A system and method are disclosed for the collection and aggregation of data from contributing members of a community, such as health-related, personal, genomic, medical, and other data of interest for individuals and populations. Contributors become members of a community upon creation of an account and providing of data or files. The data is received and processed, such as to analyze, structure, perform quality control, and curate the data. Value or shares in one or more community databases are computed and attributed to each contributing member. The data is controlled to avoid identification or personalization. Steps are taken to determine incompleteness and incorrectness of the data, and the data may be improved or completed automatically, based upon interaction with members, additional contributions of data, and so forth.

Herein are techniques for dynamic aggregation of results of a database request, including concurrent grouping of result items in memory based on quasi-dense keys. Each of many computational threads concurrently performs as follows. A hash code is calculated that represents a particular natural grouping key (NGK) for an aggregate result of a database request. Based on the hash code, the thread detects that a set of distinct NGKs that are already stored in the aggregate result does not contain the particular NGK. A distinct dense grouping key for the particular NGK is statefully generated. The dense grouping key is bound to the particular NGK. Based on said binding, the particular NGK is added to the set of distinct NGKs in the aggregate result.

Extending access to a data model in a data analytics computer data processing system includes loading into a programmatically isolated process address space of a computer, an instance of an extension framework computer program and executing in the framework, computer program logic configured to establish a communicative channel between the isolated process address space and a data analytics computer data processing system executing in a separate process address space. Thereafter, within the framework a directive may be received to access a data model managed in the data analytics computer data processing system. In response, a function may be selected in respect to an API to the data analytics computer data processing system corresponding to the received directive. Finally, the selected API function may be invoked over the communicative channel and a result derived from the data model may be received in the framework from over the communicative channel in response to the selected API function.

Joining data using a disjunctive operator using a lookup table is described. An example computer-implemented method can include receiving a query with a set of conjunctive predicates and a set of disjunctive predicates. The method may also include generating a lookup table for each predicate in the sets of conjunctive predicates and disjunctive predicates. The method, for each row in a probe-side table, may also further include looking up a value associated with that row in each of the lookup tables and adding the row to a results set when there is a match. Additionally, the method may also include returning the results set.

The present disclosure is directed to an agent automation framework that is capable of extracting meaning from user utterances and suitably responding using a search-based natural language understanding (NLU) framework. The NLU framework includes a meaning extraction subsystem capable of detecting multiple alternative meaning representations for a given natural language utterance. Furthermore, the NLU framework includes a meaning search subsystem that enables elastic confidence thresholds (e.g., elastic beam-width meaning searches), forced diversity, and cognitive construction grammar (CCG)-based predictive scoring functions to provide an efficient and effective meaning search. As such, the disclosed meaning extraction subsystem and meaning search subsystem improve the performance, the domain specificity, the inference quality, and/or the efficiency of the NLU framework.

Certain aspects of the disclosure are directed to context aggregation in a data communications network. According to a specific example, user-data communications between a client-specific endpoint device and the other participating endpoint device during a first time period can be retrieved from a plurality of interconnected data communications systems. The client entity can be configured and arranged to interface with a data communications server providing data communications services on a subscription basis. A context can be determined for each respective user-data communication between the endpoint devices during the first time period. A plurality of user-data communications between the client-specific endpoint device and the other participating endpoint device can be aggregated during a second time period, and a context can be determined for the aggregated user-data communications during the second time period based on a comparison of the aggregated user-data communications and the user-data communications during the first time period.

According to examples, an apparatus may include a processor and a memory on which is stored machine-readable instructions that when executed by the processor, may cause the processor to receive a request for result data from a requestor and determine queries to create the result data. The processor may determine a subset of a data set based on the queries. The subset of the data set may be displayed in small multiples by the requestor. The processor may output the subset of the data set as the result data to the requestor. In some examples, the processor may receive a request for additional result data from the requestor. The processor may determine a second subset of the data set to be displayed in the small multiples and output the second subset of the data set as the additional result data to the requestor.

Database systems and methods that implement a data aggregation framework are provided. The framework can be configured to optimize aggregate operations over non-relational distributed databases, including, for example, data access, data retrieval, data writes, indexing, etc. Various embodiments are configured to aggregate multiple operations and/or commands, where the results (e.g., database documents and computations) captured from the distributed database are transformed as they pass through an aggregation operation. The aggregation operation can be defined as a pipeline which enables the results from a first operation to be redirected into the input of a subsequent operation, which output can be redirected into further subsequent operations. Computations may also be executed at each stage of the pipeline, where each result at each stage can be evaluated by the computation to return a result. Execution of the pipeline can be optimized based on data dependencies and re-ordering of the pipeline operations.

The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.

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.