Abstract: A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.

Abstract: Examples for graph database query classification include receiving a graph query and determining if the graph query matches benchmark data. In the event that the graph query does not match benchmark data, the query may be parsed, a canonical internal representation of the query may be determined, the representation may be mapped to a rule, and the query may be classified based on the rule. In the event that the confidence score for the query classification does not exceed a threshold, the query may be sent to a synthetic graph or synopsis for simulation. In some examples, the simulation may include selecting computationally expensive graph operators in the query for simulation.

Abstract: Examples relate to citation explanations. A process to provide citation explanation is provided herein. The process analyzes a primary document to extract a citation claim. The process generates a set of candidate segments of a cited document that may correspond to the citation claim. The process also analyzes the set of candidate segments.

Abstract: A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.

Abstract: Examples for graph database query classification include receiving a graph query and determining if the graph query matches benchmark data. In the event that the graph query does not match benchmark data, the query may be parsed, a canonical internal representation of the query may be determined, the representation may be mapped to a rule, and the query may be classified based on the rule. In the event that the confidence score for the query classification does not exceed a threshold, the query may be sent to a synthetic graph or synopsis for simulation. In some examples, the simulation may include selecting computationally expensive graph operators in the query for simulation.

Abstract: Example implementations relate to a database transfer of changes. For example, a computing device may include at least one processor. The at least one processor may receive a stream of changes to an online transaction processing (OLTP) database and may cache the stream of changes in a buffer. The at least one processor may identify specified criteria indicating a manner of sending the stream of changes to an online analytical processing (OLAP) database and may transfer the stream of changes from the buffer to the OLAP database based on the specified criteria.

Abstract: Example implementations relate to read-optimized database changes. For example, a computing device may include at least one processor. The at least one processor may receive a change associated with a read-optimized database and may identify at least one hot attribute associated with the change. The at least one processor may generate an insert operation based on the change using the at least one hot attribute and may send the insert operation to a hot table associated with the read-optimized database.

Abstract: Examples relate to citation explanations. A process to provide citation explanation is provided herein. The process analyzes a primary document to extract a citation claim. The process generates a set of candidate segments of a cited document that may correspond to the citation claim. The process also analyzes the set of candidate segments.

Abstract: Example implementations relate to speculative execution of a stream of changes. For example, a computing device may include at least one processor. The at least one processor may receive a stream of changes concurrently received by an online transaction processing (OLTP) database engine in communication with the computing device. The at least one processor may process the stream of changes based on speculative execution and verify that an order of the stream of changes processed based on speculative execution matches an OLTP order of the stream of changes committed by the OLTP database engine. The at least one processor may send the stream of changes processed based on speculative execution to an online analytical processing (OLAP) database engine to be stored in an OLAP database.

Abstract: A computer-implemented method for accessing content items in a content store are described. In one embodiment, the computer-implemented method includes maintaining a text index of content items in a content store to enable a keyword search on the content items, receiving a query having a keyword and generating a hit list from the text index using the keyword, and extracting frequent phrases from text within content items of the hit list. The computer-implemented method also includes assigning a relative relevance to the frequent phrases and grouping content items into topics based on presence of relevant phrases within the content items of the hit list. The hit list includes one or more content items of the content store. The frequent phrases having a relatively high relevance are relevant phrases.

Abstract: A computer-implemented method for accessing content items in a content store are described. In one embodiment, the computer-implemented method includes maintaining a text index of content items in a content store to enable a keyword search on the content items, receiving a query having a keyword and generating a hit list from the text index using the keyword, and extracting frequent phrases from text within content items of the hit list. The computer-implemented method also includes assigning a relative relevance to the frequent phrases and grouping content items into topics based on presence of relevant phrases within the content items of the hit list. The hit list includes one or more content items of the content store. The frequent phrases having a relatively high relevance are relevant phrases.