Techniques are described for providing a ML data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.

A sequence of context based search verb and search terms are selected via either touch or voice selection in a mobile wireless device and then a human articulated voice query is expanded using a culture and a world intelligence dictionary for conducting more efficient searches. Focus groups are used for populating prior query search databases for storage in the mobile wireless device that are organized by context based search terms in a mobile wireless device for efficient search.

A sequence of context based search verb and search terms are selected via either touch or voice selection in a mobile wireless device and then a human articulated voice query is expanded using a culture and a world intelligence dictionary for conducting more efficient searches. Focus groups are used for populating prior query search databases for storage in the mobile wireless device that are organized by context based search terms in a mobile wireless device for efficient search.

An example method for mapping data can include: generating a user interface configured to enable a user to create a data element of a mapping specification, wherein the mapping specification includes a spreadsheet having a plurality of data fields; allowing for dragging of the data element onto the user interface and multi-selection of the data element with other data elements; allowing for dropping of the data element into a desired location of the user interface and the multi-selection of the data element; storing the data element in a temporary schema independent from a database schema of the data warehouse; and enabling the user to associate the data element with one or more physical data elements in the database schema.

An example method for mapping data can include: generating a user interface configured to enable a user to create a data element of a mapping specification, wherein the mapping specification includes a spreadsheet having a plurality of data fields; allowing for dragging of the data element onto the user interface and multi-selection of the data element with other data elements; allowing for dropping of the data element into a desired location of the user interface and the multi-selection of the data element; storing the data element in a temporary schema independent from a database schema of the data warehouse; and enabling the user to associate the data element with one or more physical data elements in the database schema.

Prioritizing crawled data in a document store for reprocess operations. Reprocessing occurs upon a triggering change to configurations. Prioritization is based on the status of the crawled data with respect to an ACL. During reprocessing, the crawled data is reprocessed in an order defined by assigned priority levels.

Prioritizing crawled data in a document store for reprocess operations. Reprocessing occurs upon a triggering change to configurations. Prioritization is based on the status of the crawled data with respect to an ACL. During reprocessing, the crawled data is reprocessed in an order defined by assigned priority levels.

In some aspects, the disclosure is directed to methods and systems for data storage and retrieval from a computer memory. A computing device may store a first hierarchical data structure having a first sequence of sub-data structures and a second hierarchical data structure having a second sequence of sub-data structures in memory. The computing device may link the first hierarchical data structure and the second hierarchical data structure together. The computing device may link the first hierarchical data structure and the second hierarchical data structure by inserting an identifier of a sub-data structure of the second sequence in the first sequence.

In some aspects, the disclosure is directed to methods and systems for data storage and retrieval from a computer memory. A computing device may store a first hierarchical data structure having a first sequence of sub-data structures and a second hierarchical data structure having a second sequence of sub-data structures in memory. The computing device may link the first hierarchical data structure and the second hierarchical data structure together. The computing device may link the first hierarchical data structure and the second hierarchical data structure by inserting an identifier of a sub-data structure of the second sequence in the first sequence.

Dashboard loading from a cloud-based data warehouse cache, including determining that a result for a first query is stored in a cache of a cloud-based data warehouse; sending, in response to the result being stored in the cache, to the cloud-based data warehouse, a request for the result from the cache; and providing, based on the result for the first query, one or more dashboard visualizations.