Characterizing Geo-located Tweets in Brazilian Megacities

This work presents a framework for collecting, processing and mining geo-located tweets in order to extract meaningful and actionable knowledge in the context of smart cities. We collected and characterized more than 9M tweets from the two biggest cities in Brazil, Rio de Janeiro and S\~ao Paulo. We performed topic modeling using the Latent Dirichlet Allocation model to produce an unsupervised distribution of semantic topics over the stream of geo-located tweets as well as a distribution of words over those topics. We manually labeled and aggregated similar topics obtaining a total of 29 different topics across both cities. Results showed similarities in the majority of topics for both cities, reflecting similar interests and concerns among the population of Rio de Janeiro and S\~ao Paulo. Nevertheless, some specific topics are more predominant in one of the cities

On the Role of Interceptors and AOP in Adapting CORBA Applications ∗

Abstract. In this paper we describe two meta-programming strategies that have been used to extend CORBA-based applications with minimal or no impact on existing application code: CORBA interceptors and aspect-oriented programming (AOP). We compare the benefits of using AOP with those of exploiting interceptors to extend CORBA-based applications. We present the main issues in which using AOP in this context is different from taking advantage of the existing CORBA interceptor mechanism. In order to illustrate our discussion we use a dynamic aspect-oriented language, AspectLua, and a meta-object protocol, LuaMOP, that supports dynamic weaving of CORBA components and aspects.

A microservice based architecture topology for machine learning deployment

Smart solutions that make use of machine learning and data analyses are on the rise. Big Data analysis is attracting more and more developers and researchers, and at least five requirements (Velocity, Volume, Value, Variety, and Veracity) show challenges in deploying such solutions. Across the globe, many Smart City initiatives are using Big Data Analytics as a tool for doing predictive analytics which can be helpful to human well being. This work presents a generic architecture named Machine Learning in Microservices Architecture (MLMA) that provides design patterns to transform a monolithic architecture of machine learning pipelines in microservices with separate roles. We present two case studies deployed to a Smart City initiative, where we discuss how each component of the architecture applied in specific applications that use predictions with machine learning. Among the benefits of this architecture, we argue prediction performance, scalability, code maintenance and reusability makes such transition a natural trend in Big Data and machine learning applications

A geotecnologia Smart Geo Layers (SGeoL) e sua aplicação para as políticas públicas: o caso do Painel de Segurança Hídrica (PSH)

Following this dossier that aims to discuss research methodologies in Territorial Intelligence, this article seeks to detail the scientific methodology applied to Smart Geo Layers (SGeoL) to build the Water Security Panel (PSH). SGeoL is a platform for data management and integration, an innovative technology developed by IMD/UFRN and used in PSH. The SGeoL platform provides support for data integration, data visualization and data overlay. The platform also supports geographic processing, time series management, etc. Through SGeoL, information previously organized in a fragmented and isolated manner was grouped and integrated, to compose the PSH. Through PSH, managers and researchers can have access to a myriad of information relating to water security, which can be filtered, processed, correlated, and viewed in a georeferenced way. Allowing the production of new value-added information, more efficient management of resources, as well as more assertive decision-making based on real, integrated, and updated information

An exploratory study of fault-proneness in evolving aspect-oriented programs

This paper presents the results of an exploratory study on the fault- proneness of aspect-oriented programs. We analysed the faults collected from three evolving aspect-oriented systems, all from different application domains. The analysis develops from two different angles. Firstly, we measured the impact of the obliviousness property on the fault-proneness of the evaluated systems. The results show that 40% of reported faults were due to the lack of awareness among base code and aspects. The second analysis regarded the fault-proneness of the main aspect-oriented programming (AOP) mechanisms, namely pointcuts, advices and intertype declarations. The results indicate that these mechanisms present similar fault-proneness when we consider both the overall system and concern- specific implementations. Our findings are reinforced by means of statistical tests. In general, this result contradicts the common intuition stating that the use of pointcut languages is the main source of faults in AOP