Abstract: Embodiments relate to a method, system, and computer program product for a process model. The method includes extracting data associated with a process execution trace of a running process and extracting any prior knowledge data relating to the running process. The method also includes calculating at least one transition confidence parameter for the prior knowledge data; and identifying any existing process models relating to the running process. A confidence trace bias is also generated for any existing process model identified. An enhanced bias value is then calculated by combining the confidence trace bias value and value of the transition confidence parameter. Using as input the extracted process execution trace data, the prior knowledge data, the identified existing model and the enhanced bias value, a learned process model is then generated.

Abstract: A mechanism is provided for process-aware code migration. A plurality of nodes and a plurality of edges are annotated within a call graph with information from execution data for executing the code and a topology of the data processing system to form an annotated call graph. Each node in the plurality of nodes is clustered into an associated cluster based on a subset of attributes used from the execution data or the topology to generate the annotated call graph to form a clustered call graph comprising a plurality of clusters. The execution data associated with each cluster in the plurality of clusters is process mined to form an existing process model. Processes identified within the existing process model are mapped to a proposed business-process model thereby forming a set of correlations between the existing process model and the proposed business-process model.

Abstract: Embodiments relate to a method, system, and computer program product for a process model. The method includes extracting data associated with a process execution trace of a running process and extracting any prior knowledge data relating to the running process. The method also includes calculating at least one transition confidence parameter for the prior knowledge data; and identifying any existing process models relating to the running process. A confidence trace bias is also generated for any existing process model identified. An enhanced bias value is then calculated by combining the confidence trace bias value and value of the transition confidence parameter. Using as input the extracted process execution trace data, the prior knowledge data, the identified existing model and the enhanced bias value, a learned process model is then generated.

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.

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 for generating website feature requirements includes receiving a screen capture image including a website. The screen capture image is matched to one or more known website features listed within a database of website features. A website features requirements specification is generated based on the results of the matching. One or more recommendations for modifying the website features requirements specification is automatically generated based on considerations of cost or complexity of implementation. The website features requirements specification is updated based on the one or more recommendations.

Abstract: A method for generating website feature requirements includes receiving a screen capture image including a website. The screen capture image is matched to one or more known website features listed within a database of website features. A website features requirements specification is generated based on the results of the matching. One or more recommendations for modifying the website features requirements specification is automatically generated based on considerations of cost or complexity of implementation. The website features requirements specification is updated based on the one or more recommendations.

Abstract: A mechanism is provided for process-aware code migration. A plurality of nodes and a plurality of edges are annotated within a call graph with information from execution data for executing the code and a topology of the data processing system to form an annotated call graph. Each node in the plurality of nodes is clustered into an associated cluster based on a subset of attributes used from the execution data or the topology to generate the annotated call graph to form a clustered call graph comprising a plurality of clusters. The execution data associated with each cluster in the plurality of clusters is process mined to form an existing process model. Processes identified within the existing process model are mapped to a proposed business-process model thereby forming a set of correlations between the existing process model and the proposed business-process model.

Abstract: Embodiments relate to a method, system, and computer program product for a process model. The method includes extracting data associated with a process execution trace of a running process and extracting any prior knowledge data relating to the running process. The method also includes calculating at least one transition confidence parameter for the prior knowledge data; and identifying any existing process models relating to the running process. A confidence trace bias is also generated for any existing process model identified. An enhanced bias value is then calculated by combining the confidence trace bias value and value of the transition confidence parameter. Using as input the extracted process execution trace data, the prior knowledge data, the identified existing model and the enhanced bias value, a learned process model is then generated.

Abstract: Embodiments relate to a method, system, and computer program product for a process model. The method includes extracting data associated with a process execution trace of a running process and extracting any prior knowledge data relating to the running process. The method also includes calculating at least one transition confidence parameter for the prior knowledge data; and identifying any existing process models relating to the running process. A confidence trace bias is also generated for any existing process model identified. An enhanced bias value is then calculated by combining the confidence trace bias value and value of the transition confidence parameter. Using as input the extracted process execution trace data, the prior knowledge data, the identified existing model and the enhanced bias value, a learned process model is then generated.

Abstract: A method for generating website feature requirements includes receiving a screen capture image including a website. The screen capture image is matched to one or more known website features listed within a database of website features. A website features requirements specification is generated based on the results of the matching. One or more recommendations for modifying the website features requirements specification is automatically generated based on considerations of cost or complexity of implementation. The website features requirements specification is updated based on the one or more recommendations.

Abstract: A method for generating website feature requirements includes receiving a screen capture image including a website. The screen capture image is matched to one or more known website features listed within a database of website features. A website features requirements specification is generated based on the results of the matching. One or more recommendations for modifying the website features requirements specification is automatically generated based on considerations of cost or complexity of implementation. The website features requirements specification is updated based on the one or more recommendations.

Abstract: Embodiments include a method of estimating effects of modifications to a process. The method includes determining, by a processor, first differences between a graphical representation of a first modified process and a base process having been modified by the first modified process. The method also includes comparing, by the processor, the first differences with second differences between a graphical representation of at least one second modified process and the base process. The method also includes estimating a first effect of the first differences relative to the base process based on a similarity between the first differences and the second differences and based on at least one second effect of the second differences relative to the base process, the second effect stored and associated with the second differences in memory.

Abstract: A computer program product estimates effects of modifications to a process. The computer program product includes a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method. The method includes determining first differences between a graphical representation of a first modified process and a base process having been modified by the modified process, comparing the first differences with second differences between a graphical representation of at least one second modified process and the base process, and estimating a first effect of the first differences relative to the base process based on a similarity between the first differences and the second differences and based on at least one second effect of the second differences relative to the base process, the at least one second effect stored, and associated with the second differences in memory.

Abstract: Embodiments include a method for analyzing processes. The method includes receiving, by a processor, a process query and parsing, by the processor, the process query to extract one or more key features from the process query. The method includes determining a relationship between the process query and one or more stored clusters of processes organized at least according to key features based on the parsing of the process query, where the one or more stored clusters are less than all of the stored clusters. The method also includes comparing the one or more key features of the process query with processes in the one or more stored clusters and generating a list of one or more stored processes being the most similar to the process query based on the comparing of the one or more key features of the process query with the processes in the one or more stored clusters.

Abstract: Embodiments include a method for managing processes. The method includes collecting data from first processes and second processes stored in memory, the first processes differing from the second processes by at least one of a formality level and a log detail level. The method includes identifying commonalities among the first processes and second processes and organizing the first processes and second stored processes into clusters based on the commonalities.

Abstract: Embodiments include an apparatus for managing changes in processes. The apparatus includes memory and a processor. The memory includes a base process repository for storing one or more stored base processes, a modified process repository for storing one or more stored modified processes of the one or more stored base processes, and an effects repository for storing effects associated with the stored modified processes of the one or more stored base processes. The processor is configured to determine a difference between a received modified process and the one or more stored base processes, to compare the difference to stored differences between the stored modified processes and the stored base processes, and to estimate effects of the received modified process based on the comparison of the difference of the received modified process with the stored differences and based on effects associated with the stored differences in the effects repository.

Abstract: Embodiments include a method for managing processes. The method includes collecting, by a processor, data from a plurality of processes. The method also includes recognizing, by the processor, commonality among two or more of the plurality of processes based on the data. The method also includes generating suggestions for transforming one or more of the plurality of processes based on the commonality.

Abstract: An apparatus for suggesting changes to processes includes memory and a processor. The memory includes a base process repository for storing data representing one or more base processes, a modified process repository for storing data representing one or more modified processes modifying the one or more base processes, and an effects repository for storing data associated with effects of the one or more modified processes relative to the base processes. The processor is configured to receive data corresponding to a desired base process and desired effects, to compare the desired effects to effects stored in the effects repository, and to provide one or more suggested modified processes from the modified process repository based on a similarity of the suggested modified processes with the desired base process and a similarity of effects associated with the suggested modified processes with the desired effects.

Abstract: Embodiments include an apparatus for managing processes including a processing circuit for obtaining process data corresponding to a plurality of processes from at least one of storage, an external device, and a user, and for performing a method. The method includes parsing the process data to identify key features of the plurality of processes and recognizing, by the processor, commonality among two or more of the plurality of processes based on the key features. The method further includes generating suggestions for transforming one or more of the plurality of processes based on the commonality among the two or more of the plurality of processes.