Analysis and improvement of business process models using spreadsheets

Software in general is thoroughly analyzed before it is released to its users. Business processes often are not - at least not as thoroughly as it could be - before they are released to their users, e.g., employees or software agents. This paper ascribes this practice to the lack of suitable instruments for business process analysts, who design the processes, and aims to provide them with the necessary instruments to allow them to also analyze their processes. We use the spreadsheet paradigm to represent business process analysis tasks, such as writing metrics and assertions, running performance analysis and verification tasks, and reporting on the outcomes, and implement a spreadsheet-based tool for business process analysis. The results of two independent user studies demonstrate the viability of the approach

The use of UML activity diagrams and the i* language in the modeling of the balanced scorecard implantation process

Business management is a complex task that can be facilitated using different methodologies and models. One of their most relevant purposes is to align the organization strategy with the daily functioning of the organization. One of these models is the Balanced Scorecard (BSC). In this paper, we propose a modeling strategy for the BSC implantation process. We will model it using UML Activity Diagrams and Strategy Dependency models of the language i*. The Activity Diagrams allow determining the order in which involved activities must be performed, and at the same time, to identify which people has the responsability to carry them out. The Strategic Dependency model allows showing the intentional aspects of the actors involved in the most strategic activities of this process. Finally, relationships among the actors and the people involved in the BSC implantation process are modelled using again the language i*. Although this paper only considers the case study of the BSC implantation, our proposal can be generalized to other implantation processes of systems with a high strategic impact on the organization, like ERP or CRM systems.Peer ReviewedPostprint (published version

A machine learning approach for layout inference in spreadsheets

Spreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets, which otherwise could provide the means to automatically understand and extract knowledge from these files. In this paper, we propose a classification approach to discover the layout of tables in spreadsheets. Therefore, we focus on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach deliver s very high accuracy bringing us a crucial step closer towards automatic table extraction.Peer ReviewedPostprint (published version