From Informal Process Diagrams To Formal Process Models
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From Informal Process Diagrams To Formal Process Models

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Process modeling is an important activity in business transformation projects. Free-form diagramming tools, such as PowerPoint and Visio, are the preferred tools for creating process models. However, ...

Process modeling is an important activity in business transformation projects. Free-form diagramming tools, such as PowerPoint and Visio, are the preferred tools for creating process models. However, the designs created using such tools are informal sketches, which are not amenable to automated analysis. Formal models, although desirable, are rarely created (during early design) because of the usability problems associated with formal-modeling tools. In this paper, we present an approach for automatically inferring formal process models from informal business process diagrams, so that the strengths of both types of tools can be leveraged. We discuss different sources of structural and semantic ambiguities, commonly present in informal diagrams, which pose challenges for automated inference. Our approach consists of two phases. First, it performs structural inference to identify the set of nodes and edges that constitute a process model. Then, it performs semantic interpretation, using a classifier that mimics human reasoning to associate modeling semantics with the nodes and edges. We discuss both supervised and unsupervised techniques for training such a classifier. Finally, we report results of empirical studies, conducted using flow diagrams from real projects, which illustrate the effectiveness of our approach.

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    From Informal Process Diagrams To Formal Process Models From Informal Process Diagrams To Formal Process Models Presentation Transcript

    • IBM Research - India, New Delhi, India‡ IBM TJ Watson Research Center, New York, USA†
    •  Free form diagramming tools (e.g., Visio, Powerpoint) are preferred in creation for initial process models  Ease of use, Intuitiveness  Ubiquity  Doesn’t hinder your creativity  Process modeling software (e.g., WBM, ARIS) create models with formal underpinnings  Allow formal analysis, model checking  Process Reuse  Process Improvement  Traceability with realized executable process  Sound, automatic approach to convert process diagrams to formal process models is essential  A bridge between the worlds of diagramming and formal modeling September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    •  Challenges  Ambiguities in diagrams  Limitation of existing capabilities  Approach  Structure Inference  Semantic Interpretation  Empirical Study  Related Work & Future directions September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Human can interpret different visual cues in drawings to correctly resolve the structure and semantics of the models, but machines cannot do the same! September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Connectors not glued to shapes at their endpoints Missing Edge Missing Edge Missing Edge
    • Text annotations not explicitly part of any shape for node/edge
    • Same shape conveys multiple semantics Same semantic conveyed in multiple shapes September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    •  Popular BPM tools such as Websphere Business Modeler, ARIS, Lombardi, Telelogic System Architect, have Visio import capabilities  Create imprecise flow structure when faced with structural ambiguities  Employ a simple mapping (fixed or pluggable) from a set of diagram shapes to a target set of process semantics to interpret semantics  Such an approach cannot deal with under-specification  Building an exhaustive mapping is painful in presence of over-specification September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Process Diagram Parsing Diagram Attributes such as Use format specific Parse information coordinates, dimensions, SDKs or parse XML about diagram shapes text, geometry formats Shapes & Attributes Structure Inference Precisely determine the Deal with structural Extract features for each underlying flow graph ambiguities node and edge Flow Graph Semantic Interpretation Assign process semantics to every Process node and edge in the flow graph using Supervised and unsupervised schemes Model to train such a classifier a trained classifier September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Process Diagram Parsing Diagram Attributes such as Use format specific Parse information coordinates, dimensions, SDKs or parse XML about diagram shapes text, geometry formats Shapes & Attributes Structure Inference Precisely determine the Deal with structural Extract features for each underlying flow graph ambiguities node and edge Flow Graph Semantic Interpretation Assign process semantics to every Process node and edge in the flow graph using Supervised and unsupervised schemes Model to train such a classifier a trained classifier September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • A B A B C D C D September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    •  Uses notion of connection points created at node – line SRC SRC SRC SRC TGT and line – line intersections SRC NEU NEU C1 C2 C5 C8 A B  Assign direction to connection C3 TGT UNK C6 points SRC SRC C4 TGT C7  Starting at connection points attached to nodes, propagate C D their directions along paths in which the directions are consistent and identifies the reached nodes  Create edges if connection point at reached node has a different direction September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Process Diagram Parsing Diagram Attributes such as Use format specific Parse information coordinates, dimensions, SDKs or parse XML about diagram shapes text, geometry formats Shapes & Attributes Structure Inference Precisely determine the Deal with structural Extract features for each underlying flow graph ambiguities node and edge Flow Graph Semantic Interpretation Assign process semantics to every Process node and edge in the flow graph using Supervised and unsupervised schemes Model to train such a classifier a trained classifier September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    •  Train a classifier to mimic human reasoning to decide process semantics  Features used for classification:  Relational: Indegree, Outdegree, Count of nodes contained within  Geometric: Shape name, Count of horizontal, vertical, diagonal lines  Textual: Count of cue words for every target entity September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Structure {Nodes, Edges} Flow Annotated by Diagrams Inference Features Classifier {Nodes, Edges} Annotated by Features + Process Semantic Classifier establishes correspondence An expert labels all nodes & between the features and labels for edges in the input set of process semantics diagrams by their semantics September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Flow Structure {Nodes, Edges} Clusterer Diagrams Inference Annotated by Features Cluster A = Semantic X {Nodes, Edges} Cluster A Annotated by Features + Process Semantic Cluster B = Semantic Y Cluster B Clusters have An expert looks at common exemplars from each semantics Classifier cluster to label process semantic of the cluster September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    •  Data Set: 185 Visio process diagrams created in real business-transformation projects  Objective: Compare accuracy of our tool iDISCOVER and a popular modeling tool (called PMT for proprietary reasons)  Method: Compare tool outputs with models created manually by human experts to measure precision & recall  Precision = |Actual ∩ Retrieved| , Recall = |Actual ∩ Retrieved| |Retrieved| |Actual| September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Node 96.93 95.91 70.44 86.29 Edge 93.26 90.86 63.43 59.87 Dangling 47 (100%) 3 (14%) 56% Connector Unlinked Labels 46 (39%) 2 (3.7%) 38% Count of dangling connectors has a greater correlation with the edge recall of (ρ = −0.48) than with the edge recall of (ρ = −0.08). September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    • Our (Overall Δ ≈30%) and (Overall Δ ≈20%) for all process semantic classes •Accuracy is low only for scarce entities like Intermediate Events and Data Objects (together are greater than that of less than 3% of the data set) is almost as good as •Better results possible with a more equitable distribution of entities work almost Size of the training data need not be huge. Classification could as well with only a third of the dataset size September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    •  Large body of work in the area of understanding line drawings and hand sketches (e.g., Futrelle, Gross, Barbu)  Focus on identifying shape geometry  Semantic interpretation follows directly from a fixed mapping between shape geometry and target semantics  Visual Language theory prescribes geometry detection with grammar rules. September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    •  More efficient modeling of textual cues  Text is the only reliable feature in highly ambiguous scenarios  Tracking spatial patterns of shapes and labels that emerge due to local styles  Identification of higher-level relations (block structures) between model entities (e.g., sub- process, loop, and fork-merge)  Extend to other diagram types September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA
    •  Informal process diagrams contain structural and semantic ambiguities – need to be dealt with in order to discover precise formal models  Existing capabilities are limited because:  Do not resolve structural ambiguities  Interpreting semantic based on shape name does not suffice  Standard pattern-classification techniques can be successfully employed in interpreting process semantics if the feature space is carefully modeled to mimic human reasoning  Unsupervised clustering can almost match supervised techniques in performance September 14 ,2010, International Conference on Business Process Management, Hoboken, NJ, USA