Algorithms for clustering data
Algorithms for clustering data
Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
C4.5: programs for machine learning
C4.5: programs for machine learning
Recognizing multistroke geometric shapes: an experimental evaluation
UIST '93 Proceedings of the 6th annual ACM symposium on User interface software and technology
Recognizing and interpreting diagrams in design
AVI '94 Proceedings of the workshop on Advanced visual interfaces
Communications of the ACM
Relational grammars: theory and practice in a visual language interface for process modeling
Visual language theory
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Tahuti: a geometrical sketch recognition system for UML class diagrams
ACM SIGGRAPH 2006 Courses
SUMLOW: early design-stage sketching of UML diagrams on an E-whiteboard
Software—Practice & Experience
Forms-based service composition
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
Form-Based Web Service Composition for Domain Experts
ACM Transactions on the Web (TWEB)
| Hi-index | 0.00 |
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.