Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Big data e data analysis: visualização de informação

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Integração da área de negócio de retalho do grupo Riopele no Data Warehouse corporativo

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Validity of the Brazilian Portuguese version of the bipolar spectrum diagnostic scale

OBJECTIVE: Bipolar spectrum disorders (BSDs) are prevalent and frequently unrecognized and undertreated. This report describes the development and validation of the Brazilian version of the bipolar spectrum diagnostic scale (B-BSDS), a screening instrument for bipolar disorders, in an adult psychiatric population. METHOD: 114 consecutive patients attending an outpatient psychiatric clinic completed the B-BSDS. A research psychiatrist, blind to the B-BSDS scores, interviewed patients by means of a modified version of the mood module of the Structured Clinical Interview for DSM-IV ("gold standard"). Subthreshold bipolar disorders were defined as recurrent hypomania without a major depressive episode or with fewer symptoms than those required for threshold hypomania. RESULTS: The internal consistency of the B-BSDS evaluated with Cronbach's alpha coefficient was 0.89 (95% CI; 0.86-0.91). On the basis of the modified SCID, 70 patients (61.4%) of the sample received a diagnosis of BSDs. A B-BSDS screening score of 16 or more items yielded: sensitivity of 0.79 (95% CI; 0.72-0.85), specificity of 0.77 (95% CI; 0.70-0.83), a positive predictive value of 0.85 (95% CI; 0.78-0.91) and a negative predictive value of 0.70 (95% CI; 0.63-0.75). CONCLUSION: The present data demonstrate that the B-BSDS is a valid instrument for the screening of BSDs.

Enquadramento para a compreensão dos impactos do Open Government Data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Forecasting human entrances at a commercial store using facial recognition data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Data analytics: abordagem para visualização da informação

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Pervasive data mining engine

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Joint modelling of longitudinal and survival data on breast cancer

Tese de Doutoramento em Ciências (Especialidade em Matemática)

A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the de- centralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val- ues result from the distributed computation of functions like count, sum and average. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many di erent approaches have been pro- posed, with di erent trade-o s in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of ag- gregation algorithms, it can be di cult and time consuming to determine which techniques will be more appropriate to use in speci c settings, jus- tifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally de nes the concept of aggrega- tion, characterizing the di erent types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

Clinical diagnosis of 80 cases of dementia in a university hospital

OBJECTIVE: This study aims to estimate the prevalence of dementia subtypes and to assess the socio-demographic data of patients attending the outpatient clinic of dementia at Hospital das Clínicas from January 2008 to December 2009, in the city of Goiânia-GO, Brazil. METHODS: Procedures provided for diagnosis included physical and neurological examination, laboratory tests, neuroimaging and DSM-IV. The functional capacity and level of cognitive deficit were assessed by Pfeffer Functional Activities Questionnaire (Pfeffer-FAQ) and Mini-Mental State Examination (MMSE), respectively. RESULTS: Eighty patients met the criteria for dementia. The mean age was 63.48 (± 16.85) years old, the schooling was 3.30 (± 3.59) years old, the MMSE was 13.89 (± 7.79) and Pfeffer 17.73 (± 9.76). The Vascular Dementia (VD; 17.5%) was the most frequent cause of dementia, followed by Lewy body dementia (LBD) and Alzheimer's disease (AD) (12.25%). CONCLUSION: Considering entire sample and only the elderly over 60 years, VD, AD and LBD are the most common subtypes observed at both groups. Further epidemiological studies are necessary to confirm such rates, which may have a considerable impact on the organization and planning of healthcare services in our country.

A qualidade de vida da pessoa com doença renal crónica em programa regular de hemodiálise

Dissertação de mestrado em Enfermagem

Concise server-wide causality management for eventually consistent data stores

Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of net work partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conflicts. Another aspect of eventual consistency is detecting write conflicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a nonnegligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, perkey metadata cannot be permanently removed without coordination. Weintroduceanewcausalitymanagementframeworkforeventuallyconsistentdatastores,thatleveragesnodelogicalclocks(BitmappedVersion Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new efficient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.

ADSNARK: Nearly practical and privacy-preserving proofs on authenticated data

We study the problem of privacy-preserving proofs on authenticated data, where a party receives data from a trusted source and is requested to prove computations over the data to third parties in a correct and private way, i.e., the third party learns no information on the data but is still assured that the claimed proof is valid. Our work particularly focuses on the challenging requirement that the third party should be able to verify the validity with respect to the specific data authenticated by the source — even without having access to that source. This problem is motivated by various scenarios emerging from several application areas such as wearable computing, smart metering, or general business-to-business interactions. Furthermore, these applications also demand any meaningful solution to satisfy additional properties related to usability and scalability. In this paper, we formalize the above three-party model, discuss concrete application scenarios, and then we design, build, and evaluate ADSNARK, a nearly practical system for proving arbitrary computations over authenticated data in a privacy-preserving manner. ADSNARK improves significantly over state-of-the-art solutions for this model. For instance, compared to corresponding solutions based on Pinocchio (Oakland’13), ADSNARK achieves up to 25× improvement in proof-computation time and a 20× reduction in prover storage space.

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil