Robotic Process Automation (RPA) refers to process automation applications of traditional Information Technologies based on robot software with the ability to capture and interpret the specific processes of organizations. Studies show that RPAs are able to reduce resources and optimize processes effectively in relation to customers. Some of these call center business processes deal with customers most likely to complain; therefore, a “Proactive Notification” robot was developed to classify these types of customers to be prioritized. This robot defines the creation of an RPA architecture for proactive notifications applied to an electric company in Brazil. The methodology used for the development of this project consisted of data management, predictive models, and peripheral components for sending SMS and making calls. It was tested against all customers in 40 cities (two states) and the model considers the historical basis of 3 years of occurrences to predict customers with a high probability of filing a complaint due to power failure. The results show that customers who were called for this type of problem did not call the call center again to complain, suggesting positive acceptance of the robot. In conclusion, the robot presented herein is capable of making proactive notifications with high precision to customers with the highest probability of complaints, predicting possible problems.

Recent studies show that decision making in Business Process Management (BPM) and incorporating sustainability in business is vital for service innovation within a company. Likewise, it is also possible to save time and money in an automated, intelligent and sustainable way. Robotic Process Automation (RPA) is one solution that can help businesses improve their BPM and sustainability practices through digital transformation. However, deciding which processes to automate with RPA technology can be complex. Consequently, this paper presents a model for selecting indicators to determine the profitability of shifting to RPA in selected business processes. The method used in this work is the Performance Analysis Method, which allows for predicting which processes could be replaced by RPA to save time and money in a service workflow. The Performance Analysis Method consists of collecting data on the speed and efficiency of a business process and then using that data to develop discrete event simulations to estimate the cost of automating parts of that process. A case study using this model is presented, using business process data from an international utility company as input to the discrete event simulation. The model used in this study predicts that this Electric Utility Company (EUC) will save a substantial amount of money if it implements RPA in its call center.

Topic modeling comprises a set of machine learning algorithms that allow topics to be extracted from a collection of documents. These algorithms have been widely used in many areas, such as the identification of dominant topics in scientific research. However, works addressing such problems often focus on identifying static topics, providing snapshots that are unable to show how those topics evolve over time. Aiming to close this gap, in this article, we describe an approach for dynamic article set analysis and classification. This is accomplished by querying open data of notable scientific databases via representational state transfers and subsequently enforcing data management practices with a dynamic topic modeling approach on the associated metadata available. As a result, we identify research trends for a given field at specific instants and the referred terminology trends evolution throughout the years. It was possible to detect the associated lexical variation over time in published content, ultimately determining the so-called “hot topics” in arbitrary instants and also how they are correlated to each other.

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.

We defend the thesis that the use of text analytics can boost the results of analyses based on Singular Value Decomposition (SVD). To demonstrate our supposition, first we model the Digital Bibliography & Library Project (DBLP) as a relational schema; over this schema we use text analytics applied to the terms extracted from the titles of the articles. Then, we apply SVD on the relationships defined between these terms, publication vehicles, and authors; accordingly, we were able to identify the more representative communities and the more active authors relating them to the most meaningful terms and topics found in their respective publications. The results were semantically dense and concise, also leading to performance gains.

A ação do homem tem provocado mudanças no meio ambiente, sendo a poluição urbana uma das consequências negativas aplicadas nesse ecossistema. Com o desenvolvimento tecnológico, novas perspectivas computacionais afloram para o monitoramento ambiental. Este artigo apresenta pontos chaves da fenomenologia ambiental que podem se beneficiar da evolução promovida pela computação aplicada nos estudos da poluição, assim como realiza um levantamento de pesquisas e tecnologias utilizadas nesse contexto.