Systems and methods are provided for a computing system in a cloud computing environment to receive a request for planning data via a user interface of a computing device accessing a planning application executing in the cloud computing environment and to execute queries corresponding to the request for planning data against an SAP Business Warehouse Integrated Planning Cube (BW cube). The computing system loads data received from the executed queries into an application layer of the planning application executing in the cloud computing environment and stores each manipulation action to the loaded data in the application layer of the planning application executing in the cloud computing environment without persisting any data to the BW cube. The computing system persists the updated data to the BW cube only upon detecting completion of the manipulation actions.

A computer-implemented method can measure query locality during execution of a plurality of incoming queries in a database management system. The database management system includes a query execution plan cache which has a size that can store at least some of query execution plans generated for the plurality of incoming queries. Based on the measured query locality, the method can adjust the size of the query execution plan cache.

In an example embodiment, rather than passing SQL statements directly from an application runtime to a relational database, the SQL statements are first passed to a wrapper functionality. The wrapper functionality takes the uncompelled SQL statements and arguments that need to be passed for the SQL statement as input parameters, executes the statement, and returns the results. This is accomplished by generating a hash identification out of the string SQL statement. The wrapper functionality then looks for a hash identification of an input SQL statement. If it exists, then a corresponding object in the database can be retrieved and executed. If the hash identification is not found, a database object is created that performs the same functionality as the SQL statement. At this time, the SQL statement is also precompiled, and an execution plan created either by the wrapper functionality or the relational database, or both).

Systems and methods for data Process an optimized analytics environment are disclosed. The system may enable users to create data processing requests, interact with various data sources and datasets, and generate data processing outputs. The system may receive a data processing request from an audio-enabled input source or a UI-based input source. The system may determine whether the data processing request at least partially matches a stored data processing request. The system may receive a data processing request selection comprising the data processing request or the stored data processing request. The system may execute the data processing request selection on a data source.

The query method and device suitable for an On-Line Analytical Processing (OLAP) query engine includes a client agent module, a query pattern matching module, a query distributed execution module, and a pre-aggregation module. The query pattern matching module is configured to obtain an MDX query request received by an OLAP query engine and process the MDX query request to generate at least one set of aggregation query sets. The one set of aggregation query sets includes a plurality of aggregation query requests. The query distributed execution module is configured to perform concurrent processing on the plurality of aggregation query requests. The aggregation query requests are arranged corresponding to the aggregation query results. An efficient OLAP query execution engine can deal with complex OLAP queries of various reporting system. Therefore, the execution efficiency of MDX query can be significantly enhanced, and analysis requests of the reporting systems are rapidly responded.

Systems and methods are described for recommending a content item. A search query for a content item is received. The availability of the content item from more than one source is determined. In response to determining that the content item is available from more than one source, the quality of each of the available content items from respective sources is determined. A recommendation factor is determined. The recommendation factor is based on at least one of the bandwidth available to a user device, the resolution capability of the user device, and the quality of experience of each of the sources from which the content item is available. A list of search results for the available content items is generated. The list is ordered based on the quality of each of the available content items from respective sources and the recommendation factor.

As described herein, a N-Gram index may be created and the search may be conducted using the index, which will lead to faster search results. The N-Gram index may also include partial N-Gram components to capture more relevant data. Moreover, as described herein, the search may also take into account recent log data that has not yet been indexed. Techniques for building an index store using log data and efficiently searching the index store and log data to process search requests are described herein.

A computer-implemented method for access path optimization is provided according to embodiments of the present disclosure. In the method, a plurality of real values of an access path factor may be collected during a specified time period. One of the real values may be generated when a query is executed on a first access path. Then, at least one second access path may be generated for the query based on the plurality of real values of the access path factor. Moreover, an optimal access path for the query may be identified from the first access path and the at least one second access path.

A method of performing a data search in a data source by which an operator of a data search pipeline is just-in-time optimized and compiled, using an operator optimization module which optimizes and compiles an intermediate representation of the operator, considering runtime information, and optimization rules, to produce an operator that is optimized for the data search being performed. The method can be applied with one operator or with many operators applied in any sequence or tree structure according to a query plan, as determined by runtime information and optimization rules.

The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g., artificial intelligence classifier) and real-time (or near real-time) query processing.