In an aspect, provided is a method comprising monitoring one or more data analysis sessions, determining, based on the monitoring, a common data analysis technique performed across common data analysis sessions, identifying the common data analysis technique as a precedent, and providing the precedent to a precedent engine.

The present disclosure relates to generating performance profiles of member nodes. A plurality of electronic activities can be accessed. A subset of electronic activities from the plurality of electronic activities can be identified. The subset of electronic activities can be parsed to identify participants of the electronic activities. A second node profile can be accessed for each participant. Participant types can be identified from each second node profiles. A distribution of the subset of electronic activities can be determined. A performance profile can be generated.

In one aspect, a computer implemented method for translating and executing rules using a directed acyclic graph is provided. The method includes transforming a ruleset into a directed acyclic graph. The directed acyclic graph includes a plurality of nodes and a plurality of branches. The method further includes identifying similarities across the plurality of branches. The method further includes grouping branches of the directed acyclic graph based on the identified similarities. The method further includes creating a modified directed acyclic graph based on the grouping. The method further includes selecting and using a method of processing a group of the modified directed acyclic graph based on an aspect of the group.

In an aspect, provided is a method comprising monitoring one or more data analysis sessions, determining, based on the monitoring, a common data analysis technique performed across common data analysis sessions, identifying the common data analysis technique as a precedent, and providing the precedent to a precedent engine.

The present disclosure relates to platform configured for executing and rendering templates to enable automation of control and data exploration across systems in an interactive and non-interactive mode. Initially, the platform is configured to add template to an exploration either on user request or system driven request and execute the template by extracting execution model associated with the template. Further, the target system is configured to run the execution plan in interactive mode wherein the user may provide inputs or non-interactive mode wherein the target system is configured to run the execution plan without user inputs. The target system is further configured to receive data generated by the target systems in response to the operation executed by the execution engine. On receiving the data from target systems in the data store, the target system is configured to visualize the data on the template rendered on the GUI.

Techniques are described for managing internet-of-things (IoT) devices, such as managing the storage of data generated by the IoT devices, managing the access, to the data, by users, processes, and/or other entities, managing command and control of the devices, and so forth. In some implementations, an IoT platform is provided for IoT device management, and the IoT platform can be agnostic with respect to providers. For example, the IoT platform may provide one or more common interfaces that enable communications with IoT devices that are manufactured by different device providers. In some implementations, a distributed ledger system (DLS) is employed to facilitate IoT device management. For example, the DLS can act as a gateway and/or overall interface to control access of users, processes, devices, IoT device providers, and/or other entities to the IoT devices and/or to an IoT platform.

Systems and methods for performing differential and full data protection operations. Parameters that define minimum, interval, and maximum values are provided to an agent. These values relate to changes in the data or in a database. The agent monitors the database and backups are triggered in accordance with these values. A differential backup is not performed until the minimum value is reached and differential backups are performed only after subsequent intervals are reached. A full backup is performed once the maximum value is reached.

The present disclosure relates to apparatus, systems, and methods for analyzing characteristics of entities of interest. In particular, the present disclosure provides a mechanism for analyzing information about entities of interest and for rating or scoring the entities of interest based on the analyzed information. The rating or the score of an entity of interest can sometimes be referred to as a placerank value of an entity of interest.

In some embodiments, the present invention provides for an exemplary computer system which includes at least: a graphical user interface client; a dedicated application server; the dedicated application server is configured to connect to the graphical user interface client and an electronic source with electronic data records; where the electronic data records include real identification identifiers of real individuals; where the graphical user interface client is configured to generate at a graphical user interface that is configured to receive user authenticating credential information and to conduct a real-time electronic negotiation querying session between the user and the dedicated application server to generate a plurality of non-reversible synthetic electronic data records of a plurality of synthetic individuals, by utilizing at least one statistical technique so that the plurality of non-reversible synthetic electronic data records cannot be used to identify any real individual in the plurality of electronic data records.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a set of time event type definitions that each specifies a type of time event. The program further receives a set of time attribute definitions. The program also receives a set of rule definitions that each specifies a rule that includes a set of instructions for processing time data. The program further receives a set of time template definitions that each specifies time event types defined by a subset of the set of time event type definitions, time attributes defined by a subset of the set of time attribute definitions, and rules defined by a subset of the set of rule definitions. The program further uses a subset of the set of time templates to process a set of raw time event data and generate a set of time data entries.