On Engineering Support for Business Process Modelling and Redesign

Currently, there is an enormous (research) interest in business process redesign (BPR). Several management-oriented approaches have been proposed showing how to make BPR work. However, detailed descriptions of empirical experience are few. Consistent engineering methodologies to aid and guide a BPR-practitioner are currently emerging. Often, these methodologies are claimed to be developed for business process modelling, but stem directly from information system design cultures. We consider an engineering methodology for BPR to consist of modelling concepts, their representation, computerized tools and methods, and pragmatic skills and guidelines for off-line modelling, communicating, analyzing, (re)designing\ud business processes. The modelling concepts form the architectural basis of such an engineering methodology. Therefore, the choice, understanding and precise definition of these concepts determine the productivity and effectiveness of modelling tasks within a BPR project. The\ud current paper contributes to engineering support for BPR. We work out general issues that play a role in the development of engineering support for BPR. Furthermore, we introduce an architectural framework for business process modelling and redesign. This framework consists of a coherent set of modelling concepts and techniques on how to use them. The framework enables the modelling of both the structural and dynamic characteristics of business processes. We illustrate its applicability by modelling a case from service industry. Moreover, the architectural framework supports abstraction and refinement techniques. The use of these techniques for a BPR trajectory are discussed

Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates

Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers). Connections between query causality and consistency-based diagnosis and database repairs (wrt. integrity constrain violations) have been established in the literature. In this work we establish connections between query causality and abductive diagnosis and the view-update problem. The unveiled relationships allow us to obtain new complexity results for query causality -the main focus of our work- and also for the two other areas.Comment: To appear in Proc. UAI Causal Inference Workshop, 2015. One example was fixe

From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back

In this work we establish and investigate connections between causes for query answers in databases, database repairs wrt. denial constraints, and consistency-based diagnosis. The first two are relatively new research areas in databases, and the third one is an established subject in knowledge representation. We show how to obtain database repairs from causes, and the other way around. Causality problems are formulated as diagnosis problems, and the diagnoses provide causes and their responsibilities. The vast body of research on database repairs can be applied to the newer problems of computing actual causes for query answers and their responsibilities. These connections, which are interesting per se, allow us, after a transition -inspired by consistency-based diagnosis- to computational problems on hitting sets and vertex covers in hypergraphs, to obtain several new algorithmic and complexity results for database causality.Comment: To appear in Theory of Computing Systems. By invitation to special issue with extended papers from ICDT 2015 (paper arXiv:1412.4311

Story Ending Generation with Incremental Encoding and Commonsense Knowledge

Generating a reasonable ending for a given story context, i.e., story ending generation, is a strong indication of story comprehension. This task requires not only to understand the context clues which play an important role in planning the plot but also to handle implicit knowledge to make a reasonable, coherent story. In this paper, we devise a novel model for story ending generation. The model adopts an incremental encoding scheme to represent context clues which are spanning in the story context. In addition, commonsense knowledge is applied through multi-source attention to facilitate story comprehension, and thus to help generate coherent and reasonable endings. Through building context clues and using implicit knowledge, the model is able to produce reasonable story endings. context clues implied in the post and make the inference based on it. Automatic and manual evaluation shows that our model can generate more reasonable story endings than state-of-the-art baselines.Comment: Accepted in AAAI201