Business Rule Mining from Spreadsheets

Business rules represent the knowledge that guides the operations of a business organization. They are implemented in software applications used by organizations, and the activity of extracting them from software is known as business rule mining. It has various purposes amongst which migration and generating documentation are the most common. However, apart from conventional software, organizations also use spreadsheets for a large part of their operations and decision-making activities. Therefore we believe that spreadsheets are also rich in business rules. We thus propose to develop an automated system for extracting business rules from spreadsheets in a human comprehensible natural language format. This position paper describes our motivation, the problem description, related work, and challenges we foresee.Comment: In Proceedings of the 2nd Workshop on Software Engineering Methods in Spreadsheets (http://spreadsheetlab.org/sems15/

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale

A Black-Box Computational Business Rules Extraction Approach through Test-Driven Development

Business rules extraction is an important activity in situations in which a software system becomes obsolete and needs to be replaced by a newer system, since the replacing system needs to satisfy the business rules embedded in the legacy software system. In this paper, we investigate an approach in which the computational business rules of a legacy software system can be extracted given previously generated output of the system and without requiring access to the system’s source code. Furthermore, extracted computational business rules are validated automatically with minimal involvement of domain experts through Test-Driven Development (TDD) such that test cases are constructed from historic output of the system. The proposed approach is applied to extract the computational business rules of a large-scale governmental payroll legacy software system. The study results demonstrate that the suggested approach extracted computational business rules van meet a substantial number of test cases. Thus, the efforts involving domain experts can be reduces to analyze such instances

An Architecture to infer Business Rules from Event Condition Action Rules implemented in the Persistence Layer

The business rules that govern the behaviour of a business process can be hardcoded in different ways in a software application. The modernization or improvement of these applications to a process-oriented perspective implies typically the modification of the business rules. Frequently, legacy systems are not well-documented, and almost always, the documentation they have is not updated. As a consequence many times is necessary the analysis of source code and databases structures to be transformed into a business language more understandable by the business experts involved in the modernization process. Database triggers are one of the artefacts in which business rules are hardcoded. We focus on this kind of artefacts, having in mind to avoid the manual analysis of the triggers by a database expert, and bringing it closer to business experts. To get this aim we need to discover business rules that are hardcoded in triggers, and translate it into vocabularies that are commonly used by business experts. In this paper we propose an ADM-based architecture to discover business rules and rewrite then into a language that can be understood by the business experts.Ministerio de Ciencia y Tecnología TIN2009-13714Ministerio de Ciencia y Tecnología TIN2010-20057-C03-02Ministerio de Ciencia y Tecnología TIN2010-21744-C02-