International Business Machines Corporation

Aubrey J Rembert

Pietro Mazzoleni

Richard Thomas Goodwin

Juhnyoung Lee

Biplav Srivastava

Pankaj Dhoolia

Rakesh Mohan

Debdoot Mukherjee

Anca Andreea Ivan

Vibha Singhal Sinha

Karthik Subbian

Sweefen Goh

Senthil K Mani

Manisha Dattatraya Bhandar

Maharshi H Desai

Kathleen Byrnes

Joel W Branch

Rema Ananthanarayanan

Thomas Dean Rosinski

Gerhard Sigl

Mangala Gowri, Iii

Charbak Chatterjee

Dennis A Conrad

Manas R Kumar Singh

Johnathan M Reason

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.

Process model generated using biased process mining

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.

Process-aware code migration

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.

Automated recognition of process modeling semantics in flow diagrams

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.

System advisor for requirements gathering and analysis in web solutions

An interactive design of process model for packaged application software installation and configuration generates a process model from a natural language description of software installation or configuration related operation. A data structure representing one or more work products related to the process model is identified, and one or more elements of the data structure are identified that are related to one or more sub-processes of the process model. Input information associated with said one or more sub-processes is captured and stored. The input information is linked with the one or more elements of the data structure related to said one or more sub-processes. The one or more sub-processes in the process model may be also demonstrated through a simulation.

Interactive blueprinting for packaged applications

One or more document templates defining deployment procedures associated with deploying a packaged software application are obtained. One or more objects are created representing the one or more document templates and one or more elements of the one or more documents, and one or more links between the one or more objects are created. The one or more links represent one or more relationships between the created one or more objects. The one or more objects and the one or more links may be stored and/or output in a selected format.

Modeling and linking documents for packaged software application configuration

Harvesting assets for packaged application practices, in one aspect, may include obtaining one or more work products associated with deployment of packaged software applications, extracting content and style, enhancing content and style with models of work products, and storing assets in asset repository.

Harvesting assets for packaged software application configuration

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Hawthorne, NY, United States of America

Aubrey J Rembert