Abstract:
A database server in a system supporting a cloud platform may train a machine learning model on a set of reports generated by a tenant. Each report of the set of reports may include a title and a query for one or more data objects associated with the tenant. The database server may identify a data lineage for a data set associated with the tenant, where the data set is stored across multiple data sources and includes at least the one or more data objects. The database server may receive a natural language query associated with the data set and generate a set of candidate queries from the natural language query based on the machine learning model and the data lineage. The database server may select one or more of the candidate queries for display on a user interface based on a ranking of the plurality of candidate queries.
Abstract:
Dataflow optimization is described for extractions from a data depository. In one example an object-relationship graph of a data extraction definition is traversed in a first pass. The object-relationship graph has a node for each object. The steps from each node of the graph in the first pass is determined. It is determined if any of the determined steps are repeated. A single instance of each repeated step is placed before other steps in a query plan. Data is then extracted in a second pass from the object-oriented database system according to the object-relationship graph by performing the repeated steps first and then reusing the repeated steps in other determined steps.
Abstract:
In accordance with embodiments, there are provided mechanisms and methods for facilitating dynamic cross-block joining of reports in an on-demand services environment in a multi-tenant environment according to one embodiment. In one embodiment and by way of example, a method includes receiving, at a server computing device, a joining request to merge a plurality of reports into a joined report, each of the plurality of reports having data including customer relationship management (CRM) data, where the request is placed at a client computing device, and merging the plurality of report into the joined report. The joined report comprises a single report representing the merged plurality of reports. The method may further include facilitating access, via a user interface at the client computing device, to the plurality of reports represented as the joined report.
Abstract:
In accordance with embodiments, there are provided mechanisms and methods for facilitating dynamic cross-block joining of reports in an on-demand services environment in a multi-tenant environment according to one embodiment. In one embodiment and by way of example, a method includes receiving, at a server computing device, a joining request to merge a plurality of reports into a joined report, each of the plurality of reports having data including customer relationship management (CRM) data, where the request is placed at a client computing device, and merging the plurality of report into the joined report. The joined report comprises a single report representing the merged plurality of reports. The method may further include facilitating access, via a user interface at the client computing device, to the plurality of reports represented as the joined report.
Abstract:
Methods, systems, and devices for generating a query using training observations are described. According to the techniques described herein, a device (e.g., an application server) may receive a set of queries including a set of fields in a tenant-specific dataset associated with the query. The device may generate a set of training observations for the queries based on the set of fields. The device then trains a first machine learning model to determine grouping hierarchies and a second machine learning model to determine aggregation predictions. The device then builds a combined machine learning model based on the determined grouping hierarchies and the aggregation predictions. According to techniques described herein, the device uses the determined grouping hierarchies and the aggregation predictions to rank a set of suggested queries determined in response to an input query and selects a suggested query for display based on the ranking.
Abstract:
Methods, systems, and devices supporting querying disparate data sources are described. Querying disparate data sources may include receiving an input for data stored at a first data source from a plurality of data sources, selecting a first data connector from a plurality of data connectors, wherein the first data connector corresponds to the first data source, and identifying a first query language corresponding to the first data source from a plurality of query languages. Querying the disparate data sources may further include generating a converted query based at least in part on the first query language and retrieving the data from the first data source using the first data connector based at least in part on the converted query.
Abstract:
A user system includes a user interface, a processor, and one or more stored sequences of instructions. The one or more stored sequences of instructions, when executed by the processor, cause the processor to display a script field within an editor dashboard, of a runtime environment, displayed on the user interface, the editor dashboard configured to define an interactive dashboard of the runtime environment, identify a script entry input into the script field, parse the script entry to identify an operation to be performed within the interactive dashboard in response to a trigger event, and associate the operation with the interactive dashboard, so that the operation will be performed within the interactive dashboard in response to the trigger event based on the association.
Abstract:
In accordance with embodiments, there are provided mechanisms and methods for progressive rendering of report results. These mechanisms and methods for progressive rendering of report results can enable embodiments to render report results in portions as they are received. The ability of embodiments to render report results in portions as they are received can enable report results to be progressively rendered such that delay is avoided Which would otherwise occur when rendering only in response to receipt of an entirety of the report results.
Abstract:
A method for identifying errors in code is provided. The method may include rebuilding object dependencies from a heap dump, calculating memory usage of each object, identifying top consumers of memory by object class, analyzing how much memory each class consumes with respect to how much other classes consume, building a corpus of data that may be used in a progressive machine learning algorithm, and identifying suspect classes. Additionally, the suspect classes and the memory usage statistics of the suspect classes may then be used as an identifying signature of the associated out of memory error. The identifying signature of the associated out of memory error may then be used to compare with the signatures of other out of memory occurrences for identifying duplicate error occurrences.
Abstract:
A method for identifying errors in code is provided. The method may include rebuilding object dependencies from a heap dump, calculating memory usage of each object, identifying top consumers of memory by object class, analyzing how much memory each class consumes with respect to how much other classes consume, building a corpus of data that may be used in a progressive machine learning algorithm, and identifying suspect classes. Additionally, the suspect classes and the memory usage statistics of the suspect classes may then be used as an identifying signature of the associated out of memory error. The identifying signature of the associated out of memory error may then be used to compare with the signatures of other out of memory occurrences for identifying duplicate error occurrences.