Abstract: There is disclosed herein a computer-implemented system and method of providing wellness detect and response (WDR) security services for an enterprise, including computing, for the enterprise, a quantitative user-centric security posture, wherein computing the quantitative user-centric security posture comprises calculating, for a user, a quantitative user risk profile according to a combination of user role, user privileges, user behavior, and digital assets assigned to a user and owned by the enterprise.

Abstract: There is disclosed in one example a computer apparatus, including: a hardware platform including a central processor unit (CPU) and a memory; and instructions encoded within the memory to instruct the CPU to: enumerate a plurality of running processes, and associate resource demands with the running processes; predict a resource starvation condition for at least one process; rank the plurality of running processes according to a dynamic ranking algorithm, wherein the ranking algorithm includes user engagement as an input for ranking a process; and according to the ranking and a safeguard algorithm, deallocate resources from a process ranked lower than the at least one process and assign the deallocated resources to the at least one process to mitigate the predicted resource starvation condition.

Abstract: A computer-implemented method provides security services to an enterprise. The method computes, for a plurality of enterprise users, a plurality of user health scores based on respective protection statuses for a plurality of enterprise assets owned by respective users; computes, for the enterprise, an overall enterprise security status score based on the plurality of user health scores; graphically displays to an enterprise administrator the overall enterprise security status score; and presents to the enterprise administrator a plurality of action recommendations to improve the overall enterprise security status score.

Abstract: Aspects of the present disclosure relate to managing data storage resources. In embodiments, one or more data streams are received. Each data stream can include one or more data portions. Further, one or more storage parameters are monitored. Each storage parameter can include data input load of each data stream, data write rate of each stream, number of events in each stream, and data ingestion rates of one or more storage devices. Data storage resources are elastically scales based on any changes to the at least one monitored storage parameter.

Abstract: Implementations are provided herein for atomically committing related stream data across multiple, distributed resources. Transactions can be established that are distributed across multiple hosts, and their data can be made to appear atomic to an observing process. A master status for the transaction can be used to flag to other processes that the transaction is being committed. A stream to which the transaction is being appended to can be locked until the transaction data is committed in full. It can be appreciated that one an event to a transaction is acknowledged, and the transaction is merged, its commitment is guaranteed.

Abstract: Disclosed are various embodiments for scalable workflow processing. In one embodiment, a plurality of decision applications each embody a functional expression of a corresponding one of a plurality of workflow definitions. Each of the plurality of decision applications is configured to be repeatedly instantiated. A workflow execution application is configured to orchestrate an execution of each one of a plurality of workflow instances. The workflow execution application is in data communication with each instance of a respective one of the plurality of decision applications. Each of the plurality of decision applications is configured to determine a next action to be taken for respective ones of the plurality of workflow instances.

Abstract: Implementations are provided herein for managing streaming data that appended and multiplexed into a durable write ahead replicated log. By writing data in the durable log, large amounts of small writes can be processed quickly. Data in the durable log can be de-multiplexed and packaged into segment containers. Segment containers can serialize stream segment specific data and can be stored in long term storage. Data that has been stored in long term storage can be truncated from the durable write ahead log making room for new data.

Abstract: Implementations are provided herein for managing streaming data that appended and multiplexed into a durable write ahead replicated log. By writing data in the durable log, large amounts of small writes can be processed quickly. Data in the durable log can be de-multiplexed and packaged into segment containers. Segment containers can serialize stream segment specific data and can be stored in long term storage. Data that has been stored in long term storage can be truncated from the durable write ahead log making room for new data.

Abstract: Disclosed are various embodiments for scalable workflow processing. In one embodiment, a plurality of decision applications each embody a functional expression of a corresponding one of a plurality of workflow definitions. Each of the plurality of decision applications is configured to be repeatedly instantiated. A workflow execution application is configured to orchestrate an execution of each one of a plurality of workflow instances. The workflow execution application is in data communication with each instance of a respective one of the plurality of decision applications. Each of the plurality of decision applications is configured to determine a next action to be taken for respective ones of the plurality of workflow instances.

Abstract: Disclosed are various embodiments for scalable workflow processing. To this end, a workflow instance is placed in a queue accessible to a processor-based system, the queue being associated with a workflow. A workflow history associated with the workflow instance is applied to a decision application in response to a poll from the decision application, where the decision application is configured to determine a next action to be taken for the workflow instance. The next action to be taken for the workflow instance is initiated in the processor-based system based on a directive from the decision application generated based on the workflow history.

Abstract: Implementations are provided herein for auto-scaling a set of stream segments to a stream. In one implementation, the amount of stream segments can be scaled up and down depending on the amount of data ingested from writers. As the number of stream segments change, writers can have their streaming data transition to a newly merged stream segment or a newly split stream segment. The defined ordering of data as written by the writer is preserved as stream segments are scaled. It can be appreciated that a dynamically scaled stream can offer more capacity than any individual host can provide, while still preserving data order.

Abstract: Implementations are provided herein for atomically committing related stream data across multiple, distributed resources. Transactions can be established that are distributed across multiple hosts, and their data can be made to appear atomic to an observing process. A master status for the transaction can be used to flag to other processes that the transaction is being committed. A stream to which the transaction is being appended to can be locked until the transaction data is committed in full. It can be appreciated that one an event to a transaction is acknowledged, and the transaction is merged, its commitment is guaranteed.

Abstract: Implementations are provided herein for managing streaming data that appended and multiplexed into a durable write ahead replicated log. By writing data in the durable log, large amounts of small writes can be processed quickly. Data in the durable log can be de-multiplexed and packaged into segment containers. Segment containers can serialize stream segment specific data and can be stored in long term storage. Data that has been stored in long term storage can be truncated form the durable write ahead log making room for new data.