A search interface is displayed in a table format that includes one or more columns, each column including data items of an event attribute, the data items being of a set of events, and a plurality of rows forming cells with the one or more columns, each cell including one or more of the data items of the event attribute of a corresponding column. Based on a user selecting one or more of the cells, a list of options if displayed corresponding to the selection, and one or more commands are added to a search query that corresponds to the set of events, the one or more commands being based on at least an option that is selected from the list of options and the event attribute for each of the one or more of the data items of each of the selected one or more cells.

A search interface is displayed in a table format that includes one or more columns, each column including data items of an event attribute, the data items being of a set of events, and a plurality of rows forming cells with the one or more columns, each cell including one or more of the data items of the event attribute of a corresponding column. Based on a user selecting one or more of the cells, a list of options if displayed corresponding to the selection, and one or more commands are added to a search query that corresponds to the set of events, the one or more commands being based on at least an option that is selected from the list of options and the event attribute for each of the one or more of the data items of each of the selected one or more cells.

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.

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.

A claim editing engine for automated integration and error resolution of claim records is provided. The processor of the engine is configured to extract a set of claim components of a plurality of claim components. The processor is further configured to transform the set of claim components to conform to a standardized data format. The processor is also configured to integrate the set of transformed claim components into a set of unified claims by unifying each of the set of transformed claim components having matching claim identifiers into a unified claim. The processor is configured to apply a rule set to the set of unified claims to generate a simulation of execution of the set of claims and identify errors in the simulated execution. The processor is configured to transmit an instruction to resolve each identified error. The processor is configured to cause each resolved unified claim to be processed.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering user interface event data for analysis and retrieval are disclosed. In one aspect, a system includes a data store and computer(s) that interact with the data store and execute instructions that cause the computer(s) to receive, for a user interface event, event data specifying a structure of a user interface presented during the user session. The event is assigned to a respective cluster based on a comparison of the structure of the user interface specified by the event data to a user interface structure that represents the respective cluster. For each cluster, a user interface attribute indicative of a user interface state of user interfaces specified by the event data in the cluster is determined. User interface state groups are generated based on the user interface attribute for each cluster.

Object service receives communication of fingerprints stream, corresponding to file segments, from file source, and identifies sequential fingerprints in fingerprints stream as fingerprints group. Object service identifies group identifier for fingerprints group, and communicates fingerprints group to deduplication service associated with group identifier range including group identifier. Deduplication service identifies fingerprints in fingerprints group which are missing from fingerprint storage, and communicates identified fingerprints to object service, which communicates request for file segments, corresponding to identified fingerprints, to file source. Deduplication service receives communication of requested segments from file source, and stores requested segments. System identifies generation identifier associated with time of communicating by object service or deduplication service and identifies generation identifier associated with another time of communicating by object service or deduplication service. If generation identifier associated with time differs from generation identifier associated with other time, object service or deduplication service restarts communication.

A cluster view method of a database to perform compaction and clustering of database objects, such as database materialized view is shown. The database can comprise a cache to store changes to storage units of tables of the database objects. The cluster view method can implement clustering to remove data based on the cache and clustering to group the data of the materialized view.

Embodiments of the present disclosure may provide a data protection system that performs identification of errors from queries on a database. The data protection system can further identify corrupted data from additional errors, are difficult to detect, and occur between layers of data in the database system. The data protection system can perform corrections of the error data by rebuilding database data or removing the corrupted data.

Computer-implemented techniques for data extraction are described. The techniques include a method and system for retrieving an extraction job specification, wherein the extraction job specification comprises a source repository identifier that identifies a source repository comprising a plurality of data records; a data recipient identifier that identifies a data recipient; and a schedule that indicates a timing of when to retrieve the plurality of data records. The method and system further include retrieving the plurality of data records from the source repository based on the schedule, creating an extraction transaction from the plurality of data records, wherein the extraction transaction comprises a subset of the plurality of data records and metadata, and sending the extraction transaction to the data recipient.