Bionomics data for Anopheles (Anopheles) forattinii Wilkerson & Sallum, 1999

Immature and adult stages of Anopheles (Anopheles) forattinii were collected in the Parque Nacional do Jaú, Novo Airão, Amazonas, Brazil. Larvae and pupae were taken from fresh water among floating plant debris inside flooded "igapó" forest. This species may make use of plant debris for passive dispersal throughout its distribution range.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

From a NoSQL data source to a business intelligence solution: An experiment

We are living in the era of Big Data. A time which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), in which everyone and (eventually) everything is linked to the Internet, data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data. Indeed, Big Data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence. In this paper we describe an experiment in which data obtained from a NoSQL data source (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution.

A geographic opportunistic forwarding strategy for vehicular named data networking

Studies in Computational Intelligence, 616

Task clustering on ETL systems – A pattern-oriented approach

Usually, data warehousing populating processes are data-oriented workflows composed by dozens of granular tasks that are responsible for the integration of data coming from different data sources. Specific subset of these tasks can be grouped on a collection together with their relationships in order to form higher- level constructs. Increasing task granularity allows for the generalization of processes, simplifying their views and providing methods to carry out expertise to new applications. Well-proven practices can be used to describe general solutions that use basic skeletons configured and instantiated according to a set of specific integration requirements. Patterns can be applied to ETL processes aiming to simplify not only a possible conceptual representation but also to reduce the gap that often exists between two design perspectives. In this paper, we demonstrate the feasibility and effectiveness of an ETL pattern-based approach using task clustering, analyzing a real world ETL scenario through the definitions of two commonly used clusters of tasks: a data lookup cluster and a data conciliation and integration cluster.

Conceptual-physical bridging – From BPMN models to physical implementations on kettle

Developing and implementing data-oriented workflows for data migration processes are complex tasks involving several problems related to the integration of data coming from different schemas. Usually, they involve very specific requirements - every process is almost unique. Having a way to abstract their representation will help us to better understand and validate them with business users, which is a crucial step for requirements validation. In this demo we present an approach that provides a way to enrich incrementally conceptual models in order to support an automatic way for producing their correspondent physical implementation. In this demo we will show how B2K (Business to Kettle) system works transforming BPMN 2.0 conceptual models into Kettle data-integration executable processes, approaching the most relevant aspects related to model design and enrichment, model to system transformation, and system execution.

A domain-specific language for ETL patterns specification in Data Warehousing Systems

During the last few years many research efforts have been done to improve the design of ETL (Extract-Transform-Load) systems. ETL systems are considered very time-consuming, error-prone and complex involving several participants from different knowledge domains. ETL processes are one of the most important components of a data warehousing system that are strongly influenced by the complexity of business requirements, their changing and evolution. These aspects influence not only the structure of a data warehouse but also the structures of the data sources involved with. To minimize the negative impact of such variables, we propose the use of ETL patterns to build specific ETL packages. In this paper, we formalize this approach using BPMN (Business Process Modelling Language) for modelling more conceptual ETL workflows, mapping them to real execution primitives through the use of a domain-specific language that allows for the generation of specific instances that can be executed in an ETL commercial tool.

Using a domain-specific language to enrich ETL schemas

Today it is easy to find a lot of tools to define data migration schemas among different types of information systems. Data migration processes use to be implemented on a very diverse range of applications, ranging from conventional operational systems to data warehousing platforms. The implementation of a data migration process often involves a serious planning, considering the development of conceptual migration schemas at early stages. Such schemas help architects and engineers to plan and discuss the most adequate way to migrate data between two different systems. In this paper we present and discuss a way for enriching data migration conceptual schemas in BPMN using a domain-specific language, demonstrating how to convert such enriched schemas to a first correspondent physical representation (a skeleton) in a conventional ETL implementation tool like Kettle.

Automatic generation of ETL physical systems from BPMN conceptual models

ETL conceptual modeling is a very important activity in any data warehousing system project implementation. Owning a high-level system representation allowing for a clear identification of the main parts of a data warehousing system is clearly a great advantage, especially in early stages of design and development. However, the effort to model conceptually an ETL system rarely is properly rewarded. Translating ETL conceptual models directly into something that saves work and time on the concrete implementation of the system process it would be, in fact, a great help. In this paper we present and discuss a hybrid approach to this problem, combining the simplicity of interpretation and power of expression of BPMN on ETL systems conceptualization with the use of ETL patterns to produce automatically an ETL skeleton, a first prototype system, which has the ability to be executed in a commercial ETL tool like Kettle.

Categorização do consumo de energia em sistemas de povoamento de Data Warehouses

Os recursos computacionais exigidos durante o processamento de grandes volumes de dados durante um processo de povoamento de um data warehouse faz com que a necessidade da procura de novas implementações tenha também em atenção a eficiência energética dos diversos componentes processuais que integram um qualquer sistema de povoamento. A lacuna de técnicas ou metodologias para categorizar e avaliar o consumo de energia em sistemas de povoamento de data warehouses é claramente notória. O acesso a esse tipo de informação possibilitaria a construção de sistemas de povoamento de data warehouses com níveis de consumo de energia mais baixos e, portanto, mais eficientes. Partindo da adaptação de técnicas aplicadas a sistemas de gestão de base de dados para a obtenção dos consumos energéticos da execução de interrogações, desenhámos e implementámos uma nova técnica que nos permite obter os consumos de energia para um qualquer processo de povoamento de um data warehouse, através da avaliação do consumo de cada um dos componentes utilizados na sua implementação utilizando uma ferramenta convencional. Neste artigo apresentamos a forma como fazemos tal avaliação, utilizando na demonstração da viabilidade da nossa proposta um processo de povoamento bastante típico em data warehouses – substituição encadeada de chaves operacionais -, que foi implementado através da ferramenta Kettle.

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables

Lecture Notes in Computer Science, 9273

Predicting type of delivery by identification of obstetric risk factors through data mining

In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries.

Relação entre regulação das ordens profissionais e gestão jurisdicional dos profissionais de contabilidade: estudo comparado entre Portugal e o Brasil

Tese de Doutoramento em Ciência da Administração