The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Systems and methods are provided for using point-in-time architecture (PTA) databases. An exemplary method includes: entering first data, received from a first data source, into a first PTA database; receiving a first instruction to process the first data using a first statistical operation; executing the first statistical operation for the first data, resulting in first output data; filtering the first output data based on a user-selected attribute; and performing multiple stages of a data processing operation for the first output data.

An embodiment relates to a computer-implemented data processing system and method for storing a data set at a plurality of data centers. The data centers and hosts within the data centers may, for example, be organized according to a multi-tiered ring arrangement. A hashing arrangement may be used to implement the ring arrangement to select the data centers and hosts where the writing and reading of the data sets occurs. Version histories may also be written and read at the hosts and may be used to evaluate causal relationships between the data sets after the reading occurs.

Provided are a data archiving method and apparatus capable of providing a remote near-line data archiving function by receiving remote function invoking from a target system in which data is stored, providing the target system with a first function for archiving, in a storage system, at least some of the data stored in the target system over a network in response to the remote function invoking, and providing the target system with a second function for the query of the data archived in the storage system over the network.

Time-based partitioning of a data set is applied to capture updates to the data set in a copy of the data set. Items that have been updated in a data set with in a time period are identified. Partitions of the data set that include the updated items are created according to a partitioning scheme. The created partitions are grouped in a storage location for the time period in a file structure that stores a copy of the database. A latest version of the copy of the data set may be accessed according to latest partitions of the data set stored in the different locations of the file structure.

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.

Data protection methods and systems for a storage environment are provided. A first-in-first out (FIFO) structure stores a logical representation of a first storage location that retains previous data for a data container, after new data for the data container is stored at a second storage location. The FIFO structure also stores a logical representation of a file system tree structure that is stored in persistent storage, after a consistent point operation. In response to an event, the file system tree structure is selected, based on the file system tree structure being closest to a transaction. A snapshot is generated using the file system tree structure. Thereafter, the data container is restored from the snapshot or from a copy of the snapshot.

A device retrieves historical data and new data each a respective hardware component identifier and a respective associated value. The device creates a synthesized set of data by having subsets for anomalous data, data that is associated with an attenuation signal, and other data. The device discards the anomalous data and weights the data associated with an attenuation signal. The device generates a searchable database, the searchable database including each hardware component named by an entry of the synthesized set of data, along with an associated value determined based on the weighted value of the entry. The device receives user input of a search query, and outputs search results based on a comparison of the user input of the search query to entries of the searchable database.

Techniques and technologies for providing a fully managed datastore for clients to securely store, discover, retrieve, remove, and share curated data, or features, to develop machine learning (ML) models in an efficient manner. The feature store service may provide clients with the ability to create and store feature groups that include features and associated metadata providing clients with a quick understanding of features so that they may determine which features are suitable for training ML models and/or use with ML models. The feature store service may provide first a data store configured to store the most recent values associated with a feature group, such that client can access the features and utilize ML models to make real-time predictions with low latency and high throughput, and a second datastore configured to store historical values associated with a feature group, such that a client can utilize the features to train ML models.