain; determining search terms based on the search query; determining, for each of the search terms, whether a search term corresponds to an entity name, wherein the entity name is derived from metadata associated with the search domain; in response to determining that entity names correspond to a portion of the search terms, determining an entity type and an entity score associated with each of the corresponding entity names; determining a remaining portion of the entity names by removing at least one of the matching entity names based on the entity score and contextual information in the search query; and performing a search in the search domain with the remaining portion of entity names, wherein each entity name in the remaining portion of entity names is searched corresponding to the associated entity type.

A system includes reception of a first instruction at a first system to effect a first change to a hierarchy data model, storage of a first record including first values specifying the first change in a local memory of the first system, reception of a second instruction to effect a second change to the hierarchy data model, storage of a second record including second values specifying the second change in the local memory of the first computer system, reception of an instruction to save the changed hierarchy data model, and, in response to the instruction, transmit the first record and the second record to a second system. The first record and the second record are received and merged to generate a third record including third values specifying a third change to the hierarchy data model, and a query language statement is generated to effect the third change to the hierarchy data model based on the third record.

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.

A tool for combining common processes shared by at least two or more sub-queries within a query is provided. The tool determines whether one or more sub set relationships are shared between the at least two or more sub-queries. Responsive to a determination that one or more sub set relationships are shared between the at least two or more sub-queries, the tool determines an order class for the at least two or more sub-queries based on the one or more sub set relationships, wherein determining the order class includes transforming the query to include one or more differing aspects within the single shared common process, with the one or more differing aspects arranged based, at least in part, on a query style, a query type, and a query function. Responsive to determining an access path for the query, the tool executes the access path during run-time for data accessing.