Data processing systems and methods, according to various embodiments, are adapted for determining an applicable privacy policy based on various criteria associated with a user and the associated product or service. User and product criteria may be obtained automatically and/or based on user input and analyzed by a privacy policy rules engine to determine the applicable policy. Text from the applicable policy can then be presented to the user. A default policy can be used when no particular applicable policy can be identified using by the rules engine. Policies may be ranked or prioritized so that a policy can be selected in the event the rules engine identifies two, conflicting policies based on the criteria.

An allocator tool represents work items in project. Work items may have an associated ID, status, cross-reference to child tasks to be complete before a work item may be complete, a child status of what resource the child is assigned, a sequence ID to indicate in which order a resource is going to work on a work items, and an expectation of how long it will take to do each work item. A calendar aspect of the allocator tool may track when work items are to be performed. Calendar entries may employ formulas/logic/AI to determine whether or not on that date for that work item a resource will be working on a task. Calendar gaps (idle time) may be identified and work items rearranged or reassigned to minimize gaps. Artificial Intelligence may perform calendar gap identification and remediation.

The possibility of a work machine 40 being affected by a disaster in a second designated area including an existence position of the work machine 40 is predicted based on an amount of rainfall in a first designated area. A hazard map representing a result of the prediction of the possibility of the work machine 40 being affected by a disaster in the second designated area is outputted to a remote output interface 220 in a remote operation apparatus 20 (a client) (or a management output interface 620 in a management client 60). Accordingly, a user can take measures to reduce the possibility of the work machine being affected by a disaster, for example, to communicate with the persons involved in order to move the work machine 40 from a current position.

The journeys and/or timelines of multiple customers may be used in escalation management and/or journey mining. An event of interest, pertaining to an issue or an incident, on a timeline may be used in the escalation management and/or journey mining. Escalation management is directed to addressing and resolving incidents, problems, and customer situations which could result in a high level of customer dissatisfaction or damage to a service provider's reputation, using the appropriate response and/or resources. Journey mining is directed to using patterns across customers and their journeys to determine where things in the journey went differently than what was expected.

Systems and methods for market value at risk evaluation are disclosed. In one embodiment, a method for performing a calculation workflow may include (1) a server comprising a computer processor receiving a request for a calculation; (2) the server receiving at least one data parameter; (3) the server identifying a plurality of workflow components required for the calculation; (4) the server identifying dependencies for each identified workflow component; (5) the server ordering the identified workflow components based on the dependencies for each workflow component; (6) the server retrieving data to conduct the calculation; and (7) the server performing the requested calculation using the ordered workflow components based on the data parameter and the data.

Aspects of the disclosure relate to isolating and reinstating nodes in a distributed ledger using proof of innocence. In some embodiments, a first plug-in embedded with the blockchain network may monitor consumer-initiated transactions submitted to an enterprise organization node to determine the legitimacy of each consumer-initiated transaction. The first plug-in may identify consumer-initiated transactions associated with malicious activity and may flag the consumer node for further analysis. A second plug-in may identify and analyze the consumer-initiated transactions associated with the consumer node to determine a proof of innocence value associated with the consumer node. The first plug-in may isolate the consumer node from the distributed ledger if the proof of innocence value exceeds a proof of innocence threshold. Alternatively, the first plug-in may permit the consumer node to remain within the distributed ledger if the proof of innocence value falls below the proof of innocence threshold.

A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.

An online system receives a content item including a link to a landing page and determines a likelihood the landing page violates an online system policy based on a structural similarity between the landing page and a web page violating the policy. To determine the likelihood, the online system determines a hierarchical structure associated with the web page violating the policy and an additional hierarchical structure associated with the landing page. The hierarchical structure represents a structure of at least a portion of the web page and the additional hierarchical structure represents a structure of a corresponding portion of the landing page. The online system compares the hierarchical structure and additional hierarchical structure. Based on the comparison, the online system computes a measure of dissimilarity between the hierarchical structure and additional hierarchical structure and determines a likelihood the landing page violates the policy based on the measure of dissimilarity.

An online system receives a content item including a link to a landing page and determines a likelihood the landing page violates an online system policy based on a structural similarity between the landing page and a web page violating the policy. To determine the likelihood, the online system determines a hierarchical structure associated with the web page violating the policy and an additional hierarchical structure associated with the landing page. The hierarchical structure represents a structure of at least a portion of the web page and the additional hierarchical structure represents a structure of a corresponding portion of the landing page. The online system compares the hierarchical structure and additional hierarchical structure. Based on the comparison, the online system computes a measure of dissimilarity between the hierarchical structure and additional hierarchical structure and determines a likelihood the landing page violates the policy based on the measure of dissimilarity.

A device may receive historical risk data identifying historical risks associated with entities, and historical compliance data identifying historical compliance actions performed by the entities. The device may train a machine learning model with the historical risk data and the historical compliance data to generate a structured semantic model, and may receive entity risk data identifying new and existing risks associated with an entity. The device may receive entity compliance data identifying new and existing compliance actions performed by the entity, and may process the entity risk data and the entity compliance data, with the structured semantic model, to determine risk and compliance insights for the entity. The risk and compliance insights may include insights associated with a key performance indicator, a compliance issue, a regulatory issue, an operational risk, a compliance risk, or a qualification of controls. The device may perform actions based on the risk and compliance insights.