Abstract: A method and apparatus for estimating a wave velocity of negative pressure wave in a fluid transportation pipeline. The method including: receiving a plurality of pressure signals from a plurality of sensors; determining time differences produced by the negative pressure wave reaching the adjacent sensors based on the received pressure signals; determining a wave source sensor segment where a wave source of the negative pressure wave is located; and estimating the wave velocities of the negative pressure wave in a non-wave source sensor segment and the wave source sensor segment.

Abstract: An expert conversation builder contains a knowledge database that includes a plurality of dialogues having nodes and edges arranged as directed acyclic graphs. Users and authors of the system interface with the knowledge database through a graphical interface to author dialogues and to create expert conversations as threads traversing the node in the dialogues.

Abstract: A method for predicting anomalies in a computer application includes during runtime of the computer application, detecting traffic metrics and incident tickets associated with the computer application, the incident ticket indicating an incident might occur in the computer application; calculating a threshold based on absolute values of second order differences associated with the traffic metrics, wherein the threshold is such that when the absolute value of the second order difference associated with the traffic metrics exceeds the threshold, a recall rate Rrecall that the computer application is recalled is maximized; obtaining predicted metrics of the computer application in a next time period based on the traffic metrics; and in response to an absolute value of a second order difference associated with the predicted metrics exceeding the threshold, predicting potential anomalies of the computer application in the next time period.

Abstract: One or more problems may be detected in an executing application by retrieving runtime execution information from the application executing on one or more computers. The runtime information is transformed into a temporal sequence of events. A knowledgebase is searched for a dialog that has nodes in an order that match the temporal sequence of events according to a threshold degree. Responsive to finding the dialog in the knowledgebase, the dialog is launched on a user interface to interact with a user and guide the user through a problem identification and solution. Responsive to not finding the dialog, additional instrumenter is enabled in the application.

Abstract: One or more problems may be detected in an executing application by retrieving runtime execution information from the application executing on one or more computers. The runtime information is transformed into a temporal sequence of events. A knowledgebase is searched for a dialog that has nodes in an order that match the temporal sequence of events according to a threshold degree. Responsive to finding the dialog in the knowledgebase, the dialog is launched on a user interface to interact with a user and guide the user through a problem identification and solution. Responsive to not finding the dialog, additional instrumenter is enabled in the application.

Abstract: A method for predicting anomalies in a computer application includes during runtime of the computer application, detecting traffic metrics and incident tickets associated with the computer application, the incident ticket indicating an incident might occur in the computer application; calculating a threshold based on absolute values of second order differences associated with the traffic metrics, wherein the threshold is such that when the absolute value of the second order difference associated with the traffic metrics exceeds the threshold, a recall rate Rrecall that the computer application is recalled is maximized; obtaining predicted metrics of the computer application in a next time period based on the traffic metrics; and in response to an absolute value of a second order difference associated with the predicted metrics exceeding the threshold, predicting potential anomalies of the computer application in the next time period.

Abstract: A method for predicting anomalies in a computer application includes during runtime of the computer application, detecting traffic metrics and incident tickets associated with the computer application, the incident ticket indicating an incident might occur in the computer application; calculating a threshold based on absolute values of second order differences associated with the traffic metrics, wherein the threshold is such that when the absolute value of the second order difference associated with the traffic metrics exceeds the threshold, a recall rate Rrecall that the computer application is recalled is maximized; obtaining predicted metrics of the computer application in a next time period based on the traffic metrics; and in response to an absolute value of a second order difference associated with the predicted metrics exceeding the threshold, predicting potential anomalies of the computer application in the next time period.

Abstract: Providing an open data marketplace may include identifying one or more data attributes from a plurality of applications registered with a shared data management system; mapping said one or more data attributes to a data model to create a first set of mapped data; augmenting said first set of mapped data with additional data imported into the shared data management system from other data locations to create an augmented set of mapped data; receiving one or more of second set of mapped data from one or more shared data management system associated respectively with one or more shared data management networks; enabling of composing a data set by combining and cleansing one or more of said first set of mapped data, said second set of mapped data, and said augmented set of mapped data; and offering said composed data set in a digital marketplace with associated pricing characteristics.

Abstract: A method and apparatus for estimating a wave velocity of negative pressure wave in a fluid transportation pipeline. The method including: receiving a plurality of pressure signals from a plurality of sensors; determining time differences produced by the negative pressure wave reaching the adjacent sensors based on the received pressure signals; determining a wave source sensor segment where a wave source of the negative pressure wave is located; and estimating the wave velocities of the negative pressure wave in a non-wave source sensor segment and the wave source sensor segment.

Abstract: A method for predicting anomalies in a computer application includes during runtime of the computer application, detecting traffic metrics and incident tickets associated with the computer application, the incident ticket indicating an incident might occur in the computer application; calculating a threshold based on absolute values of second order differences associated with the traffic metrics, wherein the threshold is such that when the absolute value of the second order difference associated with the traffic metrics exceeds the threshold, a recall rate Rrecall that the computer application is recalled is maximized; obtaining predicted metrics of the computer application in a next time period based on the traffic metrics; and in response to an absolute value of a second order difference associated with the predicted metrics exceeding the threshold, predicting potential anomalies of the computer application in the next time period.

Abstract: A method and apparatus for estimating a wave velocity of negative pressure wave in a fluid transportation pipeline. The method including: receiving a plurality of pressure signals from a plurality of sensors; determining time differences produced by the negative pressure wave reaching the adjacent sensors based on the received pressure signals; determining a wave source sensor segment where a wave source of the negative pressure wave is located; and estimating the wave velocities of the negative pressure wave in a non-wave source sensor segment and the wave source sensor segment.

Abstract: Building a runtime dialog is provided. An initial answer to an initial question is received from a user. A state of a conversation thread is created based on the initial answer. A next question to ask is automatically selected by a processor based on the updated state of the conversation thread. The next question is presented to the user. A next answer to the next question is received. The state of the conversation thread is updated based on the next answer. The selecting, presenting, receiving, and updating are repeated until a criterion is satisfied.

Abstract: One or more problems may be detected in an executing application by retrieving runtime execution information from the application executing on one or more computers. The runtime information is transformed into a temporal sequence of events. A knowledgebase is searched for a dialog that has nodes in an order that match the temporal sequence of events according to a threshold degree. Responsive to finding the dialog in the knowledgebase, the dialog is launched on a user interface to interact with a user and guide the user through a problem identification and solution. Responsive to not finding the dialog, additional instrumenter is enabled in the application.

Abstract: One or more problems may be detected in an executing application by retrieving runtime execution information from the application executing on one or more computers. The runtime information is transformed into a temporal sequence of events. A knowledgebase is searched for a dialog that has nodes in an order that match the temporal sequence of events according to a threshold degree. Responsive to finding the dialog in the knowledgebase, the dialog is launched on a user interface to interact with a user and guide the user through a problem identification and solution. Responsive to not finding the dialog, additional instrumenter is enabled in the application.

Abstract: Diagnosing and detecting causes of an incident may comprise classifying the incident by keywords, searching for co-occurring and reoccurring group of incidents, summarizing commonalities in the group of incidents, correlating the group of incidents with causes, defining association rules between the commonalities, and predicting potential problems based on the correlated group of incidents with causes.

Abstract: Diagnosing and detecting causes of an incident may comprise classifying the incident by keywords, searching for co-occurring and reoccurring group of incidents, summarizing commonalities in the group of incidents, correlating the group of incidents with causes, defining association rules between the commonalities, and predicting potential problems based on the correlated group of incidents with causes.

Abstract: A method for predicting anomalies in a computer application includes during runtime of the computer application, detecting traffic metrics and incident tickets associated with the computer application, the incident ticket indicating an incident might occur in the computer application; calculating a threshold based on absolute values of second order differences associated with the traffic metrics, wherein the threshold is such that when the absolute value of the second order difference associated with the traffic metrics exceeds the threshold, a recall rate Rrecall that the computer application is recalled is maximized; obtaining predicted metrics of the computer application in a next time period based on the traffic metrics; and in response to an absolute value of a second order difference associated with the predicted metrics exceeding the threshold, predicting potential anomalies of the computer application in the next time period.

Abstract: A method for predicting anomalies in a computer application includes during runtime of the computer application, detecting traffic metrics and incident tickets associated with the computer application, the incident ticket indicating an incident might occur in the computer application; calculating a threshold based on absolute values of second order differences associated with the traffic metrics, wherein the threshold is such that when the absolute value of the second order difference associated with the traffic metrics exceeds the threshold, a recall rate Rrecall that the computer application is recalled is maximized; obtaining predicted metrics of the computer application in a next time period based on the traffic metrics; and in response to an absolute value of a second order difference associated with the predicted metrics exceeding the threshold, predicting potential anomalies of the computer application in the next time period.

Abstract: Financial transformation in one aspect establishes a hub and spoke network of models including at least a process model as the hub and at least components model, value driver model, applications model, solutions models as the spoke connected to the process model. A plurality of correlations is inferred among said models using daisy-chain analysis that navigate the hub and spoke network of models, and heat map is generated. Heat map analysis, application shortfall analysis, organization shortfall analysis, solution analysis, and business case analysis are performed.

Abstract: An example embodiment disclosed is a system for automated model extraction of documents containing flow diagrams. An extractor is configured to extract from the flow diagrams flow graphs. The extractor further extracts nodes and edges, and relational, geometric and textual features for the extracted nodes and edges. A classifier is configured to recognize process semantics based on the extracted nodes and edges, and the relational, geometric and textual features of the extracted nodes and edges. A process modeling language code is generated based on the recognized process semantics. Rules to recognize patterns in process diagrams may be determined using supervised learning and/or unsupervised learning. During supervised learning, an expert labels example flow diagrams so that a classifier can derive the classification rules. During unsupervised learning flow diagrams are clustered based on relational, geometric and textual features of nodes and edges.