Abstract: Methods and systems for pruning process execution logs include learning a predictive model from a set of execution traces that characterize a process, where the predictive model determines a likelihood of a given instance reaching a specified outcome; identifying attributes in the predictive model that fall below a threshold measure of relevance to the specified outcome using a processor; and removing the identified attributes from the set of execution traces.

Abstract: An analyzer system may include a computer-apparatus to collect traces from a pool of business traces, and to assign an unique vector value to each trace. The system may also include an assembler to create a tree based upon the unique vector value of each trace. The system may further include an analyzer to detect sub-trees within the tree to identify similarities among the traces based upon the traces inclusion within a given sub-tree.

Abstract: Methods and systems for refining a process model include determining whether the process model is too dense or too sparse. A predictive model is learned from execution traces to predict an outcome. The predictive model is modified responsive to the determination of whether the process model is too dense or too sparse. A refined process model is refined from updated traces based on attributes present in the modified predictive model.

Abstract: Systems and methods for data analysis include correlating event data to provide process instances. The process instances are clustered, using a processor, by representing the process instances as strings and determining distances between strings to form a plurality of clusters. One or more metrics are computed on the plurality of clusters to monitor deviation of the event data.

Abstract: Systems and methods for data analysis include correlating event data to provide process instances. The process instances are clustered, using a processor, by representing the process instances as strings and determining distances between strings to form a plurality of clusters. One or more metrics are computed on the plurality of clusters to monitor deviation of the event data.

Abstract: Systems and methods for data analysis include constructing patient traces as a set of medical events for each patient of a patient population, the patient population being segmented based on patient outcomes. Medical events in one or more of the patient traces are reduced to provide processed patient traces. The processed patient traces are clustered to identify a cluster of patient traces. A process model is mined, using a processor, representing an aggregation of treatment pathways in the patient traces from the cluster. Patterns from patient traces are identified that are discriminative of patient outcomes. At least one of the patterns is represented with respect to the process model to identify treatment pathways correlated with the patient outcomes.

Abstract: Systems and methods for data analysis include constructing patient traces as a set of medical events for each patient of a patient population, the patient population being segmented based on patient outcomes. Medical events in one or more of the patient traces are reduced to provide processed patient traces. The processed patient traces are clustered to identify a cluster of patient traces. A process model is mined, using a processor, representing an aggregation of treatment pathways in the patient traces from the cluster. Patterns from patient traces are identified that are discriminative of patient outcomes. At least one of the patterns is represented with respect to the process model to identify treatment pathways correlated with the patient outcomes.

Abstract: Systems and methods for data analysis include constructing patient traces as a set of medical events for each patient of a patient population, the patient population being segmented based on patient outcomes. Medical events in one or more of the patient traces are reduced to provide processed patient traces. The processed patient traces are clustered to identify a cluster of patient traces. A process model is mined, using a processor, representing an aggregation of treatment pathways in the patient traces from the cluster. Patterns from patient traces are identified that are discriminative of patient outcomes. At least one of the patterns is represented with respect to the process model to identify treatment pathways correlated with the patient outcomes.

Abstract: Methods and systems for refining a process model include determining whether the process model is too dense or too sparse. A predictive model is learned from execution traces to predict an outcome. The predictive model is modified responsive to the determination of whether the process model is too dense or too sparse. A refined process model is refined from updated traces based on attributes present in the modified predictive model.

Abstract: A method for refining a process model includes mining a process model from a set of execution traces; determining whether the process model is too dense or too sparse; learning a predictive model from the execution traces to predict an outcome; modifying the predictive model; and mining a refined process model from updated traces based on attributes present in the modified predictive model. Modifying the predictive model includes making the predictive model more specific if it is determined that the process model is too dense; and making the predictive model more general if it is determined that the process model is too sparse.

Abstract: A method of case management includes receiving a plurality of previously executed case instances, receiving a selection of current case attributes and at least one candidate case outcome during runtime of a currently executing case instance, and generating a visual representation of case distributions using the previously executed case instances. The visual representation depicts a correlation between the current case attributes and the at least one candidate case outcome, and is generated using analytics applied to the plurality of previously executed case instances.

Abstract: A method for refining a process model includes mining a process model from a set of execution traces; determining whether the process model is too dense or too sparse; learning a predictive model from the execution traces to predict an outcome; modifying the predictive model; and mining a refined process model from updated traces based on attributes present in the modified predictive model. Modifying the predictive model includes making the predictive model more specific if it is determined that the process model is too dense; and making the predictive model more general if it is determined that the process model is too sparse.

Abstract: Methods and systems for pruning process execution logs include learning a predictive model from a set of execution traces that characterize a process, where the predictive model determines a likelihood of a given instance reaching a specified outcome; identifying attributes in the predictive model that fall below a threshold measure of relevance to the specified outcome using a processor; and removing the identified attributes from the set of execution traces.

Abstract: A method of processing raw events to typed events, each raw event including data items containing data values, the method includes: providing a library of event type objects, each event type object relating to a given event type and including attributes of given data types, the attributes in each event type object structured according to a given structure; for each raw event, determining an event type object in the library which meets: (i) each of the data items in the raw event maps to an attribute in the event type object, and (ii) a data value in each of the data items is of a data type detected to match the given data type of the mapped attribute; and generating a typed event from each raw event, the typed event including the data values of the raw event structured according to the structure of the determined event type object.

Abstract: Methods and systems for mapping an event type to an activity in a business process model are disclosed. In accordance with one such method, the event type and the activity are tokenized by determining event tokens for event type labels in the event type and determining activity tokens for activity labels in the activity. In addition, a score matrix is generated for pairs of the event tokens and the activity tokens indicating a degree of similarity between the event token and the activity token in each of the pairs. The method also includes determining whether the event type and the activity are correlated by determining scores of the pairs of event tokens and activity tokens that are ranked highest in said score matrix. Further, a mapping report indicating whether the event type and the activity are correlated in the business process model is output.

Abstract: A method (and structure) for implementing a software tool, as executable by a processor on a computer to exercise any of a plurality of prediction tools. Questions are provided to a user output port, and inputs from a user input port are received as responses to the questions. The question responses are used to instantiate, customize, and configure a specific one of said plurality of prediction tools for executing a specific application on the software tool.

Abstract: Systems and methods for data analysis include determining a patient trace as a set of medical events for a patient. Medical events of the patient trace are grouped into subsets of medical events using a processor according to a temporal relationship between the medical events. Co-occurring events are identified from the subsets of medical events as event clusters. A plurality of medical events in one or more of the subsets of the patient trace is represented using the event clusters to condense the patient trace.

Abstract: Systems and methods for data analysis include determining a patient trace as a set of medical events for a patient. Medical events of the patient trace are grouped into subsets of medical events using a processor according to a temporal relationship between the medical events. Co-occurring events are identified from the subsets of medical events as event clusters. A plurality of medical events in one or more of the subsets of the patient trace is represented using the event clusters to condense the patient trace.

Abstract: Systems and methods for data analysis include constructing patient traces as a set of medical events for each patient of a patient population, the patient population being segmented based on patient outcomes. Medical events in one or more of the patient traces are reduced to provide processed patient traces. The processed patient traces are clustered to identify a cluster of patient traces. A process model is mined, using a processor, representing an aggregation of treatment pathways in the patient traces from the cluster. Patterns from patient traces are identified that are discriminative of patient outcomes. At least one of the patterns is represented with respect to the process model to identify treatment pathways correlated with the patient outcomes.

Abstract: Systems and methods for data analysis include correlating event data to provide process instances. The process instances are clustered, using a processor, by representing the process instances as strings and determining distances between strings to form a plurality of clusters. One or more metrics are computed on the plurality of clusters to monitor deviation of the event data.