Abstract: An example operation may include one or more of receiving edge data that has been captured by one or more edge devices in an Internet of Things (IoT) network, encrypting the received edge data and storing the encrypted edge data as one or more transactions within a blockchain, determining, by a blockchain node, that an event has been captured by the received edge data based on metadata of the received edge data, and in response to determining that the event has been captured, generating a notification associated with the event being captured and outputting the generated notification for display on a user device.

Abstract: An example operation may include one or more of storing encrypted IoT data as transactions in a blockchain, the IoT data being captured by one or more edge devices of an IoT network, receiving an identification of an event, and in response, retrieving encrypted IoT transaction data stored in the blockchain which is associated with the event, decrypting, via a blockchain node, the retrieved IoT transaction data associated with the event and generating anonymized data of the event from the decrypted IoT transaction data in which personally identifiable information is anonymized, and outputting information concerning the anonymized data of the event to one or more of a user device and a display device.

Abstract: An example operation may include one or more of storing encrypted IoT data as transactions in a blockchain, the IoT data being captured by one or more edge devices of an IoT network, receiving an identification of an event, and in response, retrieving encrypted IoT transaction data stored in the blockchain which is associated with the event, decrypting, via a blockchain node, the retrieved IoT transaction data associated with the event and generating anonymized data of the event from the decrypted IoT transaction data in which personally identifiable information is anonymized, and outputting information concerning the anonymized data of the event to one or more of a user device and a display device.

Abstract: An example operation may include one or more of receiving edge data that has been captured by one or more edge devices in an Internet of Things (IoT) network, encrypting the received edge data and storing the encrypted edge data as one or more transactions within a blockchain, determining, by a blockchain node, that an event has been captured by the received edge data based on metadata of the received edge data, and in response to determining that the event has been captured, generating a notification associated with the event being captured and outputting the generated notification for display on a user device.

Abstract: A method, apparatus and computer-usable medium for the selective, contextual retrieval and presentation of information used in review processes. Search criteria is entered for information to be retrieved for review and information source documents are then searched, maintaining references to the contextual levels encountered (e.g., chapter, section, sub-section, etc.). Once the search is completed, a hierarchical list of sentence extracts matching the search criteria and indicating their location in their respective source document is presented to the reviewer. The user can then select any level of the hierarchical view, which then displays an expanded view of the related section of the source document. The retrieved information can be exported to a plurality of formats, which are annotatable by the reviewer. Users are thereby provided with information relevant to the subject under review, structured and presented in the context of its usage within its associated source document.

Abstract: An invention is disclosed for improved computer system management by allowing complex computer-monitored events to be handled in different formats for correlation, analysis and action. Specifically, a method and system are disclosed for processing multiple heterogeneous event types in a complex event processing (CEP) engine.

Abstract: Embodiments of the present invention provide a method, system and computer program product for cross-cutting event correlation in an enterprise computing monitoring and management system. An enterprise computing monitoring and management system can include a hierarchy of nodes, where several of the nodes are each coupled to a corresponding embedded correlation engine and an event bus. The system further can include a root node among the nodes. The root node can be coupled to the event bus and to centralized correlation logic programmed to identify a high correspondence between events from a particular event source among the nodes and a particular set of correlation rules in that correlation engine. The identification of such correspondence can be used to move to the set of correlation rules to an embedded correlation engine closer to the particular event source.

Abstract: A system and method for enhancing event correlation with exploitation of external data is presented. A correlation engine receives events and selects a correlation rule that corresponds to the events. The correlation rule includes an event selection, a trigger condition, and a correlation conclusion. The correlation engine uses the event selection to access external data and select events based upon the external data. In turn, the correlation engine monitors the selected events and checks whether they meet the correlation rule's trigger condition. When the events meet the correlation rule's trigger condition, the correlation engine performs an action based upon the correlation rule's correlation condition.

Abstract: Under the present invention, a sequence of actions made by a user in operating a software system is tracked. Upon request by the user, predictive help is given based on the sequence of actions. In a typical embodiment, the predictive help is contained in a knowledge base, which is initially built during development of the software system. Specifically, the knowledge base is populated based on sequences of actions by other users as they operate the software system during its development. Moreover, the knowledge base can be updated after release of the software system as the user operates the system. To this extent, the knowledge base will “learn” the patterns of the user, and the predictive help will be continually tailored based thereon.