On the use of domain knowledge for process model repair

Process models are important for supporting organizations in documenting, understanding and monitoring their business. When these process models become outdated, they need to be revised to accurately describe the new status quo of the processes in the organization. Process model repair techniques help at automatically revising the existing model from behavior traced in event logs. So far, such techniques have focused on identifying which parts of the model to change and how to change them, but they do not use knowledge from practitioners to inform the revision. As a consequence, fragments of the model may change in a way that defies existing regulations or represents outdated information that was wrongly considered from the event log. This paper uses concepts from theory revision to provide formal foundations for process model repair that exploits domain knowledge. Specifically, it conceptualizes (1) what are unchangeable fragments in the model and (2) the role that various traces in the event log should play when it comes to model repair. A scenario of use is presented that demonstrates the benefits of this conceptualization. The current state of existing process model repair techniques is compared against the proposed concepts. The results show that only two existing techniques partially consider the concepts presented in this paper for model repair.Peer Reviewe

PREDICTIVE BUSINESS PROCESS MONITORINGWITH CONTEXT INFORMATION FROM DOCUMENTS

Predictive business process monitoring deals with predicting a process’s future behavior or the value of process-related performance indicators based on process event data. A variety of prototypical predictive business process monitoring techniques has been proposed by researchers in order to help process participants performing business processes better. In practical settings, these techniques have a low predictive quality that is often close to random, so that predictive business process monitoring applications are rare in practice. The inclusion of process-context data has been discussed as a way to improve the predictive quality. Existing approaches have considered only structured data as context. In this paper, we argue that process-related unstructured documents are also a promising source for extracting process-context data. Accordingly, this research-in-progress paper outlines a design-science research process for creating a predictive business process monitoring technique that utilizes context data from process-related documents to predict a process instance’s next activity more accurately

Effort Estimation of Business Process Modeling through Clustering Techniques

A critical activity in project planning, especially in business process modeling (BPM) projects, is effort estimation. It involves several dimensions such as business domain complexity, team and technology characteristics, turning estimation into a difficult and inaccurate task. In order to reduce this difficulty, background knowledge about past projects is typically applied; however, it is too costly to be carried out manually. On the other hand, Data Mining enables the automatic extraction of new nontrivial and useful knowledge from existing data. This paper presents a new approach for BPM project effort estimation using data mining through clustering technique. This approach was successfully applied to real dat

Process Mining Techniques in Internal Auditing: A Stepwise Case Study

A business process is a sequence of activities organized in a logical way in order to produce a service or a product that is valued for a particular group of customers. Process auditing in corporate environment aims to assess the degree of compliance of processes and their controls. Due to the volume of information that needs to be analyzed in an audit job, auditing´s cost can be very high. We argue that process mining techniques have the potential to improve this activity, allowing the auditor to meet the short deadlines, as well as bringing greater value to the senior management and reliability in the service provided by the audit. The goal of this paper is to discuss, through a case study, how process mining techniques can optimize and bring agility to the verification of process model compliance against the process actually performed. With this approach, it will be possible to detect errors and/or failures in activities or controls of a running process. The main contribution of this paper is to describe a simple set of steps that could be applied by auditors and experts in order to get introduced and to obtain the first insights in the process mining area

Management of Scientific Experiments in Computational Modeling: Challenges and Perspectives

Currently the computer is essential to the success in conducting scientific research. In this context, e-Science appears as science performed with computer support aiming efficiency. The challenge, “Computational Modeling of artificial, naturals and socio-cultural complex systems and man-nature interaction” from SBC Great Challenges is strongly related to the e-Science context. The goal of this challenge is to create, evaluate, modify, compose, manage and exploit computer models in fields related to complex, artificial, natural, socio-cultural and human-nature systems. Technologies like semantic web service composition, data provenance, peer to peer networks and scientific software product line can be used as basis for the specification and development of an e-Science infrastructure to handle challenges and solve problems. This paper discusses the main challenges involved in developing an eScience infrastructure, presenting research challenges for the next years

Valuing Prior Learning: Designing an ICT Artifact to Assess Professional Competences Through Text Mining

Purpose: This paper introduces an ICT artifact that uses text mining to support the innovative and standardized assessment of professional competences within the validation of prior learning. By assessing, we mean comparing identified and documented professional competences against a standard or reference point. We evaluate the designed artifact by matching a set of curriculum vitae (CV) scraped from LinkedIn against a comprehensive model of professional competence. Design/Methodology/Approach: A design science approach informed the development and evaluation of the ICT artifact presented in this paper. Findings: A proof of concept shows that the ICT artifact can support assessors within the validation of prior learning procedure. Rather the output of such an ICT artifact can be used to structure documentation in the validation process. Research limitations/implications: Evaluating the artifact shows that ICT support to assess documented learning outcomes is a promising endeavor but remains a challenge. Further research should work on standardized ways to document professional competences, ICT artifacts that capture the semantic content of documents, and refine ontologies of theoretical models of professional competences. Practical implications: Text mining methods to assess professional competences rely on large bodies of textual data - thus a thoroughly built and large portfolio is necessary as input for this ICT artifact. Originality/value: Following the recent call of European policy makers to develop standardized and ICT-based approaches for the assessment of professional competences, we designed and evaluated an ICT artifact that supports the automatized assessment of professional competences within the validation of prior learning

Application of Knowledge Discovery in Databases in Evapotranspiration Estimation: an Experiment in the State of Rio de Janeiro

With the growing volume of data in various areas such as Hydrology, there is a need for using information systems to aid in handling such data. This article is a report of an experiment that used knowledge discovery techniques to estimate an important component of the hydrological cycle: evapotranspiration. The experiment reported in this article was done with weather data and showed that some algorithms, such as M5P, present good results when compared to historical data of the estimated evapotranspiration

UMA METODOLOGIA PARA O APRENDIZADO DE UM MODELO CLASSIFICADOR PARA O ALINHAMENTO DE ONTOLOGIAS

Ontology alignment is a common and successful way to reduce the semantic heterogeneity among ontologies, relying on the application of similarity functions to decide whether a pair of entities from two input ontologies corresponds to each other. There are several similarity functions proposed in the literature capturing distinct and complementary perspectives, but the challenge is on how to combine their use. This paper presents a methodology to automatically learn a classifier that combines distinct string-based similarity functions for the ontology alignment task, through machine learning. The proposed approach was evaluated experimentally on sixteen scenarios defined on top of the Ontology Alignment Evaluation Initiative (OAEI).Ontology alignment is a common and successful way to reduce the semantic heterogeneity among ontologies, relying on the application of similarity functions to decide whether a pair of entities from two input ontologies corresponds to each other. There are several similarity functions proposed in the literature capturing distinct and complementary perspectives, but the challenge is on how to combine their use. This paper presents a methodology to automatically learn a classifier that combines distinct string-based similarity functions for the ontology alignment task, through machine learning. The proposed approach was evaluated experimentally on sixteen scenarios defined on top of the Ontology Alignment Evaluation Initiative (OAEI).O alinhamento de ontologias é uma estratégia comum e que tem sido aplicada com sucesso para reduzir a heterogeneidade semântica entre ontologias de um mesmo domínio. Durante o processo de alinhamento são consideradas diferentes funções de similaridade a fim de selecionar corretamente os pares de entidades correspondentes entre as duas ontologias sendo alinhadas. Existem diversas funções de similaridade, mas o desafio atual está em como combiná-las para gerar alinhamentos de melhor qualidade. Este trabalho apresenta uma metodologia para gerar um modelo classificador, que combina diferentes funções de similaridade baseadas em string no alinhamento de ontologias, por meio de aprendizado de máquina. A abordagem proposta foi avaliada experimentalmente em dezesseis cenários definidos sobre a Iniciativa de Avaliação de Alinhamento de Ontologias (OAEI)

Ontology Extraction from Stories: an exploratory study in storytelling

Business and IT systems are facing increasingly complex environments characterized by collaboration, change and variety of customers, suppliers and products. Group storytelling technique can contribute to the business knowledge management. The stories count brings benefits from capture to securing information, through communication and understanding of the concepts. American Companies (3M and Apple), Japanese (Sony and Toshiba) and European (ClubMed and Océ) already use this approach in practice. Ontology Engineering can contribute towards improving the quality of information and offer a solution to address knowledge management systematically. However, the specification and manually made of ontology management can be expensive, tedious, biased and prone to error. Automatic learning ontology is an approach that extracts ontology from the data, both structured and unstructured (text). This work presents, at the exploratory stage, a proposal able to specify, automatically, elements of an ontology, from the tacit knowledge of those involved in the field. An exploratory study was able to get the concepts of an ontology, automatically, from stories told by a group storytelling tool on the business process of one department of the University