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

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

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

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

DW-CGU: Integração dos Dados do Portal da Transparência do Governo Federal Brasileiro

Os portais de transparência governamental são ferramentas de consolidação e desenvolvimento da democracia. Porém, a simples disponibilização de informações não garante uma visão unificada dos dados. O fato de os dados apresentados serem oriundos de Sistemas de Informação independentes traz problemas de integração. O objetivo desse trabalho é desenvolver uma solução de integração de dados para o Portal da Transparência do Governo Federal e sua principal contribuição é a proposta de uma arquitetura capaz de controlar todas as fases do processo de integração dos dados de um portal coorporativo. Os resultados obtidos evidenciaram o potencial de análise que esses dados integrados oferecem. A validação da arquitetura proposta avaliou os resultados do processo de integração de dados não apenas antes da implantação da solução, como também durante toda a fase de operação do sistema. O trabalho evidencia que a integração de dados envolve questões que vão além dos desafios tecnológicos, carecendo de interpretações semânticas dos dados

0006/2010 - Estudos do Profile SoaML

As técnicas tradicionais para modelagem de aplicações como, por exemplo, UML, não podem ser diretamente aplicadas à modelagem de serviços em uma abordagem SOA. Dessa forma, é necessário estendê-las a fim de que todas as informações inerentes a serviços, seus provedores e consumidores sejam especificadas. Isto pode ser feito utilizando extensões da UML. Estas extensões da linguagem são chamadas de profiles. Estes profiles UML adicionam estereótipos na linguagem para representar artefatos com semântica mais específica. Alguns profiles UML para SOA foram propostos para representar características específicas de SOA como: especificação técnica do serviço, relacionamento do provedor do serviço com seus consumidores, mensagens trocadas etc. Neste relatório são apresentados alguns conceitos básicos sobre SoaML. Este profile foi proposto pela OMG e pretende-se que este se torne um padrão para especificação de artefatos em SOA

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

Context-Aware Process Performance Indicator Prediction

It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phase guides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3

Link prediction using a probabilistic description logic

Due to the growing interest in social networks,\ud link prediction has received significant attention. Link prediction\ud is mostly based on graph-based features, with some\ud recent approaches focusing on domain semantics. We propose\ud algorithms for link prediction that use a probabilistic\ud ontology to enhance the analysis of the domain and the\ud unavoidable uncertainty in the task (the ontology is specified\ud in the probabilistic description logic crALC). The scalability\ud of the approach is investigated, through a combination of\ud semantic assumptions and graph-based features. We evaluate\ud empirically our proposal, and compare it with standard\ud solutions in the literature.The third author is partially supported by CNPq. The work reported here has received substantial support by FAPESP Grant 2008/03995-5 and FAPERJ Grant E-26/111484/2010. Thanks to Jesus Pascual Mena Chalco for providing us datasets and figures of the Lattes research areas

BPM2Text: A language independent framework for Business Process Models to Natural Language Text

The proper representation of a Business process is important for its execution and understanding. BPMN has been used as the standard notation for business process models, however domain specialists, which are experts in the business, do not have necessarily the modeling skills to easily read a business process model. It is easier for them to read in natural language. In this work, we propose a language-independent framework, instantiated using Java standard technology, for generating automatically natural language texts from business process models. A case study was conducted to evaluate the quality of the generated text. We found empirical support that the textual work instructions can be considered equivalent, in terms of knowledge representation, to process models represented in BPMN. Regarding the framework output quality (textual descriptions) 86% of the subjects claims that it vary from excellent to good